Robert Schapire

Partner Researcher

출판물

자료 보기:

- Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles
  
  Aadirupa Saha, Robert E. Schapire
  
  NeurIPS 2025 | October 2025
  
  Publication
- Convex Analysis at Infinity: An Introduction to Astral Space
  
  Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  July 2024
  
  Preprint
- Provable Interactive Learning with Hindsight Instruction Feedback
  
  Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
  
  ICML 2024 | April 2024
  
  DOI Publication Publication
- Contextual search in the presence of adversarial corruptions
  
  Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
  
  Operations Research | November 2022
  
  Publication
- 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
  
  Publication 프로젝트
- Bayesian decision-making under misspecified priors with applications to meta-learning
  
  Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Multiclass Boosting and the Cost of Weak Learning
  
  Nataly Brukhim, Elad Hazan, Shay Moran, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Interactive Learning from Activity Description
  
  Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Gradient Descent Follows the Regularization Path for General Losses
  
  Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  COLT 2020 | June 2020
  
  PDF
- 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
  
  Publication 프로젝트
- Learning Deep ResNet Blocks Sequentially using Boosting Theory
  
  Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
  
  ICML 2018 | July 2018
  
  Publication
- 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
  
  출판
- Oracle-Efficient Learning and Auction Design
  
  Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
  
  Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
  
  Publication Publication
- 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
  
  Publication 프로젝트
- 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
  
  출판
- 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
  
  Publication
- 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
  
  Publication
- Efficient Algorithms for Adversarial Contextual Learning
  
  Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
  
  International Conference on Machine Learning | June 2016
  
  Publication
- Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
  
  David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
  
  ArXiv preprint | March 2016
  
  출판
- 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
  
  출판
- 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
  
  출판
- Contextual Dueling Bandits
  
  Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
  
  Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
  
  Proceedings of The 28th Conference on Learning Theory (COLT)
  
  출판
- Robust Multi-objective Learning with Mentor Feedback
  
  Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
  
  27th Conf. on Learning Theory (COLT) | July 2014
  
  27th Conf. on Learning Theory (COLT)
  
  출판
- 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
  
  Publication 프로젝트 프로젝트
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
  
  Alekh Agarwal, Daniel Hus, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
  
  MSR-TR-2014-33 | February 2014
  
  출판 프로젝트
- Contextual bandit algorithms with supervised learning guarantees
  
  Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | October 2011
  
  Publication 프로젝트
- Contextual Bandits with Linear Payoﬀ Functions
  
  Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | April 2011
  
  Publication 프로젝트
- 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
  
  Publication 프로젝트
- The Nonstochastic Multiarmed Bandit Problem
  
  Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
  
  SIAM Journal on Computing | October 2002, 제 32 권(1): pp. 48-77
  
  Publication
- 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
  
  Publication 프로젝트 프로젝트

- Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles
  
  Aadirupa Saha, Robert E. Schapire
  
  NeurIPS 2025 | October 2025
  
  Publication
- Multiclass Boosting and the Cost of Weak Learning
  
  Nataly Brukhim, Elad Hazan, Shay Moran, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Oracle-Efficient Learning and Auction Design
  
  Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
  
  Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
  
  Publication Publication
- 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
  
  Publication
- Efficient Algorithms for Adversarial Contextual Learning
  
  Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
  
  International Conference on Machine Learning | June 2016
  
  Publication
- Robust Multi-objective Learning with Mentor Feedback
  
  Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
  
  27th Conf. on Learning Theory (COLT) | July 2014
  
  27th Conf. on Learning Theory (COLT)
  
  출판
- 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
  
  Publication 프로젝트 프로젝트
- Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles
  
  Aadirupa Saha, Robert E. Schapire
  
  NeurIPS 2025 | October 2025
  
  Publication
- Convex Analysis at Infinity: An Introduction to Astral Space
  
  Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  July 2024
  
  Preprint
- Provable Interactive Learning with Hindsight Instruction Feedback
  
  Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
  
  ICML 2024 | April 2024
  
  DOI Publication Publication
- Contextual search in the presence of adversarial corruptions
  
  Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
  
  Operations Research | November 2022
  
  Publication
- 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
  
  Publication 프로젝트
- Bayesian decision-making under misspecified priors with applications to meta-learning
  
  Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Multiclass Boosting and the Cost of Weak Learning
  
  Nataly Brukhim, Elad Hazan, Shay Moran, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Interactive Learning from Activity Description
  
  Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Gradient Descent Follows the Regularization Path for General Losses
  
  Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  COLT 2020 | June 2020
  
  PDF
- 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
  
  Publication 프로젝트
- Learning Deep ResNet Blocks Sequentially using Boosting Theory
  
  Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
  
  ICML 2018 | July 2018
  
  Publication
- 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
  
  출판
- Oracle-Efficient Learning and Auction Design
  
  Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
  
  Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
  
  Publication Publication
- 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
  
  Publication 프로젝트
- 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
  
  Publication
- 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
  
  출판
- Efficient Algorithms for Adversarial Contextual Learning
  
  Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
  
  International Conference on Machine Learning | June 2016
  
  Publication
- Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
  
  David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
  
  ArXiv preprint | March 2016
  
  출판
- 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
  
  출판
- 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
  
  출판
- Contextual Dueling Bandits
  
  Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
  
  Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
  
  Proceedings of The 28th Conference on Learning Theory (COLT)
  
  출판
- Robust Multi-objective Learning with Mentor Feedback
  
  Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
  
  27th Conf. on Learning Theory (COLT) | July 2014
  
  27th Conf. on Learning Theory (COLT)
  
  출판
- 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
  
  Publication 프로젝트 프로젝트
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
  
  Alekh Agarwal, Daniel Hus, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
  
  MSR-TR-2014-33 | February 2014
  
  출판 프로젝트
- Contextual bandit algorithms with supervised learning guarantees
  
  Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | October 2011
  
  Publication 프로젝트
- Contextual Bandits with Linear Payoﬀ Functions
  
  Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | April 2011
  
  Publication 프로젝트
- 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
  
  Publication 프로젝트
- The Nonstochastic Multiarmed Bandit Problem
  
  Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
  
  SIAM Journal on Computing | October 2002, 제 32 권(1): pp. 48-77
  
  Publication
- Convex Analysis at Infinity: An Introduction to Astral Space
  
  Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  July 2024
  
  Preprint
- Oracle-Efficient Learning and Auction Design
  
  Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
  
  Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
  
  Publication Publication

- 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
  
  Publication 프로젝트 프로젝트
- Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles
  
  Aadirupa Saha, Robert E. Schapire
  
  NeurIPS 2025 | October 2025
  
  Publication
- Provable Interactive Learning with Hindsight Instruction Feedback
  
  Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
  
  ICML 2024 | April 2024
  
  DOI Publication Publication
- 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
  
  Publication 프로젝트
- Bayesian decision-making under misspecified priors with applications to meta-learning
  
  Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Multiclass Boosting and the Cost of Weak Learning
  
  Nataly Brukhim, Elad Hazan, Shay Moran, Robert E. Schapire
  
  NeurIPS 2021 | December 2021
  
  Publication
- Interactive Learning from Activity Description
  
  Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miro Dudík, Patrick Shafto
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Gradient Descent Follows the Regularization Path for General Losses
  
  Ziwei Ji, Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  COLT 2020 | June 2020
  
  PDF
- 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
  
  Publication 프로젝트
- Learning Deep ResNet Blocks Sequentially using Boosting Theory
  
  Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
  
  ICML 2018 | July 2018
  
  Publication
- 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
  
  출판
- Oracle-Efficient Learning and Auction Design
  
  Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
  
  Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
  
  Publication Publication
- 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
  
  Publication 프로젝트
- 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
  
  Publication
- 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
  
  출판
- 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
  
  Publication
- Efficient Algorithms for Adversarial Contextual Learning
  
  Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
  
  International Conference on Machine Learning | June 2016
  
  Publication
- 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
  
  출판
- 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
  
  출판
- Contextual Dueling Bandits
  
  Miro Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
  
  Proceedings of The 28th Conference on Learning Theory (COLT) | July 2015
  
  Proceedings of The 28th Conference on Learning Theory (COLT)
  
  출판
- Robust Multi-objective Learning with Mentor Feedback
  
  Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert E. Schapire, Aleksandrs Slivkins, Aleksandrs Slivkins, Miro Dudík
  
  27th Conf. on Learning Theory (COLT) | July 2014
  
  27th Conf. on Learning Theory (COLT)
  
  출판
- 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
  
  Publication 프로젝트 프로젝트
- Contextual bandit algorithms with supervised learning guarantees
  
  Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | October 2011
  
  Publication 프로젝트
- Contextual Bandits with Linear Payoﬀ Functions
  
  Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
  
  AISTATS 2011 | April 2011
  
  Publication 프로젝트
- 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
  
  Publication 프로젝트
- Convex Analysis at Infinity: An Introduction to Astral Space
  
  Miro Dudík, Robert E. Schapire, Matus Telgarsky
  
  July 2024
  
  Preprint
- Contextual search in the presence of adversarial corruptions
  
  Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
  
  Operations Research | November 2022
  
  Publication
- Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
  
  David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
  
  ArXiv preprint | March 2016
  
  출판
- The Nonstochastic Multiarmed Bandit Problem
  
  Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
  
  SIAM Journal on Computing | October 2002, 제 32 권(1): pp. 48-77
  
  Publication
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
  
  Alekh Agarwal, Daniel Hus, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
  
  MSR-TR-2014-33 | February 2014
  
  출판 프로젝트

Robert Schapire

출판물

Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles

Convex Analysis at Infinity: An Introduction to Astral Space

Provable Interactive Learning with Hindsight Instruction Feedback

Contextual search in the presence of adversarial corruptions

Provably sample-efficient RL with side information about latent dynamics

Bayesian decision-making under misspecified priors with applications to meta-learning

Multiclass Boosting and the Cost of Weak Learning

Interactive Learning from Activity Description

Gradient Descent Follows the Regularization Path for General Losses

On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Practical Contextual Bandits with Regression Oracles

Oracle-Efficient Learning and Auction Design

Contextual Decision Processes with low Bellman rank are PAC-Learnable

Corralling a Band of Bandit Algorithms

Open Problem: First-Order Regret Bounds for Contextual Bandits

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits

Efficient Algorithms for Adversarial Contextual Learning

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

Efficient and Parsimonious Agnostic Active Learning

Fast Convergence of Regularized Learning in Games

Contextual Dueling Bandits

Robust Multi-objective Learning with Mentor Feedback

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Contextual bandit algorithms with supervised learning guarantees

Contextual Bandits with Linear Payoﬀ Functions

A contextual-bandit approach to personalized news article recommendation

The Nonstochastic Multiarmed Bandit Problem

Contextual bandit learning with predictable rewards

Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles

Multiclass Boosting and the Cost of Weak Learning

Oracle-Efficient Learning and Auction Design

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits

Efficient Algorithms for Adversarial Contextual Learning

Robust Multi-objective Learning with Mentor Feedback

Contextual bandit learning with predictable rewards

Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles

Convex Analysis at Infinity: An Introduction to Astral Space

Provable Interactive Learning with Hindsight Instruction Feedback

Contextual search in the presence of adversarial corruptions

Provably sample-efficient RL with side information about latent dynamics

Bayesian decision-making under misspecified priors with applications to meta-learning

Multiclass Boosting and the Cost of Weak Learning

Interactive Learning from Activity Description

Gradient Descent Follows the Regularization Path for General Losses

On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Practical Contextual Bandits with Regression Oracles

Oracle-Efficient Learning and Auction Design

Contextual Decision Processes with low Bellman rank are PAC-Learnable

Open Problem: First-Order Regret Bounds for Contextual Bandits

Corralling a Band of Bandit Algorithms

Efficient Algorithms for Adversarial Contextual Learning

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

Fast Convergence of Regularized Learning in Games

Efficient and Parsimonious Agnostic Active Learning

Contextual Dueling Bandits

Robust Multi-objective Learning with Mentor Feedback

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Contextual bandit algorithms with supervised learning guarantees

Contextual Bandits with Linear Payoﬀ Functions

A contextual-bandit approach to personalized news article recommendation

The Nonstochastic Multiarmed Bandit Problem

Convex Analysis at Infinity: An Introduction to Astral Space

Oracle-Efficient Learning and Auction Design

Contextual bandit learning with predictable rewards

Efficient and Near-Optimal Algorithm for General Contextual Dueling Bandits with Offline Regression Oracles

Provable Interactive Learning with Hindsight Instruction Feedback

Provably sample-efficient RL with side information about latent dynamics

Bayesian decision-making under misspecified priors with applications to meta-learning

Multiclass Boosting and the Cost of Weak Learning

Interactive Learning from Activity Description

Gradient Descent Follows the Regularization Path for General Losses

On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Practical Contextual Bandits with Regression Oracles