John Langford - RankMe

247 papers A* 82A 12B 1Journal 125Unranked 25

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Phi-4-reasoning-vision-15B Technical Report. CoRR Jyoti Aneja, Michael Harrison, Neel Joshi, Tyler LaBonte, John Langford, Eduardo Salinas
2026	J	`jnl`	When does predictive inverse dynamics outperform behavior cloning? CoRR Lukas Schäfer, Pallavi Choudhury, Abdelhak Lemkhenter, Chris Lovett, Somjit Nath, Luis França, Matheus Ribeiro Furtado de Mendonca, Alex Lamb, Riashat Islam, Siddhartha Sen, John Langford, Katja Hofmann, Sergio Valcarcel Macua
2025	J	`jnl`	Dion2: A Simple Method to Shrink Matrix in Muon. CoRR Kwangjun Ahn, Noah Amsel, John Langford
2025	J	`jnl`	Efficient Joint Prediction of Multiple Future Tokens. CoRR Kwangjun Ahn, Alex Lamb, John Langford
2025	—	`conf`	EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing. CHI Extended Abstracts Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford
2025	J	`jnl`	Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead. CoRR Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg, Neel Joshi, Yash Lara, John Langford, Besmira Nushi, Vibhav Vineet, Yue Wu, Safoora Yousefi
2025	J	`jnl`	Next-Latent Prediction Transformers Learn Compact World Models. CoRR Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward S. Hu, Pratyusha Sharma, Riashat Islam, Alex Lamb, John Langford
2025	A*	`conf`	The Belief State Transformer. ICLR Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
2024	A*	`conf`	Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. NeurIPS Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Animashree Anandkumar, Furong Huang
2024	J	`jnl`	Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang
2024	J	`jnl`	EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM? CoRR Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang
2024	J	`jnl`	Learning to Achieve Goals with Belief State Transformers. CoRR Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford
2024	A*	`conf`	PcLast: Discovering Plannable Continuous Latent States. ICML Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miroslav Dudík, John Langford, Alex Lamb
2024	J	`jnl`	Position Paper: Agent AI Towards a Holistic Intelligence. CoRR 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, Li Fei-Fei, Katsushi Ikeuchi, Jianfeng Gao
2024	A*	`conf`	Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss. ICML Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
2024	J	`jnl`	Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss. CoRR Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
2024	A*	`conf`	Towards Principled Representation Learning from Videos for Reinforcement Learning. ICLR Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford
2024	J	`jnl`	Towards Principled Representation Learning from Videos for Reinforcement Learning. CoRR Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford
2024	J	`jnl`	Video Occupancy Models. CoRR Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine
2023	J	`jnl`	Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing. CoRR Akanksha Saran, Jacob Alber, Danielle Bragg, Cyril Zhang, John Langford
2023	J	`jnl`	Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
2023	J	`jnl`	PcLast: Discovering Plannable Continuous Latent States. CoRR 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
2023	A*	`conf`	Principled Offline RL in the Presence of Rich Exogenous Information. ICML 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	A*	`conf`	Streaming Active Learning with Deep Neural Networks. ICML Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
2023	J	`jnl`	Streaming Active Learning with Deep Neural Networks. CoRR Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
2022	J	`jnl`	Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. CoRR 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
2022	A*	`conf`	Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. AAAI Keyi Chen, John Langford, Francesco Orabona
2022	A*	`conf`	Contextual Bandits with Large Action Spaces: Made Practical. ICML Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
2022	J	`jnl`	Contextual Bandits with Large Action Spaces: Made Practical. CoRR Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
2022	J	`jnl`	Eigen Memory Trees. CoRR Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad
2022	J	`jnl`	Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
2022	A*	`conf`	Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
2022	J	`jnl`	Interaction-Grounded Learning with Action-inclusive Feedback. CoRR Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
2022	A*	`conf`	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
2022	J	`jnl`	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
2022	A*	`conf`	Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2022	A*	`conf`	Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022	J	`jnl`	Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022	J	`jnl`	Towards Data-Driven Offline Simulations for Online Reinforcement Learning. CoRR Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman
2021	J	`jnl`	A Contextual Bandit Bake-off. J. Mach. Learn. Res. Alberto Bietti, Alekh Agarwal, John Langford
2021	A*	`conf`	ChaCha for Online AutoML. ICML Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021	J	`jnl`	ChaCha for Online AutoML. CoRR Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi
2021	A*	`conf`	Interaction-Grounded Learning. ICML Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021	J	`jnl`	Interaction-Grounded Learning. CoRR Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
2021	J	`jnl`	Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2021	A*	`conf`	Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR Dipendra Misra, Qinghua Liu, Chi Jin, John Langford
2020	J	`jnl`	Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting. CoRR Keyi Chen, John Langford, Francesco Orabona
2020	J	`jnl`	Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
2020	A*	`conf`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2020	A*	`conf`	Efficient Contextual Bandits with Continuous Actions. NeurIPS Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
2020	J	`jnl`	Efficient Contextual Bandits with Continuous Actions. CoRR Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
2020	A*	`conf`	Empirical Likelihood for Contextual Bandits. NeurIPS Nikos Karampatziakis, John Langford, Paul Mineiro
2020	J	`jnl`	Federated Residual Learning. CoRR Alekh Agarwal, John Langford, Chen-Yu Wei
2020	A*	`conf`	Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
2020	A*	`conf`	Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
2020	J	`jnl`	Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
2020	J	`jnl`	PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing. CoRR 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
2020	J	`jnl`	PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing. IEEE Data Eng. Bull. 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
2020	J	`jnl`	Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication. CoRR 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
2019	J	`jnl`	Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2019	J	`jnl`	Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. CoRR Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
2019	A*	`conf`	Contextual Memory Trees. ICML Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
2019	A*	`conf`	Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
2019	J	`jnl`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2019	A*	`conf`	Efficient Forward Architecture Search. NeurIPS Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
2019	J	`jnl`	Efficient Forward Architecture Search. CoRR Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
2019	J	`jnl`	Empirical Likelihood for Contextual Bandits. CoRR Nikos Karampatziakis, John Langford, Paul Mineiro
2019	J	`jnl`	Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
2019	A*	`conf`	Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2019	A*	`conf`	Provably efficient RL with Rich Observations via Latent State Decoding. ICML Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	J	`jnl`	Provably efficient RL with Rich Observations via Latent State Decoding. CoRR Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	A*	`conf`	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban
2019	J	`jnl`	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban
2018	A*	`conf`	A Reductions Approach to Fair Classification. ICML Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
2018	J	`jnl`	A Reductions Approach to Fair Classification. CoRR Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
2018	J	`jnl`	Contextual Memory Trees. CoRR Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
2018	A*	`conf`	Efficient Contextual Bandits in Non-stationary Worlds. COLT Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
2018	A*	`conf`	Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
2018	J	`jnl`	Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2018	A*	`conf`	On Oracle-Efficient PAC RL with Rich Observations. NeurIPS Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	J	`jnl`	On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	J	`jnl`	Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR Alberto Bietti, Alekh Agarwal, John Langford
2018	—	`conf`	Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback. ICLR (Poster) Hal Daumé III, John Langford, Amr Sharaf
2017	A*	`conf`	Active Learning for Cost-Sensitive Classification. ICML Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2017	J	`jnl`	Active Learning for Cost-Sensitive Classification. CoRR Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2017	A*	`conf`	Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2017	—	`conf`	Contextual reinforcement learning. IEEE BigData John Langford
2017	J	`jnl`	Efficient Contextual Bandits in Non-stationary Worlds. CoRR Haipeng Luo, Alekh Agarwal, John Langford
2017	—	`ch.`	Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining John Langford
2017	J	`jnl`	Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
2017	A*	`conf`	Logarithmic Time One-Against-Some. ICML Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
2017	A*	`conf`	Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. EMNLP Dipendra Kumar Misra, John Langford, Yoav Artzi
2017	J	`jnl`	Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. CoRR Dipendra Kumar Misra, John Langford, Yoav Artzi
2017	—	`conf`	Off-policy evaluation for slate recommendation. NIPS Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
2017	A*	`conf`	Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
2016	—	`conf`	A Credit Assignment Compiler for Joint Prediction. NIPS Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford
2016	J	`jnl`	A Multiworld Testing Decision Service. CoRR Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, I. Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins
2016	J	`jnl`	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2016	J	`jnl`	Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR Akshay Krishnamurthy, Alekh Agarwal, John Langford
2016	—	`conf`	Efficient Second Order Online Learning by Sketching. NIPS Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
2016	J	`jnl`	Efficient Second Order Online Learning via Sketching. CoRR Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
2016	J	`jnl`	Learning Reductions That Really Work. Proc. IEEE Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
2016	J	`jnl`	Logarithmic Time One-Against-Some. CoRR Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro
2016	J	`jnl`	Off-policy evaluation for slate recommendation. CoRR Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
2016	—	`conf`	PAC Reinforcement Learning with Rich Observations. NIPS Akshay Krishnamurthy, Alekh Agarwal, John Langford
2016	—	`conf`	Search Improves Label for Active Learning. NIPS Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
2016	J	`jnl`	Search Improves Label for Active Learning. CoRR Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
2016	J	`jnl`	The solution to AI, what real researchers do, and expectations for CS classrooms. Commun. ACM John Langford, Mark Guzdial
2015	J	`jnl`	An axiomatic characterization of wagering mechanisms. J. Econ. Theory Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock
2015	J	`jnl`	Doubly Robust Policy Evaluation and Optimization. CoRR Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2015	—	`conf`	Efficient and Parsimonious Agnostic Active Learning. NIPS Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	J	`jnl`	Efficient and Parsimonious Agnostic Active Learning. CoRR Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	—	`conf`	Hands-on Learning to Search for Structured Prediction. HLT-NAACL Hal Daumé III, John Langford, Kai-Wei Chang, He He, Sudha Rao
2015	J	`jnl`	Learning Reductions that Really Work. CoRR Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro
2015	J	`jnl`	Learning to Search Better Than Your Teacher. CoRR Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
2015	A*	`conf`	Learning to Search Better than Your Teacher. ICML Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
2015	J	`jnl`	Learning to Search for Dependencies. CoRR Kai-Wei Chang, He He, Hal Daumé III, John Langford
2015	—	`conf`	Logarithmic Time Online Multiclass prediction. NIPS Anna Choromanska, John Langford
2015	J	`jnl`	The arbitrariness of reviews, and advice for school administrators. Commun. ACM John Langford, Mark Guzdial
2014	J	`jnl`	A reliable effective terascale linear learning system. J. Mach. Learn. Res. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
2014	J	`jnl`	Conditional Probability Tree Estimation Analysis and Algorithms. CoRR Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl
2014	J	`jnl`	Efficient programmable learning to search. CoRR Hal Daumé III, John Langford, Stéphane Ross
2014	J	`jnl`	Finding a research job, and teaching CS in high school. Commun. ACM John Langford, Mark Guzdial
2014	J	`jnl`	Logarithmic Time Online Multiclass prediction. CoRR Anna Choromanska, John Langford
2014	J	`jnl`	Normalized Online Learning. CoRR Stéphane Ross, Paul Mineiro, John Langford
2014	A*	`conf`	Resourceful Contextual Bandits. COLT Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
2014	J	`jnl`	Resourceful Contextual Bandits. CoRR Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
2014	—	`conf`	Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	J	`jnl`	Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	A*	`conf`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2013	J	`jnl`	Efficient Online Bootstrapping for Large Scale Learning. CoRR Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford
2013	J	`jnl`	Normalized Online Learning CoRR Stéphane Ross, Paul Mineiro, John Langford
2013	A	`conf`	Normalized Online Learning. UAI Stéphane Ross, Paul Mineiro, John Langford
2013	J	`jnl`	Para-active learning. CoRR Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford
2012	—	`conf`	Bandits with Generalized Linear Models. ICML On-line Trading of Exploration and Exploitation Lihong Li, Wei Chu, John Langford, Taesup Moon, Xuanhui Wang
2012	J	`jnl`	Cloud control: voluntary admission control for intranet traffic management. Inf. Syst. E Bus. Manag. John Langford, Lihong Li, R. Preston McAfee, Kishore Papineni
2012	J	`jnl`	Contextual Bandit Learning with Predictable Rewards CoRR Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	A	`conf`	Contextual Bandit Learning with Predictable Rewards. AISTATS Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	J	`jnl`	Learning Performance of Prediction Markets with Kelly Bettors CoRR Alina Beygelzimer, John Langford, David M. Pennock
2012	A	`conf`	Learning performance of prediction markets with Kelly bettors. AAMAS Alina Beygelzimer, John Langford, David M. Pennock
2012	J	`jnl`	Machine learning and algorithms; agile development. Commun. ACM John Langford, Ruben Ortega
2012	J	`jnl`	Parallel machine learning on big data. XRDS John Langford
2012	J	`jnl`	Predicting Conditional Quantiles via Reduction to Classification. CoRR John Langford, Roberto Oliveira, Bianca Zadrozny
2012	J	`jnl`	Proceedings of the 29th International Conference on Machine Learning (ICML-12) CoRR John Langford, Joelle Pineau
2012	J	`jnl`	Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits CoRR Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2012	A	`conf`	Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2011	J	`jnl`	A Reliable Effective Terascale Linear Learning System CoRR Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
2011	J	`jnl`	Conferences and video lectures; scientific educational games. Commun. ACM John Langford, Judy Robertson
2011	A	`conf`	Contextual Bandit Algorithms with Supervised Learning Guarantees. AISTATS Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
2011	J	`jnl`	Doubly Robust Policy Evaluation and Learning CoRR Miroslav Dudík, John Langford, Lihong Li
2011	A*	`conf`	Doubly Robust Policy Evaluation and Learning. ICML Miroslav Dudík, John Langford, Lihong Li
2011	J	`jnl`	Efficient Optimal Learning for Contextual Bandits CoRR Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	A	`conf`	Efficient Optimal Learning for Contextual Bandits. UAI Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	A	`conf`	Online Importance Weight Aware Updates. UAI Nikos Karampatziakis, John Langford
2011	J	`jnl`	Parallel Online Learning CoRR Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola
2011	A	`conf`	Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. WSDM Lihong Li, Wei Chu, John Langford, Xuanhui Wang
2010	J	`jnl`	A Contextual-Bandit Approach to Personalized News Article Recommendation CoRR Lihong Li, Wei Chu, John Langford, Robert E. Schapire
2010	A*	`conf`	A contextual-bandit approach to personalized news article recommendation. WWW Lihong Li, Wei Chu, John Langford, Robert E. Schapire
2010	J	`jnl`	Agnostic Active Learning Without Constraints CoRR Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
2010	—	`conf`	Agnostic Active Learning Without Constraints. NIPS Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
2010	J	`jnl`	An Optimal High Probability Algorithm for the Contextual Bandit Problem CoRR Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
2010	J	`jnl`	An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms CoRR Lihong Li, Wei Chu, John Langford
2010	—	`ch.`	Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning John Langford
2010	J	`jnl`	Importance Weight Aware Gradient Updates CoRR Nikos Karampatziakis, John Langford
2010	J	`jnl`	Learning from Logged Implicit Exploration Data CoRR Alexander L. Strehl, John Langford, Sham M. Kakade
2010	—	`conf`	Learning from Logged Implicit Exploration Data. NIPS Alexander L. Strehl, John Langford, Lihong Li, Sham M. Kakade
2010	J	`jnl`	Maintaining Equilibria During Exploration in Sponsored Search Auctions. Algorithmica John Langford, Lihong Li, Yevgeniy Vorobeychik, Jennifer Wortman
2010	A*	`conf`	Robust Efficient Conditional Probability Estimation. COLT John Langford
2009	J	`jnl`	Agnostic active learning. J. Comput. Syst. Sci. Maria-Florina Balcan, Alina Beygelzimer, John Langford
2009	J	`jnl`	Conditional Probability Tree Estimation Analysis and Algorithms CoRR Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl
2009	A	`conf`	Conditional Probability Tree Estimation Analysis and Algorithms. UAI Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl
2009	J	`jnl`	Error-Correcting Tournaments CoRR Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009	B	`conf`	Error-Correcting Tournaments. ALT Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009	J	`jnl`	Feature Hashing for Large Scale Multitask Learning CoRR Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alexander J. Smola
2009	A*	`conf`	Feature hashing for large scale multitask learning. ICML Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg
2009	J	`jnl`	Hash Kernels for Structured Data. J. Mach. Learn. Res. Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan
2009	A	`conf`	Hash Kernels. AISTATS Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan
2009	A*	`conf`	Importance weighted active learning. ICML Alina Beygelzimer, Sanjoy Dasgupta, John Langford
2009	J	`jnl`	Learning Nonlinear Dynamic Models CoRR John Langford, Ruslan Salakhutdinov, Tong Zhang
2009	A*	`conf`	Learning nonlinear dynamic models. ICML John Langford, Ruslan Salakhutdinov, Tong Zhang
2009	J	`jnl`	Multi-Label Prediction via Compressed Sensing CoRR Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
2009	—	`conf`	Multi-Label Prediction via Compressed Sensing. NIPS Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
2009	J	`jnl`	Provably Secure Steganography. IEEE Trans. Computers Nicholas J. Hopper, Luis von Ahn, John Langford
2009	J	`jnl`	Search-based Structured Prediction CoRR Hal Daumé III, John Langford, Daniel Marcu
2009	J	`jnl`	Search-based structured prediction. Mach. Learn. Hal Daumé III, John Langford, Daniel Marcu
2009	—	`conf`	Slow Learners are Fast. NIPS Martin Zinkevich, Alexander J. Smola, John Langford
2009	J	`jnl`	Sparse Online Learning via Truncated Gradient. J. Mach. Learn. Res. John Langford, Lihong Li, Tong Zhang
2009	A*	`conf`	The offset tree for learning with partial labels. KDD Alina Beygelzimer, John Langford
2009	A*	`conf`	Tutorial summary: Active learning. ICML Sanjoy Dasgupta, John Langford
2009	A*	`conf`	Tutorial summary: Reductions in machine learning. ICML Alina Beygelzimer, John Langford, Bianca Zadrozny
2008	A*	`conf`	Exploration scavenging. ICML John Langford, Alexander L. Strehl, Jennifer Wortman
2008	J	`jnl`	Importance Weighted Active Learning CoRR Alina Beygelzimer, Sanjoy Dasgupta, John Langford
2008	—	`conf`	Predictive Indexing for Fast Search. NIPS Sharad Goel, John Langford, Alexander L. Strehl
2008	J	`jnl`	Robust reductions from ranking to classification. Mach. Learn. Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin
2008	A*	`conf`	Self-financed wagering mechanisms for forecasting. EC Nicolas S. Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock
2008	J	`jnl`	Sparse Online Learning via Truncated Gradient CoRR John Langford, Lihong Li, Tong Zhang
2008	—	`conf`	Sparse Online Learning via Truncated Gradient. NIPS John Langford, Lihong Li, Tong Zhang
2008	J	`jnl`	The Offset Tree for Learning with Partial Labels CoRR Alina Beygelzimer, John Langford
2007	—	`conf`	Maintaining Equilibria During Exploration in Sponsored Search Auctions. WINE Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, John Langford
2007	A*	`conf`	Robust Reductions from Ranking to Classification. COLT Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin
2007	J	`jnl`	Suboptimal behavior of Bayes and MDL in classification under misspecification. Mach. Learn. Peter Grünwald, John Langford
2007	—	`conf`	The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS John Langford, Tong Zhang
2006	A*	`conf`	Agnostic active learning. ICML Maria-Florina Balcan, Alina Beygelzimer, John Langford
2006	A*	`conf`	Continuous Experts and the Binning Algorithm. COLT Jacob D. Abernethy, John Langford, Manfred K. Warmuth
2006	A*	`conf`	Cover trees for nearest neighbor. ICML Alina Beygelzimer, Sham M. Kakade, John Langford
2006	A*	`conf`	Outlier detection by active learning. KDD Naoki Abe, Bianca Zadrozny, John Langford
2006	A*	`conf`	PAC model-free reinforcement learning. ICML Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
2006	A	`conf`	Predicting Conditional Quantiles via Reduction to Classification. UAI John Langford, Roberto Oliveira, Bianca Zadrozny
2005	A*	`conf`	A comparison of tight generalization error bounds. ICML Matti Kääriäinen, John Langford
2005	A*	`conf`	Covert two-party computation. STOC Luis von Ahn, Nicholas J. Hopper, John Langford
2005	A*	`conf`	Error limiting reductions between classification tasks. ICML Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny
2005	A	`conf`	Estimating Class Membership Probabilities using Classifier Learners. AISTATS John Langford, Bianca Zadrozny
2005	A*	`conf`	Relating reinforcement learning performance to classification performance. ICML John Langford, Bianca Zadrozny
2005	A*	`conf`	Sensitive Error Correcting Output Codes. COLT John Langford, Alina Beygelzimer
2005	A*	`conf`	The Cross Validation Problem. COLT John Langford
2005	J	`jnl`	Tutorial on Practical Prediction Theory for Classification. J. Mach. Learn. Res. John Langford
2005	A*	`conf`	Weighted One-Against-All. AAAI Alina Beygelzimer, John Langford, Bianca Zadrozny
2004	A*	`conf`	An iterative method for multi-class cost-sensitive learning. KDD Naoki Abe, Bianca Zadrozny, John Langford
2004	A*	`conf`	An objective evaluation criterion for clustering. KDD Arindam Banerjee, John Langford
2004	J	`jnl`	Computable Shell Decomposition Bounds. J. Mach. Learn. Res. John Langford, David A. McAllester
2004	J	`jnl`	Reductions Between Classification Tasks Electron. Colloquium Comput. Complex. Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford
2004	A*	`conf`	Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. COLT Peter Grünwald, John Langford
2004	J	`jnl`	Suboptimal behaviour of Bayes and MDL in classification under misspecification CoRR Peter Grünwald, John Langford
2004	J	`jnl`	Telling humans and computers apart automatically. Commun. ACM Luis von Ahn, Manuel Blum, John Langford
2003	A*	`conf`	CAPTCHA: Using Hard AI Problems for Security. EUROCRYPT Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford
2003	A*	`conf`	Correlated equilibria in graphical games. EC Sham M. Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz
2003	A*	`conf`	Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM Bianca Zadrozny, John Langford, Naoki Abe
2003	A*	`conf`	Exploration in Metric State Spaces. ICML Sham M. Kakade, Michael J. Kearns, John Langford
2003	J	`jnl`	Microchoice Bounds and Self Bounding Learning Algorithms. Mach. Learn. John Langford, Avrim Blum
2003	A*	`conf`	PAC-MDL Bounds. COLT Avrim Blum, John Langford
2002	A*	`conf`	Approximately Optimal Approximate Reinforcement Learning. ICML Sham M. Kakade, John Langford
2002	A*	`conf`	Combining Trainig Set and Test Set Bounds. ICML John Langford
2002	A*	`conf`	Competitive Analysis of the Explore/Exploit Tradeoff. ICML John Langford, Martin Zinkevich, Sham M. Kakade
2002	—	`conf`	PAC-Bayes & Margins. NIPS John Langford, John Shawe-Taylor
2002	A*	`conf`	Provably Secure Steganography. CRYPTO Nicholas J. Hopper, John Langford, Luis von Ahn
2002	J	`jnl`	Provably Secure Steganography. IACR Cryptol. ePrint Arch. Nicholas J. Hopper, John Langford, Luis von Ahn
2001	—	`conf`	(Not) Bounding the True Error. NIPS John Langford, Rich Caruana
2001	A*	`conf`	An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML John Langford, Matthias W. Seeger, Nimrod Megiddo
2001	—	`conf`	Risk Sensitive Particle Filters. NIPS Sebastian Thrun, John Langford, Vandi Verma
2000	A*	`conf`	Computable Shell Decomposition Bounds. COLT John Langford, David A. McAllester
2000	A*	`conf`	FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum
1999	A*	`conf`	Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT Avrim Blum, Adam Kalai, John Langford
1999	A*	`conf`	Microchoice Bounds and Self Bounding Learning Algorithms. COLT John Langford, Avrim Blum
1999	A*	`conf`	Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. ICML Sebastian Thrun, John Langford, Dieter Fox
1999	—	`conf`	Probabilistic Planning in the Graphplan Framework. ECP Avrim Blum, John Langford
1998	A*	`conf`	On Learning Monotone Boolean Functions. FOCS Avrim Blum, Carl Burch, John Langford