Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira Frujeri, Hiteshi Sharma, Robert Osazuwa Ness, Nebojsa Jojic, Hamid Palangi, Jonathan Larson
NeurIPS 2023 | October 2023
Return to Microsoft Research Lab – New York City
Machine Learning & AI | NYC
Publications
Publications by Year
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Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Robust dynamic assortment optimization in the presence of outlier customers
Xi Chen, Akshay Krishnamurthy, Yining Wang
Operations Research | August 2023
A complete characterization of linear estimators for offline policy evaluation
Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade
Journal of Machine Learning Research | July 2023
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
Aleksandrs Slivkins (slivkins), Karthik Abinav Sankararaman, Dylan Foster
2023 Conference on Learning Theory | July 2023
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
Andrew Wagenmaker, Dylan Foster
2023 Conference on Learning Theory | July 2023
Learning hidden Markov models using conditional samples
Sham Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
2023 Conference on Learning Theory | July 2023
Minimax Rates for Conditional Density Estimation via Empirical Entropy
Blair Bilodeau, Dylan Foster, Daniel M. Roy
Annals of Statistics | July 2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
Dylan Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin
2023 Conference on Learning Theory | July 2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
Dylan Foster, Noah Golowich, Yanjun Han
2023 Conference on Learning Theory | July 2023
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Dylan Foster, Noah Golowich, Sham M. Kakade
ICML 2023 | June 2023
Imitating Human Behaviour with Diffusion Models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Stevenson, Sergio Valcarcel Macua, Shanzheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
ICLR 2023 | May 2023
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan Foster, Yu Bai, Nan Jiang, Sham Kakade
2023 International Conference on Learning Representations | May 2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
Zakaria Mhammedi, Dylan Foster, Alexander Rakhlin
ICML 2023 | April 2023
Hybrid RL: Using both offline and online data can make RL efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
2023 International Conference on Learning Representations | April 2023
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Stevenson, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
CHI | April 2023
Provable Safe Reinforcement Learning with Binary Feedback
Andrew Bennett, Dipendra Misra, Nathan Kallus
2023 International Conference on Artificial Intelligence and Statistics | April 2023
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Olalekan Ogunmolu, Rajan Chari, Akshay Krishnamurthy, John Langford
Transactions on Machine Learning Research | February 2023
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Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
NeurIPS 2022 | November 2022
Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
On the Complexity of Adversarial Decision Making
Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | November 2022
Oral presentation
Contextual search in the presence of adversarial corruptions
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
Operations Research | November 2022
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan Foster, Nathan Srebro
2022 Neural Information Processing Systems | October 2022
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Surbhi Goel, Sham Kakade, Adam Tauman Kalai, Cyril Zhang
2022 Neural Information Processing Systems | September 2022
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh Nguyen, Yonatan Bisk, Hal Daumé III
ICML 2022 | July 2022
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Harm van Seijen, Ida Momennejad, Sarath Chandar, Janarthanan Rajendran, Ali Rahimi-Kalahroudi
ICML 2022 | July 2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
ICML 2022 | July 2022
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar, Akshay Krishnamurthy
ICML 2022 | July 2022
Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints
Amr Sharaf, Hal Daumé III, Renkun Ni
FAccT 2022 | June 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu
NAACL 2022 | May 2022
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan Foster, Nathan Srebro, Blake E. Woodworth
Mathematical Programming | May 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
How Humans Perceive Human-like Behavior in Video Game Navigation
Evelyn Zuniga, Stephanie Milani, Guy Leroy, Jaroslaw Rzepecki, Raluca Stevenson, Ida Momennejad, Dave Bignell, Mingfei Sun, Alison Shaw, Gavin Costello, Mikhail Jacob, Sam Devlin, Katja Hofmann
CHI 2022 | April 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Efficient and optimal algorithms for contextual dueling bandits under realizability
Akshay Krishnamurthy, Aadirupa Saha
International Conference on Algorithmic Learning Theory | April 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
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Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, Vol 64(12): pp. 86-92
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, Hal Daumé III
2021 Empirical Methods in Natural Language Processing | October 2021
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Sam Devlin, Raluca Stevenson, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann
2021 International Conference on Machine Learning | July 2021
Source code available at: https://github.com/microsoft/NTT
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal, Surbhi Goel, Cyril Zhang
2021 International Conference on Machine Learning | July 2021
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
2021 International Conference on Machine Learning | July 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
2021 Conference on Learning Theory | July 2021
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Dipendra Misra, Qinghua Li, Chi Jin, John Langford
2021 International Conference on Learning Representations | May 2021
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan L. Boyd-Graber, Hal Daumé III
April 2021
Optimism in reinforcement learning with generalized linear function approximation
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy
2021 International Conference on Learning Representations | April 2021
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Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
NeurIPS 2020 | December 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
On Warm-Starting Neural Network Training
Jordan Ash, Ryan P. Adams
NeurIPS 2020 | December 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
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Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Stretching Human Laws to Apply to Machines: The Dangers of a ‘Colorblind’ Computer
Zach Harned, Hanna Wallach
Florida State University Law Review, Forthcoming | December 2019
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Efficient active learning of sparse halfspaces
Chicheng Zhang
Proceedings of Machine Learning Research | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 2018
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum
International Conference on Learning Representations | April 2018
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
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Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Corralling a Band of Bandit Algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
Proceedings of the 2017 Conference on Learning Theory (COLT) | July 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
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Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Efficient Second Order Online Learning by Sketching
Haipeng Luo, Alekh Agarwal, Nicolo Cesa-Bianchi, John Langford
Advances in Neural Information Processing Systems 29 (NIPS 2016) | December 2016
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
IEEE Transcations on Information Theory | September 2016
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
SIAM Journal of Optimization | September 2016, Vol 26: pp. 2775-2799
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
A Multiworld Testing Decision Service
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
ArXiv preprint | June 2016
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
June 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
The offset tree for learning with partial labels
Alina Beygelzimer, John Langford
April 2016
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
ArXiv preprint | March 2016
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Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Best Paper Award
Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal, Leon Bottou
The 32nd International Conference on Machine Learning (ICML 2015) | July 2015
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
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Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, John Langford, Daniel Hsu, Matus Telgarsky
Advances in Neural Information Processing Systems 27 (NIPS 2014) | October 2014
Learning Sparsely Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014 | July 2014
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
July 2014
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
The 31st International Conference on Machine Learning (ICML 2014) | June 2014
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
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Efficient Online Bootstrapping for Large Scale Learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford (jcl)
MSR-TR-2013-132 | December 2013
Published by Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, Vol 12(2): pp. 4-23
Position paper and survey.
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
Para-active learning
Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford
October 2013
Crowdsourcing Gold-HIT Creation at Scale: Challenges and Adaptive Exploration Approaches (Position Paper)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins, Hai Wu
MSR-TR-2013-106 | October 2013
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Selective sampling algorithms for cost-sensitive multiclass prediction
Alekh Agarwal
Proceedings of the 30th International Conference of Machine Learning (ICML 2013) | June 2013
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, Vol 1
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
Adaptive crowdsourcing algorithms for the bandit survey problem
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins
26th Conf. on Learning Theory (COLT) | January 2013
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Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
Miroslav Dudik, Dumitru Erhan, John Langford, Lihong Li
Twenty-Eighth Conference on Uncertainty in Artificial Intelligence | October 2012
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’12) | June 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Learning Performance of Prediction Markets with Kelly Bettors
Alina Beygelzimer, John Langford, David Pennock
June 2012
Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
Twenty-Second Conference on Uncertainty in Artificial Intelligence | June 2012
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
Alekh Agarwal, Peter Bartlett, Pradeep Ravikumar, Martin Wainwright
IEEE Transcations on Information Theory | May 2012, Vol 58
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford, Xuanhui Wang
WSDM 2011 conference | March 2012
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Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
June 2011
Monotone multi-armed bandit allocations
Aleksandrs Slivkins, Aleksandrs Slivkins
Proceedings of the 24th Annual Conference on Learning Theory | April 2011
Parallel Online Learning
Daniel Hsu, Nikos Karampatziakis, John Langford, Alex J. Smola
March 2011
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Proc. of the 27th International Conference on Machine Learning (ICML 2010) | June 2010
Proc. of the 27th International Conference on Machine Learning (ICML 2010)
Full version can be found at arxiv.org (http://arxiv.org/abs/1005.5197).
Agnostic Active Learning Without Constraints
Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang
June 2010
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Sham Kakade
June 2010
Error-Correcting Tournaments
Alina Beygelzimer, John Langford, Pradeep Ravikumar
February 2010
A New Understanding of Prediction Markets Via No-Regret Learning
Yiling Chen, Jennifer Wortman Vaughan
Eleventh ACM Conference on Electronic Commerce (EC) | January 2010
Sharp Dichotomies for Regret Minimization in Metric Spaces
Robert Kleinberg, Aleksandrs Slivkins
21st ACM-SIAM Symp. on Discrete Algorithms (SODA) | January 2010
Full version available on arxiv.org (http://arxiv.org/abs/0911.1174).
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Slow Learners are Fast
John Langford, Alexander J. Smola, Martin Zinkevich
NIPS 2009 | November 2009
Extended version of conference paper – NIPS 2009
Characterizing Truthful Multi-Armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’09) | July 2009
Full version can be found on arxiv.org (http://arxiv.org/abs/0812.2291).
Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
Multi-Label Prediction via Compressed Sensing
Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang
June 2009
Learning Nonlinear Dynamic Models
John Langford, Ruslan Salakhutdinov, Tong Zhang
June 2009
Importance Weighted Active Learning
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
May 2009
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Characterizing Truthful Multi-armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
SIAM Journal on Computing (SICOMP) | November 2008, Vol 43: pp. 194-230
Adapting to a Changing Environment: the Brownian Restless Bandits
Aleksandrs Slivkins, Eli Upfal
21st Conference on Learning Theory (COLT) | July 2008
Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
July 2008
Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
ACM Symposium on Theory of Computing (STOC) | May 2008
ACM Symposium on Theory of Computing (STOC)
ISBN: 978-1-60558-047-0
The full version is available from arxiv.org (http://arxiv.org/abs/0809.4882).
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Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grunwald, John Langford
Computational Learning Theory 2004 | June 2004
This is a slightly longer version of the paper at COLT 2004, containing tow extra pages of discussion of the main results.
-
Generic Quantum Block Compression
September 2001
-
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
Publications by Research Area
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Generic Quantum Block Compression
September 2001
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira Frujeri, Hiteshi Sharma, Robert Osazuwa Ness, Nebojsa Jojic, Hamid Palangi, Jonathan Larson
NeurIPS 2023 | October 2023
Robust dynamic assortment optimization in the presence of outlier customers
Xi Chen, Akshay Krishnamurthy, Yining Wang
Operations Research | August 2023
Learning hidden Markov models using conditional samples
Sham Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
2023 Conference on Learning Theory | July 2023
A complete characterization of linear estimators for offline policy evaluation
Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade
Journal of Machine Learning Research | July 2023
Minimax Rates for Conditional Density Estimation via Empirical Entropy
Blair Bilodeau, Dylan Foster, Daniel M. Roy
Annals of Statistics | July 2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
Dylan Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin
2023 Conference on Learning Theory | July 2023
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
Aleksandrs Slivkins (slivkins), Karthik Abinav Sankararaman, Dylan Foster
2023 Conference on Learning Theory | July 2023
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
Andrew Wagenmaker, Dylan Foster
2023 Conference on Learning Theory | July 2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
Dylan Foster, Noah Golowich, Yanjun Han
2023 Conference on Learning Theory | July 2023
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Dylan Foster, Noah Golowich, Sham M. Kakade
ICML 2023 | June 2023
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan Foster, Yu Bai, Nan Jiang, Sham Kakade
2023 International Conference on Learning Representations | May 2023
Imitating Human Behaviour with Diffusion Models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Stevenson, Sergio Valcarcel Macua, Shanzheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
ICLR 2023 | May 2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
Zakaria Mhammedi, Dylan Foster, Alexander Rakhlin
ICML 2023 | April 2023
Hybrid RL: Using both offline and online data can make RL efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
2023 International Conference on Learning Representations | April 2023
Provable Safe Reinforcement Learning with Binary Feedback
Andrew Bennett, Dipendra Misra, Nathan Kallus
2023 International Conference on Artificial Intelligence and Statistics | April 2023
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Stevenson, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
CHI | April 2023
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Olalekan Ogunmolu, Rajan Chari, Akshay Krishnamurthy, John Langford
Transactions on Machine Learning Research | February 2023
On the Complexity of Adversarial Decision Making
Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | November 2022
Oral presentation
Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
NeurIPS 2022 | November 2022
Contextual search in the presence of adversarial corruptions
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
Operations Research | November 2022
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan Foster, Nathan Srebro
2022 Neural Information Processing Systems | October 2022
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Surbhi Goel, Sham Kakade, Adam Tauman Kalai, Cyril Zhang
2022 Neural Information Processing Systems | September 2022
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar, Akshay Krishnamurthy
ICML 2022 | July 2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
ICML 2022 | July 2022
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Harm van Seijen, Ida Momennejad, Sarath Chandar, Janarthanan Rajendran, Ali Rahimi-Kalahroudi
ICML 2022 | July 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh Nguyen, Yonatan Bisk, Hal Daumé III
ICML 2022 | July 2022
Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints
Amr Sharaf, Hal Daumé III, Renkun Ni
FAccT 2022 | June 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan Foster, Nathan Srebro, Blake E. Woodworth
Mathematical Programming | May 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
Efficient and optimal algorithms for contextual dueling bandits under realizability
Akshay Krishnamurthy, Aadirupa Saha
International Conference on Algorithmic Learning Theory | April 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, Vol 64(12): pp. 86-92
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Sam Devlin, Raluca Stevenson, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann
2021 International Conference on Machine Learning | July 2021
Source code available at: https://github.com/microsoft/NTT
Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
2021 International Conference on Machine Learning | July 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal, Surbhi Goel, Cyril Zhang
2021 International Conference on Machine Learning | July 2021
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
2021 Conference on Learning Theory | July 2021
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Dipendra Misra, Qinghua Li, Chi Jin, John Langford
2021 International Conference on Learning Representations | May 2021
Optimism in reinforcement learning with generalized linear function approximation
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy
2021 International Conference on Learning Representations | April 2021
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
NeurIPS 2020 | December 2020
On Warm-Starting Neural Network Training
Jordan Ash, Ryan P. Adams
NeurIPS 2020 | December 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Stretching Human Laws to Apply to Machines: The Dangers of a ‘Colorblind’ Computer
Zach Harned, Hanna Wallach
Florida State University Law Review, Forthcoming | December 2019
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Efficient active learning of sparse halfspaces
Chicheng Zhang
Proceedings of Machine Learning Research | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 2018
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum
International Conference on Learning Representations | April 2018
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
Corralling a Band of Bandit Algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
Proceedings of the 2017 Conference on Learning Theory (COLT) | July 2017
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Efficient Second Order Online Learning by Sketching
Haipeng Luo, Alekh Agarwal, Nicolo Cesa-Bianchi, John Langford
Advances in Neural Information Processing Systems 29 (NIPS 2016) | December 2016
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
SIAM Journal of Optimization | September 2016, Vol 26: pp. 2775-2799
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
IEEE Transcations on Information Theory | September 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
A Multiworld Testing Decision Service
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
ArXiv preprint | June 2016
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
June 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
The offset tree for learning with partial labels
Alina Beygelzimer, John Langford
April 2016
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
ArXiv preprint | March 2016
Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Best Paper Award
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal, Leon Bottou
The 32nd International Conference on Machine Learning (ICML 2015) | July 2015
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, John Langford, Daniel Hsu, Matus Telgarsky
Advances in Neural Information Processing Systems 27 (NIPS 2014) | October 2014
Learning Sparsely Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014 | July 2014
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
The 31st International Conference on Machine Learning (ICML 2014) | June 2014
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Efficient Online Bootstrapping for Large Scale Learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford (jcl)
MSR-TR-2013-132 | December 2013
Published by Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, Vol 12(2): pp. 4-23
Position paper and survey.
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
Para-active learning
Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford
October 2013
Crowdsourcing Gold-HIT Creation at Scale: Challenges and Adaptive Exploration Approaches (Position Paper)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins, Hai Wu
MSR-TR-2013-106 | October 2013
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
Selective sampling algorithms for cost-sensitive multiclass prediction
Alekh Agarwal
Proceedings of the 30th International Conference of Machine Learning (ICML 2013) | June 2013
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, Vol 1
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
Miroslav Dudik, Dumitru Erhan, John Langford, Lihong Li
Twenty-Eighth Conference on Uncertainty in Artificial Intelligence | October 2012
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Learning Performance of Prediction Markets with Kelly Bettors
Alina Beygelzimer, John Langford, David Pennock
June 2012
Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
Twenty-Second Conference on Uncertainty in Artificial Intelligence | June 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’12) | June 2012
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
Alekh Agarwal, Peter Bartlett, Pradeep Ravikumar, Martin Wainwright
IEEE Transcations on Information Theory | May 2012, Vol 58
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford, Xuanhui Wang
WSDM 2011 conference | March 2012
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
June 2011
Monotone multi-armed bandit allocations
Aleksandrs Slivkins, Aleksandrs Slivkins
Proceedings of the 24th Annual Conference on Learning Theory | April 2011
Parallel Online Learning
Daniel Hsu, Nikos Karampatziakis, John Langford, Alex J. Smola
March 2011
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Proc. of the 27th International Conference on Machine Learning (ICML 2010) | June 2010
Proc. of the 27th International Conference on Machine Learning (ICML 2010)
Full version can be found at arxiv.org (http://arxiv.org/abs/1005.5197).
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Sham Kakade
June 2010
Agnostic Active Learning Without Constraints
Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang
June 2010
Error-Correcting Tournaments
Alina Beygelzimer, John Langford, Pradeep Ravikumar
February 2010
A New Understanding of Prediction Markets Via No-Regret Learning
Yiling Chen, Jennifer Wortman Vaughan
Eleventh ACM Conference on Electronic Commerce (EC) | January 2010
Slow Learners are Fast
John Langford, Alexander J. Smola, Martin Zinkevich
NIPS 2009 | November 2009
Extended version of conference paper – NIPS 2009
Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
Multi-Label Prediction via Compressed Sensing
Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang
June 2009
Learning Nonlinear Dynamic Models
John Langford, Ruslan Salakhutdinov, Tong Zhang
June 2009
Importance Weighted Active Learning
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
May 2009
Characterizing Truthful Multi-armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
SIAM Journal on Computing (SICOMP) | November 2008, Vol 43: pp. 194-230
Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
July 2008
Adapting to a Changing Environment: the Brownian Restless Bandits
Aleksandrs Slivkins, Eli Upfal
21st Conference on Learning Theory (COLT) | July 2008
Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
ACM Symposium on Theory of Computing (STOC) | May 2008
ACM Symposium on Theory of Computing (STOC)
ISBN: 978-1-60558-047-0
The full version is available from arxiv.org (http://arxiv.org/abs/0809.4882).
Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grunwald, John Langford
Computational Learning Theory 2004 | June 2004
This is a slightly longer version of the paper at COLT 2004, containing tow extra pages of discussion of the main results.
-
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira Frujeri, Hiteshi Sharma, Robert Osazuwa Ness, Nebojsa Jojic, Hamid Palangi, Jonathan Larson
NeurIPS 2023 | October 2023
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu
NAACL 2022 | May 2022
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, Hal Daumé III
2021 Empirical Methods in Natural Language Processing | October 2021
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan Foster, Nathan Srebro, Blake E. Woodworth
Mathematical Programming | May 2022
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
2021 Conference on Learning Theory | July 2021
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
Learning Sparsely Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014 | July 2014
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, Vol 12(2): pp. 4-23
Position paper and survey.
Crowdsourcing Gold-HIT Creation at Scale: Challenges and Adaptive Exploration Approaches (Position Paper)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins, Hai Wu
MSR-TR-2013-106 | October 2013
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, Vol 1
Adaptive crowdsourcing algorithms for the bandit survey problem
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins
26th Conf. on Learning Theory (COLT) | January 2013
Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’12) | June 2012
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Proc. of the 27th International Conference on Machine Learning (ICML 2010) | June 2010
Proc. of the 27th International Conference on Machine Learning (ICML 2010)
Full version can be found at arxiv.org (http://arxiv.org/abs/1005.5197).
Sharp Dichotomies for Regret Minimization in Metric Spaces
Robert Kleinberg, Aleksandrs Slivkins
21st ACM-SIAM Symp. on Discrete Algorithms (SODA) | January 2010
Full version available on arxiv.org (http://arxiv.org/abs/0911.1174).
A New Understanding of Prediction Markets Via No-Regret Learning
Yiling Chen, Jennifer Wortman Vaughan
Eleventh ACM Conference on Electronic Commerce (EC) | January 2010
Characterizing Truthful Multi-Armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’09) | July 2009
Full version can be found on arxiv.org (http://arxiv.org/abs/0812.2291).
Characterizing Truthful Multi-armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
SIAM Journal on Computing (SICOMP) | November 2008, Vol 43: pp. 194-230
Adapting to a Changing Environment: the Brownian Restless Bandits
Aleksandrs Slivkins, Eli Upfal
21st Conference on Learning Theory (COLT) | July 2008
Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
ACM Symposium on Theory of Computing (STOC) | May 2008
ACM Symposium on Theory of Computing (STOC)
ISBN: 978-1-60558-047-0
The full version is available from arxiv.org (http://arxiv.org/abs/0809.4882).
-
How Humans Perceive Human-like Behavior in Video Game Navigation
Evelyn Zuniga, Stephanie Milani, Guy Leroy, Jaroslaw Rzepecki, Raluca Stevenson, Ida Momennejad, Dave Bignell, Mingfei Sun, Alison Shaw, Gavin Costello, Mikhail Jacob, Sam Devlin, Katja Hofmann
CHI 2022 | April 2022
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
-
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
-
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal, Surbhi Goel, Cyril Zhang
2021 International Conference on Machine Learning | July 2021
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grunwald, John Langford
Computational Learning Theory 2004 | June 2004
This is a slightly longer version of the paper at COLT 2004, containing tow extra pages of discussion of the main results.
-
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan L. Boyd-Graber, Hal Daumé III
April 2021
-
Generic Quantum Block Compression
September 2001
-
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, Vol 1
Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’12) | June 2012
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
A New Understanding of Prediction Markets Via No-Regret Learning
Yiling Chen, Jennifer Wortman Vaughan
Eleventh ACM Conference on Electronic Commerce (EC) | January 2010
Characterizing Truthful Multi-Armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’09) | July 2009
Full version can be found on arxiv.org (http://arxiv.org/abs/0812.2291).
-
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Proc. of the 27th International Conference on Machine Learning (ICML 2010) | June 2010
Proc. of the 27th International Conference on Machine Learning (ICML 2010)
Full version can be found at arxiv.org (http://arxiv.org/abs/1005.5197).
-
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Publications by Type
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
Ida Momennejad, Hosein Hasanbeig, Felipe Vieira Frujeri, Hiteshi Sharma, Robert Osazuwa Ness, Nebojsa Jojic, Hamid Palangi, Jonathan Larson
NeurIPS 2023 | October 2023
Learning hidden Markov models using conditional samples
Sham Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
2023 Conference on Learning Theory | July 2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
Dylan Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin
2023 Conference on Learning Theory | July 2023
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
Aleksandrs Slivkins (slivkins), Karthik Abinav Sankararaman, Dylan Foster
2023 Conference on Learning Theory | July 2023
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
Andrew Wagenmaker, Dylan Foster
2023 Conference on Learning Theory | July 2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
Dylan Foster, Noah Golowich, Yanjun Han
2023 Conference on Learning Theory | July 2023
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Dylan Foster, Noah Golowich, Sham M. Kakade
ICML 2023 | June 2023
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan Foster, Yu Bai, Nan Jiang, Sham Kakade
2023 International Conference on Learning Representations | May 2023
Imitating Human Behaviour with Diffusion Models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Stevenson, Sergio Valcarcel Macua, Shanzheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
ICLR 2023 | May 2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
Zakaria Mhammedi, Dylan Foster, Alexander Rakhlin
ICML 2023 | April 2023
Hybrid RL: Using both offline and online data can make RL efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
2023 International Conference on Learning Representations | April 2023
Provable Safe Reinforcement Learning with Binary Feedback
Andrew Bennett, Dipendra Misra, Nathan Kallus
2023 International Conference on Artificial Intelligence and Statistics | April 2023
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Stevenson, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
CHI | April 2023
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
On the Complexity of Adversarial Decision Making
Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
NeurIPS 2022 | November 2022
Oral presentation
Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
NeurIPS 2022 | November 2022
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan Foster, Nathan Srebro
2022 Neural Information Processing Systems | October 2022
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Surbhi Goel, Sham Kakade, Adam Tauman Kalai, Cyril Zhang
2022 Neural Information Processing Systems | September 2022
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar, Akshay Krishnamurthy
ICML 2022 | July 2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
ICML 2022 | July 2022
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Harm van Seijen, Ida Momennejad, Sarath Chandar, Janarthanan Rajendran, Ali Rahimi-Kalahroudi
ICML 2022 | July 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh Nguyen, Yonatan Bisk, Hal Daumé III
ICML 2022 | July 2022
Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints
Amr Sharaf, Hal Daumé III, Renkun Ni
FAccT 2022 | June 2022
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire
2022 Neural Information Processing Systems | May 2022
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu
NAACL 2022 | May 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
How Humans Perceive Human-like Behavior in Video Game Navigation
Evelyn Zuniga, Stephanie Milani, Guy Leroy, Jaroslaw Rzepecki, Raluca Stevenson, Ida Momennejad, Dave Bignell, Mingfei Sun, Alison Shaw, Gavin Costello, Mikhail Jacob, Sam Devlin, Katja Hofmann
CHI 2022 | April 2022
Efficient and optimal algorithms for contextual dueling bandits under realizability
Akshay Krishnamurthy, Aadirupa Saha
International Conference on Algorithmic Learning Theory | April 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, Hal Daumé III
2021 Empirical Methods in Natural Language Processing | October 2021
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Sam Devlin, Raluca Stevenson, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann
2021 International Conference on Machine Learning | July 2021
Source code available at: https://github.com/microsoft/NTT
Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
2021 International Conference on Machine Learning | July 2021
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
2021 International Conference on Machine Learning | July 2021
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal, Surbhi Goel, Cyril Zhang
2021 International Conference on Machine Learning | July 2021
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
2021 Conference on Learning Theory | July 2021
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States
Dipendra Misra, Qinghua Li, Chi Jin, John Langford
2021 International Conference on Learning Representations | May 2021
Optimism in reinforcement learning with generalized linear function approximation
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy
2021 International Conference on Learning Representations | April 2021
Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
NeurIPS 2020 | December 2020
On Warm-Starting Neural Network Training
Jordan Ash, Ryan P. Adams
NeurIPS 2020 | December 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miro Dudík, Haipeng Luo, Robert E. Schapire
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miro Dudík, Yisong Yue, Hal Daumé III
International Conference on Machine Learning (ICML) | July 2018
International Conference on Machine Learning
Efficient active learning of sparse halfspaces
Chicheng Zhang
Proceedings of Machine Learning Research | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 2018
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum
International Conference on Learning Representations | April 2018
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miro Dudík
The 34th International Conference on Machine Learning (ICML 2017) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
Corralling a Band of Bandit Algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
Proceedings of the 2017 Conference on Learning Theory (COLT) | July 2017
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Efficient Second Order Online Learning by Sketching
Haipeng Luo, Alekh Agarwal, Nicolo Cesa-Bianchi, John Langford
Advances in Neural Information Processing Systems 29 (NIPS 2016) | December 2016
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Advances in Neural Information Processing Systems 28 (NIPS 2015) | December 2015
Best Paper Award
Contextual Dueling Bandits
Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
Proceedings of The 28th Conference on Learning Theory (COLT)
A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal, Leon Bottou
The 32nd International Conference on Machine Learning (ICML 2015) | July 2015
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Scalable Nonlinear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, John Langford, Daniel Hsu, Matus Telgarsky
Advances in Neural Information Processing Systems 27 (NIPS 2014) | October 2014
Learning Sparsely Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014 | July 2014
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal, Sham Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
July 2014
Robust Multi-objective Learning with Mentor Feedback
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
27th Conf. on Learning Theory (COLT) | July 2014
27th Conf. on Learning Theory (COLT)
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
The 31st International Conference on Machine Learning (ICML 2014) | June 2014
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
A Combinatorial Prediction Market for the U.S. Elections
Sébastien Lahaie, Miro Dudík, David Rothschild, David Pennock
ACM Conference on Electronic Commerce | June 2013
Selective sampling algorithms for cost-sensitive multiclass prediction
Alekh Agarwal
Proceedings of the 30th International Conference of Machine Learning (ICML 2013) | June 2013
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
Adaptive crowdsourcing algorithms for the bandit survey problem
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins
26th Conf. on Learning Theory (COLT) | January 2013
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
Miroslav Dudik, Dumitru Erhan, John Langford, Lihong Li
Twenty-Eighth Conference on Uncertainty in Artificial Intelligence | October 2012
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
Twenty-Second Conference on Uncertainty in Artificial Intelligence | June 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’12) | June 2012
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford, Xuanhui Wang
WSDM 2011 conference | March 2012
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
Efficient optimal learning for contextual bandits
Nikos Karampatziakis, John Langford, Miroslav Dudik, Lev Reyzin, Tong Zhang, Miro Dudík
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) | July 2011
Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
Monotone multi-armed bandit allocations
Aleksandrs Slivkins, Aleksandrs Slivkins
Proceedings of the 24th Annual Conference on Learning Theory | April 2011
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Proc. of the 27th International Conference on Machine Learning (ICML 2010) | June 2010
Proc. of the 27th International Conference on Machine Learning (ICML 2010)
Full version can be found at arxiv.org (http://arxiv.org/abs/1005.5197).
Sharp Dichotomies for Regret Minimization in Metric Spaces
Robert Kleinberg, Aleksandrs Slivkins
21st ACM-SIAM Symp. on Discrete Algorithms (SODA) | January 2010
Full version available on arxiv.org (http://arxiv.org/abs/0911.1174).
A New Understanding of Prediction Markets Via No-Regret Learning
Yiling Chen, Jennifer Wortman Vaughan
Eleventh ACM Conference on Electronic Commerce (EC) | January 2010
Slow Learners are Fast
John Langford, Alexander J. Smola, Martin Zinkevich
NIPS 2009 | November 2009
Extended version of conference paper – NIPS 2009
Characterizing Truthful Multi-Armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
ACM Conference on Electronic Commerce (EC’09) | July 2009
Full version can be found on arxiv.org (http://arxiv.org/abs/0812.2291).
Adapting to a Changing Environment: the Brownian Restless Bandits
Aleksandrs Slivkins, Eli Upfal
21st Conference on Learning Theory (COLT) | July 2008
Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
ACM Symposium on Theory of Computing (STOC) | May 2008
ACM Symposium on Theory of Computing (STOC)
ISBN: 978-1-60558-047-0
The full version is available from arxiv.org (http://arxiv.org/abs/0809.4882).
Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grunwald, John Langford
Computational Learning Theory 2004 | June 2004
This is a slightly longer version of the paper at COLT 2004, containing tow extra pages of discussion of the main results.
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Robust dynamic assortment optimization in the presence of outlier customers
Xi Chen, Akshay Krishnamurthy, Yining Wang
Operations Research | August 2023
A complete characterization of linear estimators for offline policy evaluation
Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade
Journal of Machine Learning Research | July 2023
Minimax Rates for Conditional Density Estimation via Empirical Entropy
Blair Bilodeau, Dylan Foster, Daniel M. Roy
Annals of Statistics | July 2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Olalekan Ogunmolu, Rajan Chari, Akshay Krishnamurthy, John Langford
Transactions on Machine Learning Research | February 2023
Contextual search in the presence of adversarial corruptions
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
Operations Research | November 2022
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan Foster, Nathan Srebro, Blake E. Woodworth
Mathematical Programming | May 2022
Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, Vol 64(12): pp. 86-92
Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, Vol 67(7): pp. 30-32
https://doi.org/10.1145/3466612
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
Stretching Human Laws to Apply to Machines: The Dangers of a ‘Colorblind’ Computer
Zach Harned, Hanna Wallach
Florida State University Law Review, Forthcoming | December 2019
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, Vol 18(193): pp. 1-46
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
SIAM Journal of Optimization | September 2016, Vol 26: pp. 2775-2799
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
IEEE Transcations on Information Theory | September 2016
A Multiworld Testing Decision Service
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
ArXiv preprint | June 2016
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
ArXiv preprint | March 2016
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, Vol 12(2): pp. 4-23
Position paper and survey.
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, Vol 1
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
Alekh Agarwal, Peter Bartlett, Pradeep Ravikumar, Martin Wainwright
IEEE Transcations on Information Theory | May 2012, Vol 58
Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
Characterizing Truthful Multi-armed Bandit Mechanisms
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
SIAM Journal on Computing (SICOMP) | November 2008, Vol 43: pp. 194-230
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Generic Quantum Block Compression
September 2001
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan L. Boyd-Graber, Hal Daumé III
April 2021
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
June 2016
The offset tree for learning with partial labels
Alina Beygelzimer, John Langford
April 2016
Para-active learning
Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford
October 2013
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
Learning Performance of Prediction Markets with Kelly Bettors
Alina Beygelzimer, John Langford, David Pennock
June 2012
Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
June 2011
Parallel Online Learning
Daniel Hsu, Nikos Karampatziakis, John Langford, Alex J. Smola
March 2011
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Sham Kakade
June 2010
Agnostic Active Learning Without Constraints
Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang
June 2010
Error-Correcting Tournaments
Alina Beygelzimer, John Langford, Pradeep Ravikumar
February 2010
Multi-Label Prediction via Compressed Sensing
Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang
June 2009
Learning Nonlinear Dynamic Models
John Langford, Ruslan Salakhutdinov, Tong Zhang
June 2009
Importance Weighted Active Learning
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
May 2009
Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
July 2008
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Efficient Online Bootstrapping for Large Scale Learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford (jcl)
MSR-TR-2013-132 | December 2013
Published by Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
Crowdsourcing Gold-HIT Creation at Scale: Challenges and Adaptive Exploration Approaches (Position Paper)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins, Hai Wu
MSR-TR-2013-106 | October 2013