John Langford

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

Venues

Active years

1998–2026

Best venue rank

Where they publish

Papers

87 indexed papers, newest first.

Year	Venue	Title	Authors
2026	ACL	EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles.	Aakriti Agrawal, Mucong Ding, Chenghao Deng, Zora Che, Arjun Rajaram, Anirudh Satheesh, Bang An, C. Bayan Bruss, John Langford, Furong Huang
2025	CHI	EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing.	Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
2025	ICLR	The Belief State Transformer.	Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
2024	ICLR	Towards Principled Representation Learning from Videos for Reinforcement Learning.	Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford
2024	ICML	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 Dudk, John Langford, Alex Lamb
2024	ICML	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 Daum III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
2023	ICML	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
2023	ICML	Streaming Active Learning with Deep Neural Networks.	Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
2022	AAAI	Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting.	Keyi Chen, John Langford, Francesco Orabona
2022	COLT	Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information.	Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022	ICLR	Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics.	Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2022	ICML	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning.	Alberto Bietti, Chen-Yu Wei, Miroslav Dudk, John Langford, Zhiwei Steven Wu
2022	ICML	Contextual Bandits with Large Action Spaces: Made Practical.	Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
2021	ICLR	Provable Rich Observation Reinforcement Learning with Combinatorial Latent States.	Dipendra Misra, Qinghua Liu, Chi Jin, John Langford
2021	ICML	ChaCha for Online AutoML.	Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021	ICML	Interaction-Grounded Learning.	Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2020	ICLR	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.	Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2020	ICML	Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning.	Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
2019	COLT	Contextual bandits with continuous actions: Smoothing, zooming, and adapting.	Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
2019	COLT	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
2019	ICML	Provably efficient RL with Rich Observations via Latent State Decoding.	Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudk, John Langford
2019	ICML	Contextual Memory Trees.	Wen Sun, Alina Beygelzimer, Hal Daum III, John Langford, Paul Mineiro
2019	ICML	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.	Chicheng Zhang, Alekh Agarwal, Hal Daum III, John Langford, Sahand Negahban
2018	COLT	Efficient Contextual Bandits in Non-stationary Worlds.	Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
2018	ICLR	Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback.	Hal Daum III, John Langford, Amr Sharaf
2018	ICML	A Reductions Approach to Fair Classification.	Alekh Agarwal, Alina Beygelzimer, Miroslav Dudk, John Langford, Hanna M. Wallach
2018	ICML	Learning Deep ResNet Blocks Sequentially using Boosting Theory.	Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
2017	COLT	Open Problem: First-Order Regret Bounds for Contextual Bandits.	Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
2017	EMNLP	Mapping Instructions and Visual Observations to Actions with Reinforcement Learning.	Dipendra Kumar Misra, John Langford, Yoav Artzi
2017	ICML	Logarithmic Time One-Against-Some.	Hal Daum III, Nikos Karampatziakis, John Langford, Paul Mineiro
2017	ICML	Contextual Decision Processes with low Bellman rank are PAC-Learnable.	Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2017	ICML	Active Learning for Cost-Sensitive Classification.	Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daum III, John Langford
2015	ICML	Learning to Search Better than Your Teacher.	Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daum III, John Langford
2015	NAACL	Hands-on Learning to Search for Structured Prediction.	Hal Daum III, John Langford, Kai-Wei Chang, He He, Sudha Rao
2014	COLT	Resourceful Contextual Bandits.	Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
2014	ICML	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits.	Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2013	UAI	Normalized Online Learning.	Stphane Ross, Paul Mineiro, John Langford
2012	AAMAS	Learning performance of prediction markets with Kelly bettors.	Alina Beygelzimer, John Langford, David M. Pennock
2012	UAI	Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits.	Miroslav Dudk, Dumitru Erhan, John Langford, Lihong Li
2011	ICML	Doubly Robust Policy Evaluation and Learning.	Miroslav Dudk, John Langford, Lihong Li
2011	UAI	Efficient Optimal Learning for Contextual Bandits.	Miroslav Dudk, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	UAI	Online Importance Weight Aware Updates.	Nikos Karampatziakis, John Langford
2011	WSDM	Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms.	Lihong Li, Wei Chu, John Langford, Xuanhui Wang
2010	COLT	Robust Efficient Conditional Probability Estimation.	John Langford
2010	WWW	A contextual-bandit approach to personalized news article recommendation.	Lihong Li, Wei Chu, John Langford, Robert E. Schapire
2009	ALT	Error-Correcting Tournaments.	Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009	ICML	Importance weighted active learning.	Alina Beygelzimer, Sanjoy Dasgupta, John Langford
2009	ICML	Tutorial summary: Reductions in machine learning.	Alina Beygelzimer, John Langford, Bianca Zadrozny
2009	ICML	Tutorial summary: Active learning.	Sanjoy Dasgupta, John Langford
2009	ICML	Learning nonlinear dynamic models.	John Langford, Ruslan Salakhutdinov, Tong Zhang
2009	ICML	Feature hashing for large scale multitask learning.	Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg
2009	KDD	The offset tree for learning with partial labels.	Alina Beygelzimer, John Langford
2009	UAI	Conditional Probability Tree Estimation Analysis and Algorithms.	Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl
2008	ICML	Exploration scavenging.	John Langford, Alexander L. Strehl, Jennifer Wortman
2007	COLT	Robust Reductions from Ranking to Classification.	Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin
2006	COLT	Continuous Experts and the Binning Algorithm.	Jacob D. Abernethy, John Langford, Manfred K. Warmuth
2006	ICML	Agnostic active learning.	Maria-Florina Balcan, Alina Beygelzimer, John Langford
2006	ICML	Cover trees for nearest neighbor.	Alina Beygelzimer, Sham M. Kakade, John Langford
2006	ICML	PAC model-free reinforcement learning.	Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
2006	KDD	Outlier detection by active learning.	Naoki Abe, Bianca Zadrozny, John Langford
2006	UAI	Predicting Conditional Quantiles via Reduction to Classification.	John Langford, Roberto Oliveira, Bianca Zadrozny
2005	AAAI	Weighted One-Against-All.	Alina Beygelzimer, John Langford, Bianca Zadrozny
2005	AISTATS	Estimating Class Membership Probabilities using Classifier Learners.	John Langford, Bianca Zadrozny
2005	COLT	The Cross Validation Problem.	John Langford
2005	COLT	Sensitive Error Correcting Output Codes.	John Langford, Alina Beygelzimer
2005	ICML	Error limiting reductions between classification tasks.	Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny
2005	ICML	A comparison of tight generalization error bounds.	Matti Kriinen, John Langford
2005	ICML	Relating reinforcement learning performance to classification performance.	John Langford, Bianca Zadrozny
2005	STOC	Covert two-party computation.	Luis von Ahn, Nicholas J. Hopper, John Langford
2004	COLT	Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification.	Peter Grnwald, John Langford
2004	KDD	An iterative method for multi-class cost-sensitive learning.	Naoki Abe, Bianca Zadrozny, John Langford
2004	KDD	An objective evaluation criterion for clustering.	Arindam Banerjee, John Langford
2003	COLT	PAC-MDL Bounds.	Avrim Blum, John Langford
2003	EuroCrypt	CAPTCHA: Using Hard AI Problems for Security.	Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford
2003	ICDM	Cost-Sensitive Learning by Cost-Proportionate Example Weighting.	Bianca Zadrozny, John Langford, Naoki Abe
2003	ICML	Exploration in Metric State Spaces.	Sham M. Kakade, Michael J. Kearns, John Langford
2002	CRYPTO	Provably Secure Steganography.	Nicholas J. Hopper, John Langford, Luis von Ahn
2002	ICML	Approximately Optimal Approximate Reinforcement Learning.	Sham M. Kakade, John Langford
2002	ICML	Combining Trainig Set and Test Set Bounds.	John Langford
2002	ICML	Competitive Analysis of the Explore/Exploit Tradeoff.	John Langford, Martin Zinkevich, Sham M. Kakade
2001	ICML	An Improved Predictive Accuracy Bound for Averaging Classifiers.	John Langford, Matthias W. Seeger, Nimrod Megiddo
2000	COLT	Computable Shell Decomposition Bounds.	John Langford, David A. McAllester
2000	ICML	FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness.	Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum
1999	COLT	Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation.	Avrim Blum, Adam Kalai, John Langford
1999	COLT	Microchoice Bounds and Self Bounding Learning Algorithms.	John Langford, Avrim Blum
1999	ICML	Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes.	Sebastian Thrun, John Langford, Dieter Fox
1998	FOCS	On Learning Monotone Boolean Functions.	Avrim Blum, Carl Burch, John Langford