LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie, Adith Swaminathan
December 2023
December 2023
Real World Reinforcement Learning
設立年月日:May 3, 2019年
出版物
年別出版物
-
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
-
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 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
-
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 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
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
NAIL: A General Interactive Fiction Agent
Matthew Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, Jason Williams
arXiv: Machine Learning | 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
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
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
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
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
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
-
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
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
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
-
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 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
-
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
-
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
-
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
研究分野別論文
-
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
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie, Adith Swaminathan
December 2023
December 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
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 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
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 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
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
NAIL: A General Interactive Fiction Agent
Matthew Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, Jason Williams
arXiv: Machine Learning | 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
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
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
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
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
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
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 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
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 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
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
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
-
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
-
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
種類別出版物
-
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie, Adith Swaminathan
December 2023
December 2023
-
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
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020 Neural Information Processing Systems | October 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
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 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
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
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
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
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
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
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
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
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford, Tong Zhang
NIPS 2007 | December 2007
-
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
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
-
NAIL: A General Interactive Fiction Agent
Matthew Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, Jason Williams
arXiv: Machine Learning | February 2019
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014