Interactive Robot Learning from Verbal Correction
Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng
CoRL 2023 Workshop on Language and Robot Learning (LangRob) | November 2023
Return to Microsoft Research Lab – New York City
Reinforcement Learning
Publications
Publications by Year
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Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng
NeurIPS 2023 | October 2023
Hindsight Learning for MDPs with Exogenous Inputs
Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan
2023 International Conference on Machine Learning | July 2023
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Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan
2022 European Conference on Principles of Data Mining and Knowledge Discovery | September 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
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
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
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Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
NeurIPS 2021 | December 2021
Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
NeurIPS 2021 | December 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
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade
2021 Conference on Learning Theory | 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
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
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Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 2020
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
September 2020
Learning by Exploration: New Challenges in Real-World Environments
Qingyun Wu, Huazheng Wang, Hongning Wang
KDD ’20 | August 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
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | 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
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
Variational Integrator Networks for Physically Meaningful Embeddings
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth
Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS) | June 2020
Combining No-regret and Q-learning
Ian A. Kash, Michael Sullins, Katja Hofmann
2020 Adaptive Agents and Multi-Agents Systems | May 2020
AMRL: Aggregated Memory For Reinforcement Learning
Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
Eighth International Conference on Learning Representations (ICLR) | April 2020
Conservative Uncertainty Estimation By Fitting Prior Networks
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
Eighth International Conference on Learning Representations (ICLR) | April 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
Eighth International Conference on Learning Representations (ICLR) | April 2020
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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Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
Proceedings of the 33th Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Reinforcement Learning: Past, Present, and Future Perspectives
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Tutorial
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) | December 2019
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
Neural Information Processing Systems (NeurIPS) | December 2019
Oral Presentation
Safe Policy Improvement with Soft Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Conference in Machine Learning (ECML-PKDD) | September 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
Budgeted Policy Learning for Task-Oriented Dialogue Systems
Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen
ACL 2019 | July 2019
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler
NeurIPS 2019 | July 2019
Preprint under review
Multi-batch Reinforcement Learning
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
SPIBB-DQN: Safe Batch Reinforcement Learning with Function Approximation
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, JJ (Jingjing) Liu, Jianfeng Gao, Yejin Choi, Siddhartha Srinivasa
CVPR 2019 | June 2019
Oral
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
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
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet des Combes
International Conference on Machine Learning (ICML) | June 2019
Neural TTS Stylization with Adversarial and Collaborative Games
ShuangMa, Daniel McDuff, Yale Song
International Conference on Learning Representations (ICLR) | April 2019
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
Daniel McDuff, Ashish Kapoor
ICLR 2019 | April 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
Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | April 2019
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian Sadler, Huan Sun
AAAI 2019 | January 2019
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Xiujun Li, JJ (Jingjing) Liu, Jianfeng Gao, Yiming Yang
AAAI 2019 | January 2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N Negahban
January 2019
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A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
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
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
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen
EMNLP 2018 | October 2018
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
EMNLP 2018 | October 2018
Combining No-regret and Q-learning (2018)
Ian Kash, Katja Hofmann
The 14th European Workshop on Reinforcement Learning (EWRL 2018) | October 2018
Also presented at the AAAI-19 workshop on Reinforcement Learning in Games.
Counting to Explore and Generalize in Text-based Games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler
European Workshop on Reinforcement Learning (EWRL) | October 2018
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
European Workshop for Reinforcement Learning (EWRL) | October 2018
Soft Safe Policy Improvement with Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Workshop on Reinforcement Learning (EWRL) | October 2018
Training Dialogue Systems With Human Advice
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) | July 2018
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
Proceedings of International Conference on Machine Learning (ICML), 2018 | July 2018
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
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Xiujun Li, Sarah Panda, JJ (Jingjing) Liu, Jianfeng Gao
SLT 2018 | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Efficient Contextual Bandits in Non-stationary Worlds
Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2018
Conference on Learning Theory
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Conference on Uncertainty in Artificial Intelligence (UAI) | May 2018
Deep Learning with Logged Bandit Feedback
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
2018 International Conference on Learning Representations | May 2018
In reinforcement learning, all objective functions are not equal
Romain Laroche, Harm van Seijen
Proceedings of the 6th International Conference on Learning Representations (ICLR) – workshop track | May 2018
Reinforcement Learning Algorithm Selection
Romain Laroche, Raphael Feraud
Proceedings of the 6th International Conference on Learning Representations (ICLR) | May 2018
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen, Kam-Fai Wong
ICASSP 2018 | April 2018
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
On Value Function Representation of Long Horizon Problems
Lucas Lehnert, Romain Laroche, Harm van Seijen
Proceedings of the 33rd AAAI international conference | February 2018
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng
AAAI 2018 | February 2018
Deep Dyna-Q: Integrating planning for task-completion dialogue policy learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Kam-Fai Wong, Shang-Yu Su
ACL 2018 | January 2018
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Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
arXiv preprint arXiv:1712.06924 | December 2017, pp. 8+1+8
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
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen (jianshuc), Chong Wang, Lin Xiao (lixiao), Ji He, Lihong Li, Li Deng
Advances in Neural Information Processing Systems (NIPS) 29 | December 2017
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) | September 2017
Online learning and transfer for user adaptation in dialogue systems
Nicolas Carrara, Romain Laroche, Olivier Pietquin
Proceedings of the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL) | August 2017
The complex negotiation dialogue game
Romain Laroche
Proceedings of the 21st Workshop on on the Semantics and Pragmatics of Dialogues (SemDial) | August 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
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Lihong Li, Yu Lu, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
Stochastic Variance Reduction Methods for Policy Evaluation
Simon Du, Jianshu Chen, Lihong Li, Lin Xiao, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Hybrid Code Networks: Practical and Efficient End-To-End Dialog Control With Supervised and Reinforcement Learning
Jason Williams, Kavosh Asadi, Geoffrey Zweig
Proceedings of 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) | July 2017
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics | July 2017
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, Yoav Artzi
July 2017
A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning
Hatim Khouzaimi, Romain Laroche, Fabrice Lefèvre
Computer Speech & Language | July 2017, Vol 47: pp. 93-111
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
Hybrid Reward Architecture for Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang
Neural Information Processing Systems (NIPS) | June 2017
Algorithm selection of reinforcement learning algorithms
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Making Contextual Decisions with Low Technical Debt
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
May 2017
Multi-Advisor Reinforcement Learning
Romain Laroche, Mehdi Fatemi, Harm van Seijen, Joshua Romoff
April 2017
arXiv:1704.00756
Improving Scalability of Reinforcement Learning by Separation of Concerns
Harm van Seijen, Mehdi Fatemi, Josh Romoff, Romain Laroche
arXiv | March 2017
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche
aRXiv | March 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche, Merwan Barlier
Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI) | February 2017
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Sample-efficient Deep Reinforcement Learning for Dialog Control
Kavosh Asadi, Jason Williams
MSR-TR-2016-1134 | December 2016
Learning dialogue dynamics with the method of moments
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
Towards a virtual personal assistant based on a user-defined portfolio of multi-domain vocal applications
Tatiana Ekeinhor-Komi, Jean-Leon Bouraoui, Romain Laroche, Fabrice Lefevre
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
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
Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | December 2016
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudik
NIPS’16 Proceedings of the 30th International Conference on Neural Information Processing Systems | November 2016
End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
MSR-TR-2016-63 | September 2016
Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking
Zachary C. Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, Li Deng
MSR-TR-2016-62 | August 2016
arXiv:1608.05081
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) | August 2016
The Malmo Platform for Artificial Intelligence Experimentation
Matthew Johnson, Katja Hofmann, Tm Hutton, David Bignell, Katja Hofmann
25th International Joint Conference on Artificial Intelligence (IJCAI-16) | July 2016
Click-based Hot Fixes for Underperforming Torso Queries
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | July 2016
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
On the Prior Sensitivity of Thompson Sampling
Lihong Li
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT) | June 2016
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT)
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016 International Conference on Machine Learning | June 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
Policy Networks with Two-Stage Training for Dialogue Systems
Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, Kaheer Suleman
Proceedings of SIGDial 2016 | June 2016
End-To-End LSTM-Based Dialog Control Optimized With Supervised And Reinforcement Learning
Jason Williams, Geoffrey Zweig
MSR-TR-2016-72 | June 2016
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
June 2016
An Efficient Algorithm for Contextual Bandits with Knapsacks, and an Extension to Concave Objectives
Shipra Agrawal, Nikhil Devanur, Lihong Li
Proceedings of the 29th Conference on Learning Theory (COLT) – JMLR: Workshop and Conference Proceedings | June 2016
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
JMLR: Workshop and Conference Proceedings; Proceedings of the 33rd International Conference on Machine Learning (ICML) | 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
Collective Noise Contrastive Estimation for Policy Transfer Learning
Weinan Zhang, Ulrich Paquet, Katja Hofmann
Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016) | February 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
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
MSR-TR-2015-86 | November 2015
Learning to Search Better Than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
International Conference on Machine Learning | July 2015
International Conference on Machine Learning
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
Learning to Search for Dependencies
Kai-Wei Chang, He He, Hal Daumé III, John Langford
May 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
The Intermediate Neutrino Program
John Langford, and many others
March 2015
Learning Reductions that Really Work
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 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
Conditional Probability Tree Estimation Analysis and Algorithms
Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex Strehl
Twenty-Fifth Conference on Uncertainty in Artificial Intelligence | August 2014
See related paper from June 2009
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
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
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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
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
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 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
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
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
Contextual Bandits with Linear Payoff Functions
Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | April 2011
Parallel Online Learning
Daniel Hsu, Nikos Karampatziakis, John Langford, Alex J. Smola
March 2011
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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
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, Robert E. Schapire
WWW 2010 | March 2010
2023 Seoul Test of Time Award
Error-Correcting Tournaments
Alina Beygelzimer, John Langford, Pradeep Ravikumar
February 2010
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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
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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Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
July 2008
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The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
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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.
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Generic Quantum Block Compression
September 2001
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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
Publications by Research Area
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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
Interactive Robot Learning from Verbal Correction
Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng
CoRL 2023 Workshop on Language and Robot Learning (LangRob) | November 2023
Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng
NeurIPS 2023 | October 2023
Hindsight Learning for MDPs with Exogenous Inputs
Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan
2023 International Conference on Machine Learning | July 2023
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan
2022 European Conference on Principles of Data Mining and Knowledge Discovery | September 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
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
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
NeurIPS 2021 | December 2021
Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
NeurIPS 2021 | December 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
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade
2021 Conference on Learning Theory | 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
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
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 2020
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
September 2020
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
Learning by Exploration: New Challenges in Real-World Environments
Qingyun Wu, Huazheng Wang, Hongning Wang
KDD ’20 | August 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
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | 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
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | 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
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
Variational Integrator Networks for Physically Meaningful Embeddings
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth
Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS) | June 2020
Combining No-regret and Q-learning
Ian A. Kash, Michael Sullins, Katja Hofmann
2020 Adaptive Agents and Multi-Agents Systems | May 2020
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
Eighth International Conference on Learning Representations (ICLR) | April 2020
Conservative Uncertainty Estimation By Fitting Prior Networks
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
Eighth International Conference on Learning Representations (ICLR) | April 2020
AMRL: Aggregated Memory For Reinforcement Learning
Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
Eighth International Conference on Learning Representations (ICLR) | April 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
Proceedings of the 33th Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Reinforcement Learning: Past, Present, and Future Perspectives
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Tutorial
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
Neural Information Processing Systems (NeurIPS) | December 2019
Oral Presentation
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) | December 2019
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Safe Policy Improvement with Soft Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Conference in Machine Learning (ECML-PKDD) | 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
Budgeted Policy Learning for Task-Oriented Dialogue Systems
Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen
ACL 2019 | July 2019
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler
NeurIPS 2019 | July 2019
Preprint under review
SPIBB-DQN: Safe Batch Reinforcement Learning with Function Approximation
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
Multi-batch Reinforcement Learning
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, JJ (Jingjing) Liu, Jianfeng Gao, Yejin Choi, Siddhartha Srinivasa
CVPR 2019 | June 2019
Oral
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
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet des Combes
International Conference on Machine Learning (ICML) | 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
Neural TTS Stylization with Adversarial and Collaborative Games
ShuangMa, Daniel McDuff, Yale Song
International Conference on Learning Representations (ICLR) | April 2019
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
Daniel McDuff, Ashish Kapoor
ICLR 2019 | April 2019
Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | April 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
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Xiujun Li, JJ (Jingjing) Liu, Jianfeng Gao, Yiming Yang
AAAI 2019 | January 2019
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian Sadler, Huan Sun
AAAI 2019 | January 2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N Negahban
January 2019
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
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
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
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen
EMNLP 2018 | October 2018
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
EMNLP 2018 | October 2018
Counting to Explore and Generalize in Text-based Games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler
European Workshop on Reinforcement Learning (EWRL) | October 2018
Soft Safe Policy Improvement with Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Workshop on Reinforcement Learning (EWRL) | October 2018
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
European Workshop for Reinforcement Learning (EWRL) | October 2018
Combining No-regret and Q-learning (2018)
Ian Kash, Katja Hofmann
The 14th European Workshop on Reinforcement Learning (EWRL 2018) | October 2018
Also presented at the AAAI-19 workshop on Reinforcement Learning in Games.
Training Dialogue Systems With Human Advice
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) | July 2018
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
Proceedings of International Conference on Machine Learning (ICML), 2018 | July 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
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Xiujun Li, Sarah Panda, JJ (Jingjing) Liu, Jianfeng Gao
SLT 2018 | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Efficient Contextual Bandits in Non-stationary Worlds
Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2018
Conference on Learning Theory
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Conference on Uncertainty in Artificial Intelligence (UAI) | May 2018
Deep Learning with Logged Bandit Feedback
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
2018 International Conference on Learning Representations | May 2018
In reinforcement learning, all objective functions are not equal
Romain Laroche, Harm van Seijen
Proceedings of the 6th International Conference on Learning Representations (ICLR) – workshop track | May 2018
Reinforcement Learning Algorithm Selection
Romain Laroche, Raphael Feraud
Proceedings of the 6th International Conference on Learning Representations (ICLR) | May 2018
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen, Kam-Fai Wong
ICASSP 2018 | April 2018
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
On Value Function Representation of Long Horizon Problems
Lucas Lehnert, Romain Laroche, Harm van Seijen
Proceedings of the 33rd AAAI international conference | February 2018
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng
AAAI 2018 | February 2018
Deep Dyna-Q: Integrating planning for task-completion dialogue policy learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Kam-Fai Wong, Shang-Yu Su
ACL 2018 | January 2018
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
arXiv preprint arXiv:1712.06924 | December 2017, pp. 8+1+8
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
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen (jianshuc), Chong Wang, Lin Xiao (lixiao), Ji He, Lihong Li, Li Deng
Advances in Neural Information Processing Systems (NIPS) 29 | December 2017
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) | September 2017
Online learning and transfer for user adaptation in dialogue systems
Nicolas Carrara, Romain Laroche, Olivier Pietquin
Proceedings of the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL) | August 2017
The complex negotiation dialogue game
Romain Laroche
Proceedings of the 21st Workshop on on the Semantics and Pragmatics of Dialogues (SemDial) | August 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
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Lihong Li, Yu Lu, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
Stochastic Variance Reduction Methods for Policy Evaluation
Simon Du, Jianshu Chen, Lihong Li, Lin Xiao, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Hybrid Code Networks: Practical and Efficient End-To-End Dialog Control With Supervised and Reinforcement Learning
Jason Williams, Kavosh Asadi, Geoffrey Zweig
Proceedings of 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) | July 2017
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics | July 2017
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, Yoav Artzi
July 2017
A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning
Hatim Khouzaimi, Romain Laroche, Fabrice Lefèvre
Computer Speech & Language | July 2017, Vol 47: pp. 93-111
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
Hybrid Reward Architecture for Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang
Neural Information Processing Systems (NIPS) | June 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Algorithm selection of reinforcement learning algorithms
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Making Contextual Decisions with Low Technical Debt
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
May 2017
Multi-Advisor Reinforcement Learning
Romain Laroche, Mehdi Fatemi, Harm van Seijen, Joshua Romoff
April 2017
arXiv:1704.00756
Improving Scalability of Reinforcement Learning by Separation of Concerns
Harm van Seijen, Mehdi Fatemi, Josh Romoff, Romain Laroche
arXiv | March 2017
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche
aRXiv | March 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche, Merwan Barlier
Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI) | February 2017
Sample-efficient Deep Reinforcement Learning for Dialog Control
Kavosh Asadi, Jason Williams
MSR-TR-2016-1134 | December 2016
Learning dialogue dynamics with the method of moments
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
Towards a virtual personal assistant based on a user-defined portfolio of multi-domain vocal applications
Tatiana Ekeinhor-Komi, Jean-Leon Bouraoui, Romain Laroche, Fabrice Lefevre
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | 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
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudik
NIPS’16 Proceedings of the 30th International Conference on Neural Information Processing Systems | November 2016
End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
MSR-TR-2016-63 | September 2016
Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking
Zachary C. Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, Li Deng
MSR-TR-2016-62 | August 2016
arXiv:1608.05081
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) | August 2016
The Malmo Platform for Artificial Intelligence Experimentation
Matthew Johnson, Katja Hofmann, Tm Hutton, David Bignell, Katja Hofmann
25th International Joint Conference on Artificial Intelligence (IJCAI-16) | July 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Click-based Hot Fixes for Underperforming Torso Queries
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | July 2016
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
On the Prior Sensitivity of Thompson Sampling
Lihong Li
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT) | June 2016
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT)
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016 International Conference on Machine Learning | June 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
Policy Networks with Two-Stage Training for Dialogue Systems
Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, Kaheer Suleman
Proceedings of SIGDial 2016 | 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
An Efficient Algorithm for Contextual Bandits with Knapsacks, and an Extension to Concave Objectives
Shipra Agrawal, Nikhil Devanur, Lihong Li
Proceedings of the 29th Conference on Learning Theory (COLT) – JMLR: Workshop and Conference Proceedings | June 2016
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
JMLR: Workshop and Conference Proceedings; Proceedings of the 33rd International Conference on Machine Learning (ICML) | 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
Collective Noise Contrastive Estimation for Policy Transfer Learning
Weinan Zhang, Ulrich Paquet, Katja Hofmann
Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016) | February 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
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
MSR-TR-2015-86 | November 2015
Learning to Search Better Than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
International Conference on Machine Learning | July 2015
International Conference on Machine Learning
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
Learning to Search for Dependencies
Kai-Wei Chang, He He, Hal Daumé III, John Langford
May 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
The Intermediate Neutrino Program
John Langford, and many others
March 2015
Learning Reductions that Really Work
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 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
Conditional Probability Tree Estimation Analysis and Algorithms
Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex Strehl
Twenty-Fifth Conference on Uncertainty in Artificial Intelligence | August 2014
See related paper from June 2009
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
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
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.
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
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 2013
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
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
Contextual Bandits with Linear Payoff Functions
Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | April 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
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, Robert E. Schapire
WWW 2010 | March 2010
2023 Seoul Test of Time Award
Error-Correcting Tournaments
Alina Beygelzimer, John Langford, Pradeep Ravikumar
February 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
Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
July 2008
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
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.
-
Hindsight Learning for MDPs with Exogenous Inputs
Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan
2023 International Conference on Machine Learning | July 2023
-
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
NeurIPS 2021 | December 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade
2021 Conference on Learning Theory | 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
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
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
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
Proceedings of International Conference on Machine Learning (ICML), 2018 | July 2018
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen (jianshuc), Chong Wang, Lin Xiao (lixiao), Ji He, Lihong Li, Li Deng
Advances in Neural Information Processing Systems (NIPS) 29 | December 2017
Stochastic Variance Reduction Methods for Policy Evaluation
Simon Du, Jianshu Chen, Lihong Li, Lin Xiao, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 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
Conditional Probability Tree Estimation Analysis and Algorithms
Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex Strehl
Twenty-Fifth Conference on Uncertainty in Artificial Intelligence | August 2014
See related paper from June 2009
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
-
Generic Quantum Block Compression
September 2001
-
The Intermediate Neutrino Program
John Langford, and many others
March 2015
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.
-
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) | December 2019
Deep Learning with Logged Bandit Feedback
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
2018 International Conference on Learning Representations | May 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
Click-based Hot Fixes for Underperforming Torso Queries
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | July 2016
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016 International Conference on Machine Learning | June 2016
-
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, JJ (Jingjing) Liu, Jianfeng Gao, Yejin Choi, Siddhartha Srinivasa
CVPR 2019 | June 2019
Oral
-
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
-
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) | September 2017
Hybrid Code Networks: Practical and Efficient End-To-End Dialog Control With Supervised and Reinforcement Learning
Jason Williams, Kavosh Asadi, Geoffrey Zweig
Proceedings of 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) | July 2017
Sample-efficient Deep Reinforcement Learning for Dialog Control
Kavosh Asadi, Jason Williams
MSR-TR-2016-1134 | December 2016
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) | August 2016
End-To-End LSTM-Based Dialog Control Optimized With Supervised And Reinforcement Learning
Jason Williams, Geoffrey Zweig
MSR-TR-2016-72 | June 2016
-
Click-based Hot Fixes for Underperforming Torso Queries
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | July 2016
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publications by Type
-
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
Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng
NeurIPS 2023 | October 2023
Hindsight Learning for MDPs with Exogenous Inputs
Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan
2023 International Conference on Machine Learning | July 2023
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan
2022 European Conference on Principles of Data Mining and Knowledge Discovery | September 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
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
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
NeurIPS 2021 | December 2021
Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
NeurIPS 2021 | December 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
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade
2021 Conference on Learning Theory | 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
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
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 2020
Learning by Exploration: New Challenges in Real-World Environments
Qingyun Wu, Huazheng Wang, Hongning Wang
KDD ’20 | August 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
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | 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
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht
2020 International Conference on Machine Learning | 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
Variational Integrator Networks for Physically Meaningful Embeddings
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth
Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS) | June 2020
Combining No-regret and Q-learning
Ian A. Kash, Michael Sullins, Katja Hofmann
2020 Adaptive Agents and Multi-Agents Systems | May 2020
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson
Eighth International Conference on Learning Representations (ICLR) | April 2020
Conservative Uncertainty Estimation By Fitting Prior Networks
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
Eighth International Conference on Learning Representations (ICLR) | April 2020
AMRL: Aggregated Memory For Reinforcement Learning
Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
Eighth International Conference on Learning Representations (ICLR) | April 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
Proceedings of the 33th Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Reinforcement Learning: Past, Present, and Future Perspectives
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | December 2019
Tutorial
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
Neural Information Processing Systems (NeurIPS) | December 2019
Oral Presentation
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) | December 2019
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Safe Policy Improvement with Soft Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Conference in Machine Learning (ECML-PKDD) | September 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
Budgeted Policy Learning for Task-Oriented Dialogue Systems
Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen
ACL 2019 | July 2019
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler
NeurIPS 2019 | July 2019
Preprint under review
SPIBB-DQN: Safe Batch Reinforcement Learning with Function Approximation
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
Multi-batch Reinforcement Learning
Romain Laroche, Remi Tachet des Combes
The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) | July 2019
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, JJ (Jingjing) Liu, Jianfeng Gao, Yejin Choi, Siddhartha Srinivasa
CVPR 2019 | June 2019
Oral
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
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet des Combes
International Conference on Machine Learning (ICML) | 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
Neural TTS Stylization with Adversarial and Collaborative Games
ShuangMa, Daniel McDuff, Yale Song
International Conference on Learning Representations (ICLR) | April 2019
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
Daniel McDuff, Ashish Kapoor
ICLR 2019 | April 2019
Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | April 2019
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Xiujun Li, JJ (Jingjing) Liu, Jianfeng Gao, Yiming Yang
AAAI 2019 | January 2019
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian Sadler, Huan Sun
AAAI 2019 | 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
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
Shang-Yu Su, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen
EMNLP 2018 | October 2018
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
EMNLP 2018 | October 2018
Counting to Explore and Generalize in Text-based Games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler
European Workshop on Reinforcement Learning (EWRL) | October 2018
Soft Safe Policy Improvement with Baseline Bootstrapping
Kimia Nadjahi, Romain Laroche, Remi Tachet des Combes
European Workshop on Reinforcement Learning (EWRL) | October 2018
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
European Workshop for Reinforcement Learning (EWRL) | October 2018
Combining No-regret and Q-learning (2018)
Ian Kash, Katja Hofmann
The 14th European Workshop on Reinforcement Learning (EWRL 2018) | October 2018
Also presented at the AAAI-19 workshop on Reinforcement Learning in Games.
Training Dialogue Systems With Human Advice
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) | July 2018
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
Proceedings of International Conference on Machine Learning (ICML), 2018 | July 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
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Xiujun Li, Sarah Panda, JJ (Jingjing) Liu, Jianfeng Gao
SLT 2018 | July 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Efficient Contextual Bandits in Non-stationary Worlds
Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
Conference on Learning Theory | June 2018
Conference on Learning Theory
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Conference on Uncertainty in Artificial Intelligence (UAI) | May 2018
Deep Learning with Logged Bandit Feedback
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
2018 International Conference on Learning Representations | May 2018
In reinforcement learning, all objective functions are not equal
Romain Laroche, Harm van Seijen
Proceedings of the 6th International Conference on Learning Representations (ICLR) – workshop track | May 2018
Reinforcement Learning Algorithm Selection
Romain Laroche, Raphael Feraud
Proceedings of the 6th International Conference on Learning Representations (ICLR) | May 2018
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Yun-Nung Chen, Kam-Fai Wong
ICASSP 2018 | April 2018
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
On Value Function Representation of Long Horizon Problems
Lucas Lehnert, Romain Laroche, Harm van Seijen
Proceedings of the 33rd AAAI international conference | February 2018
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng
AAAI 2018 | February 2018
Deep Dyna-Q: Integrating planning for task-completion dialogue policy learning
Baolin Peng, Xiujun Li, Jianfeng Gao, JJ (Jingjing) Liu, Kam-Fai Wong, Shang-Yu Su
ACL 2018 | January 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
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen (jianshuc), Chong Wang, Lin Xiao (lixiao), Ji He, Lihong Li, Li Deng
Advances in Neural Information Processing Systems (NIPS) 29 | December 2017
Online learning and transfer for user adaptation in dialogue systems
Nicolas Carrara, Romain Laroche, Olivier Pietquin
Proceedings of the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL) | August 2017
The complex negotiation dialogue game
Romain Laroche
Proceedings of the 21st Workshop on on the Semantics and Pragmatics of Dialogues (SemDial) | August 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
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Lihong Li, Yu Lu, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
Stochastic Variance Reduction Methods for Policy Evaluation
Simon Du, Jianshu Chen, Lihong Li, Lin Xiao, Denny Zhou
Proceedings of the 34th International Conference on Machine Learning (ICML) | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Hybrid Code Networks: Practical and Efficient End-To-End Dialog Control With Supervised and Reinforcement Learning
Jason Williams, Kavosh Asadi, Geoffrey Zweig
Proceedings of 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) | July 2017
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics | 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
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
Hybrid Reward Architecture for Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang
Neural Information Processing Systems (NIPS) | June 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Algorithm selection of reinforcement learning algorithms
Romain Laroche
Proceedings of the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making | June 2017
Transfer Reinforcement Learning with Shared Dynamics
Romain Laroche, Merwan Barlier
Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI) | February 2017
Learning dialogue dynamics with the method of moments
Merwan Barlier, Romain Laroche, Olivier Pietquin
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
Towards a virtual personal assistant based on a user-defined portfolio of multi-domain vocal applications
Tatiana Ekeinhor-Komi, Jean-Leon Bouraoui, Romain Laroche, Fabrice Lefevre
Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT) | December 2016
Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | December 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
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
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudik
NIPS’16 Proceedings of the 30th International Conference on Neural Information Processing Systems | November 2016
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) | August 2016
The Malmo Platform for Artificial Intelligence Experimentation
Matthew Johnson, Katja Hofmann, Tm Hutton, David Bignell, Katja Hofmann
25th International Joint Conference on Artificial Intelligence (IJCAI-16) | July 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Click-based Hot Fixes for Underperforming Torso Queries
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) | July 2016
Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
On the Prior Sensitivity of Thompson Sampling
Lihong Li
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT) | June 2016
Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT)
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016 International Conference on Machine Learning | June 2016
Policy Networks with Two-Stage Training for Dialogue Systems
Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, Kaheer Suleman
Proceedings of SIGDial 2016 | June 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
An Efficient Algorithm for Contextual Bandits with Knapsacks, and an Extension to Concave Objectives
Shipra Agrawal, Nikhil Devanur, Lihong Li
Proceedings of the 29th Conference on Learning Theory (COLT) – JMLR: Workshop and Conference Proceedings | June 2016
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
JMLR: Workshop and Conference Proceedings; Proceedings of the 33rd International Conference on Machine Learning (ICML) | June 2016
Collective Noise Contrastive Estimation for Policy Transfer Learning
Weinan Zhang, Ulrich Paquet, Katja Hofmann
Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016) | February 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
Learning to Search Better Than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
International Conference on Machine Learning | July 2015
International Conference on Machine Learning
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
Conditional Probability Tree Estimation Analysis and Algorithms
Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex Strehl
Twenty-Fifth Conference on Uncertainty in Artificial Intelligence | August 2014
See related paper from June 2009
Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
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
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
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
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
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
Contextual Bandits with Linear Payoff Functions
Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | April 2011
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, Robert E. Schapire
WWW 2010 | March 2010
2023 Seoul Test of Time Award
Slow Learners are Fast
John Langford, Alexander J. Smola, Martin Zinkevich
NIPS 2009 | November 2009
Extended version of conference paper – NIPS 2009
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
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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Interactive Robot Learning from Verbal Correction
Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng
CoRL 2023 Workshop on Language and Robot Learning (LangRob) | November 2023
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
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
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair
arXiv preprint arXiv:1712.06924 | December 2017, pp. 8+1+8
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) | September 2017
A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning
Hatim Khouzaimi, Romain Laroche, Fabrice Lefèvre
Computer Speech & Language | July 2017, Vol 47: pp. 93-111
Improving Scalability of Reinforcement Learning by Separation of Concerns
Harm van Seijen, Mehdi Fatemi, Josh Romoff, Romain Laroche
arXiv | March 2017
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche
aRXiv | March 2017
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
Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
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Generic Quantum Block Compression
September 2001
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
September 2020
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kiante Brantley, Miro Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
June 2020
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N Negahban
January 2019
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
December 2018
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, Yoav Artzi
July 2017
Making Contextual Decisions with Low Technical Debt
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Aleksandrs Slivkins
May 2017
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
Learning to Search for Dependencies
Kai-Wei Chang, He He, Hal Daumé III, John Langford
May 2015
The Intermediate Neutrino Program
John Langford, and many others
March 2015
Learning Reductions that Really Work
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2015
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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Multi-Advisor Reinforcement Learning
Romain Laroche, Mehdi Fatemi, Harm van Seijen, Joshua Romoff
April 2017
arXiv:1704.00756
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Sample-efficient Deep Reinforcement Learning for Dialog Control
Kavosh Asadi, Jason Williams
MSR-TR-2016-1134 | December 2016
End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
MSR-TR-2016-63 | September 2016
Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking
Zachary C. Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, Li Deng
MSR-TR-2016-62 | August 2016
arXiv:1608.05081
End-To-End LSTM-Based Dialog Control Optimized With Supervised And Reinforcement Learning
Jason Williams, Geoffrey Zweig
MSR-TR-2016-72 | June 2016
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
MSR-TR-2015-86 | November 2015
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.