Dion: Distributed Orthonormalized Updates
Kwangjun Ahn, Byron Xu, Natalie Abreu, John Langford
May 2025
John Langford
Partner Researcher Manager
Papiers de Collaboration
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EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
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Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg, Neel Joshi, Yash Lara, John Langford, Besmira Nushi, Vibhav Vineet, Yue Wu, Safoora Yousefi
MSR-TR-2025-16 | March 2025
Publié par Microsoft
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The Belief State Transformer
Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
ICLR 2025 | October 2024
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Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, A. Anandkumar, Furong Huang
NeurIPS 2024 | September 2024
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Agent AI Towards a Holistic Intelligence
Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley J. Llorens, John Langford, Hoi Vo, Fei-Fei Li, Katsushi Ikeuchi, Jianfeng Gao
arXiv: 2403.00833 | February 2024
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Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
ICML 2024 | February 2024
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PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
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Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
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Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
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Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
NeurIPS 2022 Workshop | November 2022
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Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
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Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
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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
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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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Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
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ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
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Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
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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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Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
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Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
NeurIPS 2020 | December 2020
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Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
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Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
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PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
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Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
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Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
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PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
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Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks
Vi Hart, Divya Siddarth, Bethan Cantrell, Lila Tretikov, Peter Eckersley, John Langford, Scott Leibrand, Sham Kakade, Steve Latta, Dana Lewis, Stefano Tessaro, E. Glen Weyl
Harvard Center for Ethics | April 2020
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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
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Federated Residual Learning
Alekh Agarwal, John Langford, Chen-Yu Wei
Mars 2020
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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
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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
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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
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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
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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
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Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | April 2019
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Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2019
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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
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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
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Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
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Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
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Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
July 2018
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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
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A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
May 2018
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A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
FATML’17 | March 2018
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Residual Loss Prediction: Reinforcement Learning with no Incremental Feedback
Hal Daumé III, John Langford, Paul Mineiro, Amr Sharaf
ICLR 2018 Conference | February 2018
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Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
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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
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Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, Yoav Artzi
July 2017
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Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
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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
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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)
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PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
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Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | December 2016
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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
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A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross
June 2016
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The offset tree for learning with partial labels
Alina Beygelzimer, John Langford
April 2016
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Orbiting Radiation Stars
Dean P Foster, John Langford, Gabe Perez-Giz
IOP Science | 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
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Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
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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
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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
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Learning to Search for Dependencies
Kai-Wei Chang, He He, Hal Daumé III, John Langford
May 2015
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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
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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
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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
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Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
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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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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, Vol 156: pp. 389-416
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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
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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
Publié par Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
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Para-active learning
Alekh Agarwal, Leon Bottou, Miroslav Dudik, John Langford
October 2013
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A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miro Dudík, John Langford
July 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
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Learning Performance of Prediction Markets with Kelly Bettors
Alina Beygelzimer, John Langford, David Pennock
June 2012
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Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
Twenty-Second Conference on Uncertainty in Artificial Intelligence | June 2012
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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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Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
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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
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Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
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Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
June 2011
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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
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Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Sham Kakade
June 2010
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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
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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
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Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
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Multi-Label Prediction via Compressed Sensing
Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang
June 2009
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Learning Nonlinear Dynamic Models
John Langford, Ruslan Salakhutdinov, Tong Zhang
June 2009
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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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Self-Financed Wagering Mechanisms for Forecasting
Nicolas Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, David Pennock
Ninth ACM Conference on Electronic Commerce (EC) | January 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
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Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
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Beating the Holdout: Bounds for K-Fold and Progressive Cross-Validation
Avrim Blum, Adam Tauman Kalai, John Langford
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory | July 1999
ISBN: 1-58113-167-4
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Generic Quantum Block Compression
September 2001
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Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
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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
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Dion: Distributed Orthonormalized Updates
Kwangjun Ahn, Byron Xu, Natalie Abreu, John Langford
May 2025
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EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
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Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg, Neel Joshi, Yash Lara, John Langford, Besmira Nushi, Vibhav Vineet, Yue Wu, Safoora Yousefi
MSR-TR-2025-16 | March 2025
Publié par Microsoft
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The Belief State Transformer
Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
ICLR 2025 | October 2024
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Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, A. Anandkumar, Furong Huang
NeurIPS 2024 | September 2024
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Agent AI Towards a Holistic Intelligence
Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley J. Llorens, John Langford, Hoi Vo, Fei-Fei Li, Katsushi Ikeuchi, Jianfeng Gao
arXiv: 2403.00833 | February 2024
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Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
ICML 2024 | February 2024
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PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
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Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
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Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
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Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
NeurIPS 2022 Workshop | November 2022
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Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
-
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
-
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
-
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
-
Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
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ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
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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
-
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
NeurIPS 2020 | December 2020
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Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
-
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
-
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
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
-
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
-
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
-
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
-
Federated Residual Learning
Alekh Agarwal, John Langford, Chen-Yu Wei
Mars 2020
-
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
-
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
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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
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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
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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
-
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
-
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
-
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
July 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
-
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
May 2018
-
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
FATML’17 | March 2018
-
Residual Loss Prediction: Reinforcement Learning with no Incremental Feedback
Hal Daumé III, John Langford, Paul Mineiro, Amr Sharaf
ICLR 2018 Conference | February 2018
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Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
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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
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Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, Yoav Artzi
July 2017
-
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
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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
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Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | December 2016
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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)
-
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
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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
-
Orbiting Radiation Stars
Dean P Foster, John Langford, Gabe Perez-Giz
IOP Science | February 2016
-
Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
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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
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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
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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
-
Learning Reductions that Really Work
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
February 2015
-
Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
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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
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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
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Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 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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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, Vol 156: pp. 389-416
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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
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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
Publié par Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
-
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
-
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
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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
-
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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Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
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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
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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
-
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)
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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.
-
-
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
-
Beating the Holdout: Bounds for K-Fold and Progressive Cross-Validation
Avrim Blum, Adam Tauman Kalai, John Langford
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory | July 1999
ISBN: 1-58113-167-4
-
EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
-
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | 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
-
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
-
-
-
EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
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-
-
EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
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-
-
EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
-
Agent AI Towards a Holistic Intelligence
Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley J. Llorens, John Langford, Hoi Vo, Fei-Fei Li, Katsushi Ikeuchi, Jianfeng Gao
arXiv: 2403.00833 | February 2024
-
Self-Financed Wagering Mechanisms for Forecasting
Nicolas Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, David Pennock
Ninth ACM Conference on Electronic Commerce (EC) | January 2008
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-
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Agent AI Towards a Holistic Intelligence
Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley J. Llorens, John Langford, Hoi Vo, Fei-Fei Li, Katsushi Ikeuchi, Jianfeng Gao
arXiv: 2403.00833 | February 2024
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-
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Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks
Vi Hart, Divya Siddarth, Bethan Cantrell, Lila Tretikov, Peter Eckersley, John Langford, Scott Leibrand, Sham Kakade, Steve Latta, Dana Lewis, Stefano Tessaro, E. Glen Weyl
Harvard Center for Ethics | April 2020
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-
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Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks
Vi Hart, Divya Siddarth, Bethan Cantrell, Lila Tretikov, Peter Eckersley, John Langford, Scott Leibrand, Sham Kakade, Steve Latta, Dana Lewis, Stefano Tessaro, E. Glen Weyl
Harvard Center for Ethics | April 2020
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-
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PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
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PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
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Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks
Vi Hart, Divya Siddarth, Bethan Cantrell, Lila Tretikov, Peter Eckersley, John Langford, Scott Leibrand, Sham Kakade, Steve Latta, Dana Lewis, Stefano Tessaro, E. Glen Weyl
Harvard Center for Ethics | April 2020
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-
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Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
NeurIPS 2020 | December 2020
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-
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Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
-
Suboptimal behaviour of Bayes and MDL in classification under misspecification
Peter Grunwald, John Langford
Computational Learning Theory 2004 | June 2004
This is a slightly longer version of the paper at COLT 2004, containing tow extra pages of discussion of the main results.
-
-
-
Generic Quantum Block Compression
September 2001
-
-
-
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
-
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
-
-
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
-
An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, Vol 156: pp. 389-416
-
-
-
Generic Quantum Block Compression
September 2001
-
Dion: Distributed Orthonormalized Updates
Kwangjun Ahn, Byron Xu, Natalie Abreu, John Langford
May 2025
-
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
September 2020
-
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting
Keyi Chen, John Langford, Francesca Orabona
June 2020
-
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (TR)
Justin Chan, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
April 2020
-
Federated Residual Learning
Alekh Agarwal, John Langford, Chen-Yu Wei
Mars 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
-
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
July 2018
-
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
May 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
-
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
-
-
-
Resourceful Contextual Bandits
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
27th Conf. on Learning Theory (COLT) | January 2000
-
Contextual bandit learning with predictable rewards
Miro Dudík, Satyen Kale, John Langford, Alekh Agarwal, Robert E. Schapire
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12) | January 2000
-
Beating the Holdout: Bounds for K-Fold and Progressive Cross-Validation
Avrim Blum, Adam Tauman Kalai, John Langford
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory | July 1999
ISBN: 1-58113-167-4
-
EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing
Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
CHI 2025 | April 2025
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The Belief State Transformer
Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
ICLR 2025 | October 2024
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Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, A. Anandkumar, Furong Huang
NeurIPS 2024 | September 2024
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Agent AI Towards a Holistic Intelligence
Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley J. Llorens, John Langford, Hoi Vo, Fei-Fei Li, Katsushi Ikeuchi, Jianfeng Gao
arXiv: 2403.00833 | February 2024
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Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
ICML 2024 | February 2024
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PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudík, John Langford, Alex Lamb
ICML 2024 | November 2023
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Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
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Interaction-Grounded Learning with Action-Inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan P Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
NeurIPS 2022 | November 2022
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Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
NeurIPS 2022 Workshop | November 2022
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Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro
2022 International Conference on Machine Learning | July 2022
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Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu
ICML 2022 | July 2022
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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
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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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Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
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Interaction-Grounded Learning
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021 International Conference on Machine Learning | July 2021
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ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021 International Conference on Machine Learning (ICML 2021) | July 2021
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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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Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
NeurIPS 2020 | December 2020
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Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
-
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
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Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
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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
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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
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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
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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
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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
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Logarithmic Time One-Against-Some
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
ICML 2017 | April 2019
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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
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Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furon Huang, Jordan Ash, John Langford, Robert E. Schapire
ICML 2018 | July 2018
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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
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A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
FATML’17 | March 2018
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Residual Loss Prediction: Reinforcement Learning with no Incremental Feedback
Hal Daumé III, John Langford, Paul Mineiro, Amr Sharaf
ICLR 2018 Conference | February 2018
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Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
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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
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Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
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Search Improves Label for Active Learning
Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
NIPS 2016 | December 2016
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PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
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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)
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Logarithmic Time Online Multiclass prediction
Anna Choromanska, John Langford
NIPS 2015 | December 2015
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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
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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
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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
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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
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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
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Normalized Online Learning
Stephane Ross, Paul Mineiro, John Langford
Twenty-Ninth Conference on Uncertainty in Artificial Intelligence2013 | August 2014
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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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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
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Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
Twenty-Second Conference on Uncertainty in Artificial Intelligence | June 2012
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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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Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
AISTATS 2011 | October 2011
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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
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Doubly Robust Policy Evaluation and Learning
Miro Dudík, John Langford, Lihong Li
ICML 2011 | June 2011
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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
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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
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Self-Financed Wagering Mechanisms for Forecasting
Nicolas Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, David Pennock
Ninth ACM Conference on Electronic Commerce (EC) | January 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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Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg, Neel Joshi, Yash Lara, John Langford, Besmira Nushi, Vibhav Vineet, Yue Wu, Safoora Yousefi
MSR-TR-2025-16 | March 2025
Publié par Microsoft
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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
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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
Publié par Microsoft
Presented at the Big Learning Workshop at the 2013 Neural Information Processing Systems Conference.
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PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing
Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro
IEEE Data Engineering Bulletin | July 2020, Vol 43(2): pp. 15-35
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Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks
Vi Hart, Divya Siddarth, Bethan Cantrell, Lila Tretikov, Peter Eckersley, John Langford, Scott Leibrand, Sham Kakade, Steve Latta, Dana Lewis, Stefano Tessaro, E. Glen Weyl
Harvard Center for Ethics | April 2020
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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
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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
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Orbiting Radiation Stars
Dean P Foster, John Langford, Gabe Perez-Giz
IOP Science | February 2016
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Doubly Robust Policy Evaluation and Optimization
Miro Dudík, Dumitru Erhan, John Langford, Lihong Li
Statistical Science | November 2014
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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, Vol 156: pp. 389-416
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Search-based Structured Prediction
Hal Daumé III, John Langford, Daniel Marcu
Machine learning Journal | July 2009, (2009)
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