Langford, John

117 publications

ICLR 2025 The Belief State Transformer Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford

NeurIPS 2024 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, Anima Anandkumar, Furong Huang

NeurIPSW 2024 EnsemW2S: Can an Ensemble of LLMs Be Leveraged to Obtain a Stronger LLM? Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang

ICML 2024 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 P Molu, Miroslav Dudı́k, John Langford, Alex Lamb

ICML 2024 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

ICLR 2024 Towards Principled Representation Learning from Videos for Reinforcement Learning Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford

NeurIPSW 2024 Towards Principled Representation Learning from Videos for Reinforcement Learning Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford

NeurIPSW 2023 $\texttt{PREMIER-TACO}$ 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 Basu, Furong Huang

TMLR 2023 Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J Foster, Lekan P Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

ICML 2023 Principled Offline RL in the Presence of Rich Exogenous Information Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm Van Seijen, Remi Tachet Des Combes, John Langford

ICML 2023 Streaming Active Learning with Deep Neural Networks Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash

NeurIPSW 2022 Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information Riashat Islam, Manan Tomar, Alex Lamb, Hongyu Zang, Yonathan Efroni, Dipendra Misra, Aniket Rajiv Didolkar, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford

AAAI 2022 Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting Keyi Chen, John Langford, Francesco Orabona

ICML 2022 Contextual Bandits with Large Action Spaces: Made Practical Yinglun Zhu, Dylan J Foster, John Langford, Paul Mineiro

NeurIPS 2022 Interaction-Grounded Learning with Action-Inclusive Feedback Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

ICML 2022 Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, Steven Wu

ICLR 2022 Provably Filtering Exogenous Distractors Using Multistep Inverse Dynamics Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

COLT 2022 Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Yonathan Efroni, Dylan J Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford

NeurIPSW 2022 Towards Data-Driven Offline Simulations for Online Reinforcement Learning Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman

JMLR 2021 A Contextual Bandit Bake-Off Alberto Bietti, Alekh Agarwal, John Langford

ICML 2021 ChaCha for Online AutoML Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi

ICML 2021 Interaction-Grounded Learning Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad

ICLR 2021 Provable Rich Observation Reinforcement Learning with Combinatorial Latent States Dipendra Misra, Qinghua Liu, Chi Jin, John Langford

JMLR 2020 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

ICLR 2020 Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal

NeurIPS 2020 Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

NeurIPS 2020 Empirical Likelihood for Contextual Bandits Nikos Karampatziakis, John Langford, Paul Mineiro

ICML 2020 Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford

NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

JMLR 2019 Active Learning for Cost-Sensitive Classification Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé Iii, John Langford

COLT 2019 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

ICML 2019 Contextual Memory Trees Wen Sun, Alina Beygelzimer, Hal Daumé Iii, John Langford, Paul Mineiro

NeurIPS 2019 Efficient Forward Architecture Search Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric J Horvitz, Debadeepta Dey

COLT 2019 Model-Based RL in Contextual Decision Processes: PAC Bounds and Exponential Improvements over Model-Free Approaches Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

ICML 2019 Provably Efficient RL with Rich Observations via Latent State Decoding Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford

ICML 2019 Warm-Starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback Chicheng Zhang, Alekh Agarwal, Hal Daumé Iii, John Langford, Sahand Negahban

ICML 2018 A Reductions Approach to Fair Classification Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach

COLT 2018 Efficient Contextual Bandits in Non-Stationary Worlds Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford

ICML 2018 Learning Deep ResNet Blocks Sequentially Using Boosting Theory Furong Huang, Jordan Ash, John Langford, Robert Schapire

NeurIPS 2018 On Oracle-Efficient PAC RL with Rich Observations Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

ICLR 2018 Residual Loss Prediction: Reinforcement Learning with No Incremental Feedback Hal Daumé Iii, John Langford, Amr Sharaf

ICML 2017 Active Learning for Cost-Sensitive Classification Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé, John Langford

ICML 2017 Contextual Decision Processes with Low Bellman Rank Are PAC-Learnable Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

ICML 2017 Logarithmic Time One-Against-Some Hal Daumé, Nikos Karampatziakis, John Langford, Paul Mineiro

NeurIPS 2017 Off-Policy Evaluation for Slate Recommendation Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni

COLT 2017 Open Problem: First-Order Regret Bounds for Contextual Bandits Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire

NeurIPS 2016 A Credit Assignment Compiler for Joint Prediction Kai-Wei Chang, He He, Stephane Ross, Hal Daume Iii, John Langford

NeurIPS 2016 Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford

NeurIPS 2016 PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford

NeurIPS 2016 Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang

NeurIPS 2015 Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire

ICML 2015 Learning to Search Better than Your Teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé, John Langford

NeurIPS 2015 Logarithmic Time Online Multiclass Prediction Anna E Choromanska, John Langford

JMLR 2014 A Reliable Effective Terascale Linear Learning System Alekh Agarwal, Oliveier Chapelle, Miroslav Dudík, John Langford

COLT 2014 Resourceful Contextual Bandits Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins

NeurIPS 2014 Scalable Non-Linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J Telgarsky

ICML 2014 Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire

UAI 2013 Normalized Online Learning Stéphane Ross, Paul Mineiro, John Langford

AISTATS 2012 Contextual Bandit Learning with Predictable Rewards Alekh Agarwal, Miroslav Dudik, Satyen Kale, John Langford, Robert Schapire

UAI 2012 Sample-Efficient Nonstationary Policy Evaluation for Contextual Bandits Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li

AISTATS 2011 Contextual Bandit Algorithms with Supervised Learning Guarantees Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert Schapire

ICML 2011 Doubly Robust Policy Evaluation and Learning Miroslav Dudík, John Langford, Lihong Li

UAI 2011 Efficient Optimal Learning for Contextual Bandits Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang

UAI 2011 Online Importance Weight Aware Updates Nikos Karampatziakis, John Langford

NeurIPS 2010 Agnostic Active Learning Without Constraints Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang

NeurIPS 2010 Learning from Logged Implicit Exploration Data Alex Strehl, John Langford, Lihong Li, Sham M. Kakade

COLT 2010 Robust Efficient Conditional Probability Estimation John Langford

UAI 2009 Conditional Probability Tree Estimation Analysis and Algorithms Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl

ALT 2009 Error-Correcting Tournaments Alina Beygelzimer, John Langford, Pradeep Ravikumar

ICML 2009 Feature Hashing for Large Scale Multitask Learning Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg

AISTATS 2009 Hash Kernels Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, S. V. N. Vishwanathan

JMLR 2009 Hash Kernels for Structured Data Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan

ICML 2009 Importance Weighted Active Learning Alina Beygelzimer, Sanjoy Dasgupta, John Langford

ICML 2009 Learning Nonlinear Dynamic Models John Langford, Ruslan Salakhutdinov, Tong Zhang

NeurIPS 2009 Multi-Label Prediction via Compressed Sensing Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang

MLJ 2009 Search-Based Structured Prediction Hal Daumé Iii, John Langford, Daniel Marcu

NeurIPS 2009 Slow Learners Are Fast Martin Zinkevich, John Langford, Alex J. Smola

JMLR 2009 Sparse Online Learning via Truncated Gradient John Langford, Lihong Li, Tong Zhang

ICML 2009 Tutorial Summary: Active Learning Sanjoy Dasgupta, John Langford

ICML 2009 Tutorial Summary: Reductions in Machine Learning Alina Beygelzimer, John Langford, Bianca Zadrozny

ICML 2008 Exploration Scavenging John Langford, Alexander L. Strehl, Jennifer Wortman

NeurIPS 2008 Predictive Indexing for Fast Search Sharad Goel, John Langford, Alexander L. Strehl

MLJ 2008 Robust Reductions from Ranking to Classification Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin

NeurIPS 2008 Sparse Online Learning via Truncated Gradient John Langford, Lihong Li, Tong Zhang

COLT 2007 Robust Reductions from Ranking to Classification Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin

MLJ 2007 Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification Peter Grünwald, John Langford

NeurIPS 2007 The Epoch-Greedy Algorithm for Multi-Armed Bandits with Side Information John Langford, Tong Zhang

ICML 2006 Agnostic Active Learning Maria-Florina Balcan, Alina Beygelzimer, John Langford

COLT 2006 Continuous Experts and the Binning Algorithm Jacob D. Abernethy, John Langford, Manfred K. Warmuth

ICML 2006 Cover Trees for Nearest Neighbor Alina Beygelzimer, Sham M. Kakade, John Langford

ICML 2006 PAC Model-Free Reinforcement Learning Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman

UAI 2006 Predicting Conditional Quantiles via Reduction to Classification John Langford, Roberto Oliveira, Bianca Zadrozny

ICML 2005 A Comparison of Tight Generalization Error Bounds Matti Kääriäinen, John Langford

ICML 2005 Error Limiting Reductions Between Classification Tasks Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny

AISTATS 2005 Estimating Class Membership Probabilities Using Classifier Learners John Langford, Bianca Zadrozny

ICML 2005 Relating Reinforcement Learning Performance to Classification Performance John Langford, Bianca Zadrozny

COLT 2005 Sensitive Error Correcting Output Codes John Langford, Alina Beygelzimer

COLT 2005 The Cross Validation Problem John Langford

JMLR 2005 Tutorial on Practical Prediction Theory for Classification John Langford

AAAI 2005 Weighted One-Against-All Alina Beygelzimer, John Langford, Bianca Zadrozny

JMLR 2004 Computable Shell Decomposition Bounds John Langford, David McAllester

COLT 2004 Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification Peter Grünwald, John Langford

ICML 2003 Exploration in Metric State Spaces Sham M. Kakade, Michael J. Kearns, John Langford

MLJ 2003 Microchoice Bounds and Self Bounding Learning Algorithms John Langford, Avrim Blum

COLT 2003 PAC-MDL Bounds Avrim Blum, John Langford

ICML 2002 Approximately Optimal Approximate Reinforcement Learning Sham M. Kakade, John Langford

ICML 2002 Combining Trainig Set and Test Set Bounds John Langford

ICML 2002 Competitive Analysis of the Explore/Exploit Tradeoff John Langford, Martin Zinkevich, Sham M. Kakade

NeurIPS 2002 PAC-Bayes & Margins John Langford, John Shawe-Taylor

NeurIPS 2001 (Not) Bounding the True Error John Langford, Rich Caruana

ICML 2001 An Improved Predictive Accuracy Bound for Averaging Classifiers John Langford, Matthias W. Seeger, Nimrod Megiddo

NeurIPS 2001 Risk Sensitive Particle Filters Sebastian Thrun, John Langford, Vandi Verma

COLT 2000 Computable Shell Decomposition Bounds John Langford, David A. McAllester

ICML 2000 FeatureBoost: A Meta-Learning Algorithm That Improves Model Robustness Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum

COLT 1999 Beating the Hold-Out: Bounds for K-Fold and Progressive Cross-Validation Avrim Blum, Adam Kalai, John Langford

COLT 1999 Microchoice Bounds and Self Bounding Learning Algorithms John Langford, Avrim Blum

ICML 1999 Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes Sebastian Thrun, John Langford, Dieter Fox