John Langford

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 100

Top Venues

CoRR

ICML

NIPS

COLT

Publications

Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss. CoRR (2024)
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
Streaming Active Learning with Deep Neural Networks. CoRR (2023)
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miro Dudík, John Langford, Alex Lamb
PcLast: Discovering Plannable Continuous Latent States. CoRR (2023)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. 2023 (2023)
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
Principled Offline RL in the Presence of Rich Exogenous Information. ICML (2023)
Akanksha Saran, Jacob Alber, Danielle Bragg, Cyril Zhang, John Langford
Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing. CoRR (2023)
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
Streaming Active Learning with Deep Neural Networks. ICML (2023)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR (2022)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR (2022)
Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. CoRR (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS (2022)
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML (2022)
Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
Contextual Bandits with Large Action Spaces: Made Practical. CoRR (2022)
Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
Towards Data-Driven Offline Simulations for Online Reinforcement Learning. CoRR (2022)
Keyi Chen, John Langford, Francesco Orabona
Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. AAAI (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-inclusive Feedback. CoRR (2022)
Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad
Eigen Memory Trees. CoRR (2022)
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR (2022)
Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
Contextual Bandits with Large Action Spaces: Made Practical. ICML (2022)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT (2022)
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR (2022)
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
Interaction-Grounded Learning. CoRR (2021)
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR (2021)
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
ChaCha for Online AutoML. ICML (2021)
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
Interaction-Grounded Learning. ICML (2021)
Dipendra Misra, Qinghua Liu, Chi Jin, John Langford
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR (2021)
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
ChaCha for Online AutoML. CoRR (2021)
Alberto Bietti, Alekh Agarwal, John Langford
A Contextual Bandit Bake-off. J. Mach. Learn. Res. 22 (2021)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR (2020)
Nikos Karampatziakis, John Langford, Paul Mineiro
Empirical Likelihood for Contextual Bandits. NeurIPS (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. CoRR (2020)
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
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication. CoRR (2020)
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR (2020)
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. NeurIPS (2020)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21 (2020)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS (2020)
Alekh Agarwal, John Langford, Chen-Yu Wei
Federated Residual Learning. CoRR (2020)
Justin Chan, Dean P. Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham M. Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Sudheesh Singanamalla, Jacob E. Sunshine, Stefano Tessaro
PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing. CoRR (2020)
Justin Chan, Landon P. Cox, Dean P. Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham M. Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob E. Sunshine, Stefano Tessaro
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing. IEEE Data Eng. Bull. 43 (2) (2020)
Keyi Chen, John Langford, Francesco Orabona
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting. CoRR (2020)
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR (2019)
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Efficient Forward Architecture Search. CoRR (2019)
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
Contextual Memory Trees. ICML (2019)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT (2019)
Nikos Karampatziakis, John Langford, Paul Mineiro
Empirical Likelihood for Contextual Bandits. CoRR (2019)
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20 (2019)
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
Provably efficient RL with Rich Observations via Latent State Decoding. ICML (2019)
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR (2019)
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT (2019)
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Efficient Forward Architecture Search. NeurIPS (2019)
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR (2019)
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
Provably efficient RL with Rich Observations via Latent State Decoding. CoRR (2019)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. CoRR (2019)
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML (2019)
Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML (2018)
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS (2018)
Alberto Bietti, Alekh Agarwal, John Langford
Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR (2018)
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR (2018)
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR (2018)
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
A Reductions Approach to Fair Classification. ICML (2018)
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
A Reductions Approach to Fair Classification. CoRR (2018)
Hal Daumé III, John Langford, Amr Sharaf
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback. ICLR (Poster) (2018)
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
Contextual Memory Trees. CoRR (2018)
Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
Efficient Contextual Bandits in Non-stationary Worlds. COLT (2018)
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
Active Learning for Cost-Sensitive Classification. ICML (2017)
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML (2017)
Dipendra Kumar Misra, John Langford, Yoav Artzi
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. CoRR (2017)
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
Logarithmic Time One-Against-Some. ICML (2017)
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT (2017)
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
Active Learning for Cost-Sensitive Classification. CoRR (2017)
Dipendra Kumar Misra, John Langford, Yoav Artzi
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. EMNLP (2017)
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
Off-policy evaluation for slate recommendation. NIPS (2017)
Haipeng Luo, Alekh Agarwal, John Langford
Efficient Contextual Bandits in Non-stationary Worlds. CoRR (2017)
John Langford
Contextual reinforcement learning. IEEE BigData (2017)
Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR (2017)
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins
A Multiworld Testing Decision Service. CoRR (2016)
Akshay Krishnamurthy, Alekh Agarwal, John Langford
PAC Reinforcement Learning with Rich Observations. NIPS (2016)
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR (2016)
Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
Efficient Second Order Online Learning by Sketching. NIPS (2016)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
Search Improves Label for Active Learning. NIPS (2016)
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
Off-policy evaluation for slate recommendation. CoRR (2016)
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
Learning Reductions That Really Work. Proc. IEEE 104 (1) (2016)
Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
Logarithmic Time One-Against-Some. CoRR (2016)
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR (2016)
John Langford, Mark Guzdial
The solution to AI, what real researchers do, and expectations for CS classrooms. Commun. ACM 59 (6) (2016)
Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford
A Credit Assignment Compiler for Joint Prediction. NIPS (2016)
Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
Efficient Second Order Online Learning via Sketching. CoRR (2016)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
Search Improves Label for Active Learning. CoRR (2016)
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Efficient and Parsimonious Agnostic Active Learning. NIPS (2015)
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
Learning to Search Better than Your Teacher. ICML (2015)
Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock
An axiomatic characterization of wagering mechanisms. J. Econ. Theory 156 (2015)
Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
Learning Reductions that Really Work. CoRR (2015)
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Efficient and Parsimonious Agnostic Active Learning. CoRR (2015)
Kai-Wei Chang, He He, Hal Daumé III, John Langford
Learning to Search for Dependencies. CoRR (2015)
Hal Daumé III, John Langford, Kai-Wei Chang, He He, Sudha Rao
Hands-on Learning to Search for Structured Prediction. HLT-NAACL (2015)
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
Learning to Search Better Than Your Teacher. CoRR (2015)
Anna Choromanska, John Langford
Logarithmic Time Online Multiclass prediction. NIPS (2015)
Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
Doubly Robust Policy Evaluation and Optimization. CoRR (2015)
John Langford, Mark Guzdial
The arbitrariness of reviews, and advice for school administrators. Commun. ACM 58 (4) (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

John Langford