John Langford

Phone: 914-948-1871
E-mail: jl@hunch.net
Web: http://hunch.net/~jl/

Research Interests

I want to solve AI and believe that the right way to do that is via machine learning. In a solved form, AI is automatic, robust, scalable, and easily used for many problems. In the last several years, we have made great progress in this direction.

Education

Employment History

Microsoft Research, New York, NY May 2012-present Partner Research Manager

Yahoo! Research, New York, NY June 2006-April 2012 Senior Research Scientist

TTI-Chicago, Chicago, IL September 2003 - May 2006 Research Assistant Professor

IBM, TJ Watson, Yorktown, NY September 2002 - August 2003 Herman Goldstine Fellow

University of Pennsylvania, Philadelphia, PA June 2002 - August 2002 Postdoc with Michael Kearns

Activities

Blog: Machine Learning (Theory)
Tutorial: Discovering Agent Centric Latent Dynamics ICML 2023 (and AAMAS 2023).
Tutorial: Real World Interactive Learning at ICML 2017.
Class: Machine Learning the Future at Cornell Tech in 2017.
Tutorial: Learning to Search at HLT-NAACL 2015 and ICML 2015.
Tutorial: Learning to Interact at NIPS 2013.
Class: Large Scale Machine Learning
Tutorial: Scaling up Machine Learning at KDD 2011.
Tutorial: Learning through Exploration Video at ICML 2010 and KDD 2010.
Workshop: Organizer Cores, Clusters, and Clouds at NIPS 2010.
Tutorial: Active Learning Video at ICML 2009.
Tutorial: Reductions in Machine Learning Video at ICML 2009
Workshop: Organizer Principles of Learning Problem Design at NIPS 2007
Tutorial: Learning Reductions IJCAI2005 and MLSS 2005
School: Organizer Machine Learning Summer School Chicago 2005
Workshop: Organizer (Ab)Use of Bounds NIPS 2004
Workshop: Organizer Machine Learning Reductions TTI-Chicago, 2003
Tutorial: Practical Prediction Theory for Classification ICML2003 and MLSS 2005

Service

Treasurer ICML 2022-present
Pandemic President of ICML 2020-2022
President-Elect of ICML in 2017
General Chair: ICML 2016
Program Chair: ICML 2012
New York ML Symposium co-organizer: 2008 2009 2010 2011 2012 2014 2015 2016 2017 2018 2024
Area chair/Senior PC: ICML 2004 NIPS 2006 ICML 2007 ICML 2009 ICML 2010 KDD 2010 NIPS 2010 ICML 2011 and other since.
Program committee: ICML 2003, 2005 & 2008, SODA 2008, AAAI 2005, AAAI 2007ALT 2004, UAI 2007 AIStat 2005 COLT 2008 COLT 2009 and others
Reviewing: NIPS 2001, 2002, 2003, 2005 & 2007, AAAI 2002, MLJ, JMLR, JAIR, JCSS, TCS, JACM, and others

Mentoring

I have worked with many students over time. In all cases, I try to learn something from them as well.
  1. Jacob Abernethy (Associate Professor, Georgia Institute of Technology)
  2. Alekh Agarwal (Staff Research Scientist, Google Research)
  3. Kwangjun Ahn (NVIDIA)
  4. Luis von Ahn (*) (CEO and Co-Founder, Duolingo)
  5. Jordan T. Ash (Principal Researcher, Microsoft Research)
  6. Ashwinkumar Badanidiyuru (Applied Scientist, Uber)
  7. Nina Balcan (*) (Professor, Carnegie Mellon University)
  8. Arindam Banerjee (*) (Professor, University of Illinois Urbana-Champaign)
  9. Alberto Bietti (Research Scientist, Flatiron Institute)
  10. Carl Burch (*) (Software Engineer, Google)
  11. Kai-Wei Chang (Associate Professor, UCLA)
  12. Anna Choromanska (*) (Associate Professor, NYU Tandon)
  13. Varsha Dani (Assistant Professor, Rochester Institute of Technology)
  14. Christoph Dann (Research Scientist, Google (Zurich))
  15. Hal Daume (*) (Professor, University of Maryland & Senior Principal Researcher, Microsoft Research)
  16. Yonathan Efroni (Research Scientist, Meta)
  17. Mikael Henaff (Research Scientist, Meta AI (FAIR))
  18. Nick Hopper (*) (Professor, University of Minnesota)
  19. Daniel Hsu (*) (Associate Professor, Columbia University)
  20. Edward S. Hu (PhD student, University of Pennsylvania)
  21. Riashat Islam (*) (Senior Researcher, Microsoft Research)
  22. Nan Jiang (Associate Professor, University of Illinois Urbana-Champaign)
  23. Matti Kääriäinen (*) (Helsinki, Finland)
  24. Sham Kakade (*) (Professor, Harvard University)
  25. Adam Kalai (*) (Research Scientist, OpenAI)
  26. Nikos Karampatziakis (*) (Microsoft)
  27. Akshay Krishnamurthy (Senior Principal Researcher, Microsoft Research)
  28. Alex Lamb (Tsinghua University)
  29. Nicolas Lambert (Professor of Economics, University of Southern California)
  30. Lihong Li (Meta)
  31. Haipeng Luo (Associate Professor, University of Southern California)
  32. Maryam Majzoubi (Google)
  33. Dipendra Misra (Senior Researcher, Microsoft Research)
  34. Joseph O'Sullivan (ex-Google)
  35. Pradeep Ravikumar (Professor, Carnegie Mellon University)
  36. Ruslan Salakhutdinov (Professor, Carnegie Mellon University)
  37. Akanksha Saran (Research Scientist, Sony AI)
  38. Matthias Seeger (*) (Principal Applied Scientist, Amazon)
  39. Vin de Silva (*) (Professor of Mathematics, Pomona College)
  40. Alex Strehl (*) (Facebook)
  41. Wen Sun (Assistant Professor, Cornell University)
  42. Adith Swaminathan (Research Scientist, Netflix)
  43. Manan Tomar (*) (Postdoctoral Researcher, Microsoft Research)
  44. Jennifer Wortman Vaughan (Senior Principal Research Manager, Microsoft Research)
  45. Vandi Verma (*) (Principal Engineer & Deputy Section Manager, NASA JPL)
  46. Yevgeniy Vorobeychik (Professor of Computer Science, Washington University in St. Louis)
  47. Eric Weiwiora (Machine Learning Scientist, Roche)
  48. Qingyun Wu (Assistant Professor, Penn State University)
  49. Tengyang Xie (Assistant Professor, University of Wisconsin-Madison)
  50. Bianca Zadrozny (*) (Senior Research Manager, IBM Research)
  51. Chicheng Zhang (*) (Assistant Professor, University of Arizona)
  52. Yinglun Zhu (*) (Assistant Professor, University of California, Riverside)
  53. Martin Zinkevich (Research Scientist, Google Research)
(*) = on work which became part of their thesis

Select Publications

  1. Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward S. Hu, Pratyusha Sharma, Riashat Islam, Alex Lamb, John Langford, Next-Latent Prediction Transformers Learn Compact World Models. arXiv 2025
  2. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach, A Reductions Approach to Fair Classification. ICML 2018
  3. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford, A Reliable Effective Terascale Linear Learning System. Journal of Machine Learning Research 15, 2014
  4. Lihong Li, Wei Chu, John Langford, Robert E. Schapire, A contextual-bandit approach to personalized news article recommendation. WWW 2010 [Best Paper Award]
  5. Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg, Feature hashing for large scale multitask learning. ICML 2009
  6. Alina Beygelzimer, Sham M. Kakade, John Langford, Cover Trees for Nearest Neighbor. ICML 2006 [Pat Goldberg Best Paper Award in Computer Science, Electrical Engineering, and Mathematics]
  7. Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford, CAPTCHA: Using Hard AI Problems for Security. Eurocrypt 2003
  8. Sham M. Kakade, John Langford, Approximately Optimal Approximate Reinforcement Learning. ICML 2002
  9. Josh Tenenbaum, Vin de Silva, John Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290: 2319-2323, 2000. isomap site

Papers, forthcoming

  1. John Langford, Giovanni Monea, Yoav Artzi, Harry Dong, Ying Fan, Nathan Godey, Gustavo de Rosa, Zheng Zhan, Free Pause Tokens. Draft, 2026.
  2. Xi Wang, Ziyang Cai, Zheng Zhan, Harry Dong, Ying Fan, Gustavo de Rosa, Tim Pearce, John Langford, Full-bandwidth transformer. Draft, 2026.
  3. Noah Amsel, Jack Zhang, Kwangjun Ahn, Ali Naeimi, Austin Feng, Berlin Chen, Tri Dao, John Langford, Dion3: Full-stack orthogonal updates. Draft, 2026.

Research Programs

Much of my work can be organized into coherent programs.
  1. Latent State Transformers. Most recently, we've been working on translating latent state discovery techniques into transformer representations to make them applicable on the core representations powering today's models.
  2. Latent State Discovery. Many situations are not as simple as contextual bandit learning, so we built up a new theory of contextual multistep reinforcement learning which effectively comes to center on latent state discovery.
  3. Vowpal Wabbit is an open source machine learning system which encodes some of the below. VW has many unique features which makes it a useful tool for advanced prediction, large scale, and interactive settings.
  4. Contextual Bandits. In many real-life situations, we only get feedback about choices taken rather than choices not taken, as is assumed in normal supervised learning. This requires a ground-up rethink of what machine learning means. We have developed evaluators (equivalent to test sets in supervised learning), optmized the use of exploration information, developed exponentially faster algorithms, and created a system for learning.
  5. Active Learning. We proved that active learning was possible in the same noisy settings as supervised learning and eventually refined the algorithm to efficiently use any supervised algorithm as an oracle.
  6. Scalable learning. The goal here is to scale up learning algorithms in all ways. We have addressed scaling in the number of parameters, the number of examples, and the number of labels.
  7. Learning Reductions. We transform complex learning problems into simple ones and then use good algorithms for the simple ones to solve the complex problems. Here we designed the method of analysis as well as the algorithms, and again a small program of work by others is growing out of it.
  8. Creating and using tight sample complexity bounds for evaluation and machine learning algorithm design. This was my thesis work, and a small program of work by others has grown out of it.

Patents

  1. US 11,049,006 B2 Computing system for training neural networks
  2. US 2017/0308535 A1 Computational query modeling and action selection
  3. US 2013/0290223 A1 Method and system for distributed machine learning (granted as US 9,633,315 B2)
  4. US 2013/0268374 A1 Learning Accounts
  5. US 2015/0213510 A1 Framework that facilitates user participation in auctions for display advertisements
  6. US 2016/0105351 A1 Application Testing (granted as US 11,182,280 B2)
  7. US 8,108,323 B2 Distributed Personal Spam Filtering
  8. US 8,174,974 B2 Voluntary Admission Control for Traffic Yield Management
  9. US 8,006,157 B2 Resource-light method and apparatus for outlier detection
  10. US 8,032,535 B2 Personalized web search ranking
  11. US 2012/0016642 A1 Contextual-bandit approach to personalized news article recommendation
  12. US 2010/0057546 A1 System and method for online advertising using user social information
  13. US 2010/0010891 A1 Methods for advertisement slate selection
  14. US 2009/0265227 A1 Methods for Advertisement Display Policy Exploration
  15. US 2005/0091524 A1 Confidential fraud detection system and method
  16. US 2010/0131496 A1 Predictive indexing for fast search

References

Avrim Blum (Phd advisor) avrim@ttic.edu
Michael Kearns mkearns@cis.upenn.edu
Robert Schapire schapire@microsoft.com
Jennifer Chayes jchayes@berkeley.edu
Preston McAfee preston@mcafee.cc
Prabhakar Raghavan pragh@acm.org
Citizenship
  • U.S.
Awards

Publications, All

  1. Aakriti Agrawal, Mucong Ding, Chenghao Deng, Zora Che, Arjun Rajaram, Anirudh Satheesh, Bang An, C. Bayan Bruss, John Langford, Furong Huang, EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles. ACL (Findings), 2026
  2. Vasilis Kontonis, Yuchen Zeng, Shivam Garg, Lingjiao Chen, Hao Tang, Ziyan Wang, Ahmed Awadallah, Eric Horvitz, John Langford, Dimitris Papailiopoulos, MEMENTO: Teaching LLMs to Manage Their Own Context. COLM, 2026
  3. Jyoti Aneja, Michael Harrison, Neel Joshi, Tyler LaBonte, John Langford, Eduardo Salinas, Phi-4-reasoning-vision-15B Technical Report. arXiv, 2026
  4. 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, When does predictive inverse dynamics outperform behavior cloning?. ICML, 2026
  5. Kwangjun Ahn, Noah Amsel, John Langford, Dion2: A Simple Method to Shrink Matrix in Muon. arXiv, 2025
  6. Kwangjun Ahn, Alex Lamb, John Langford, Efficient Joint Prediction of Multiple Future Tokens. arXiv, 2025
  7. Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso, Danielle Bragg, John Langford, EyeO: Autocalibrating Gaze Output with Gaze Input for Gaze Typing. CHI Extended Abstracts, 2025
  8. Vidhisha Balachandran, Jingya Chen, Lingjiao Chen, Shivam Garg, Neel Joshi, Yash Lara, John Langford, Besmira Nushi, Vibhav Vineet, Yue Wu, Safoora Yousefi, Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead. arXiv, 2025
  9. Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward S. Hu, Pratyusha Sharma, Riashat Islam, Alex Lamb, John Langford, Next-Latent Prediction Transformers Learn Compact World Models. arXiv, 2025
  10. Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford, The Belief State Transformer. ICLR, 2025
  11. Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Animashree Anandkumar, Furong Huang, Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. NeurIPS, 2024
  12. 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, PcLast: Discovering Plannable Continuous Latent States. ICML, 2024
  13. 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. ICML, 2024
  14. Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford, Towards Principled Representation Learning from Videos for Reinforcement Learning. ICLR, 2024
  15. Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine, Video Occupancy Models. arXiv, 2024
  16. Akanksha Saran, Jacob Alber, Danielle Bragg, Cyril Zhang, John Langford, Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing. arXiv, 2023
  17. 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
  18. 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
  19. Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash, Streaming Active Learning with Deep Neural Networks. ICML, 2023
  20. Keyi Chen, John Langford, Francesco Orabona, Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. AAAI, 2022
  21. Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro, Contextual Bandits with Large Action Spaces: Made Practical. ICML, 2022
  22. Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad, Eigen Memory Trees. arXiv, 2022
  23. 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
  24. Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu, Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML, 2022
  25. Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR, 2022
  26. Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford, Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT, 2022
  27. Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman, Towards Data-Driven Offline Simulations for Online Reinforcement Learning. arXiv, 2022
  28. Alberto Bietti, Alekh Agarwal, John Langford, A Contextual Bandit Bake-off. J. Mach. Learn. Res. 22, 2021
  29. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi, ChaCha for Online AutoML. ICML, 2021
  30. Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad, Interaction-Grounded Learning. ICML, 2021
  31. Dipendra Misra, Qinghua Liu, Chi Jin, John Langford, Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR, 2021
  32. Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang, Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21, 2020
  33. Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal, Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR, 2020
  34. Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Efficient Contextual Bandits with Continuous Actions. NeurIPS, 2020
  35. Nikos Karampatziakis, John Langford, Paul Mineiro, Empirical Likelihood for Contextual Bandits. NeurIPS, 2020
  36. Alekh Agarwal, John Langford, Chen-Yu Wei, Federated Residual Learning. arXiv, 2020
  37. Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford, Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML, 2020
  38. 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
  39. 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, 2020
  40. 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. arXiv, 2020
  41. Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford, Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20, 2019
  42. Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro, Contextual Memory Trees. ICML, 2019
  43. Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey, Efficient Forward Architecture Search. NeurIPS, 2019
  44. 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
  45. 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
  46. Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban, Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML, 2019
  47. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach, A Reductions Approach to Fair Classification. ICML, 2018
  48. Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford, Efficient Contextual Bandits in Non-stationary Worlds. COLT, 2018
  49. Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire, Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML, 2018
  50. Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire, On Oracle-Efficient PAC RL with Rich Observations. NeurIPS, 2018
  51. Hal Daumé III, John Langford, Amr Sharaf, Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback. ICLR (Poster), 2018
  52. Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire, Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML, 2017
  53. John Langford, Contextual reinforcement learning. IEEE BigData, 2017
  54. John Langford, Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining, 2017
  55. Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro, Logarithmic Time One-Against-Some. ICML, 2017
  56. Dipendra Kumar Misra, John Langford, Yoav Artzi, Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. EMNLP, 2017
  57. Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni, Off-policy evaluation for slate recommendation. NIPS, 2017
  58. Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire, Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT, 2017
  59. Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford, A Credit Assignment Compiler for Joint Prediction. NIPS, 2016
  60. 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, A Multiworld Testing Decision Service. arXiv, 2016
  61. Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford, Efficient Second Order Online Learning by Sketching. NIPS, 2016
  62. Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro, Learning Reductions That Really Work. Proc. IEEE 104, 2016
  63. Akshay Krishnamurthy, Alekh Agarwal, John Langford, PAC Reinforcement Learning with Rich Observations. NIPS, 2016
  64. Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang, Search Improves Label for Active Learning. NIPS, 2016
  65. John Langford, Mark Guzdial, The solution to AI, what real researchers do, and expectations for CS classrooms. Commun. ACM 59, 2016
  66. 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
  67. Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire, Efficient and Parsimonious Agnostic Active Learning. NIPS, 2015
  68. Hal Daumé III, John Langford, Kai-Wei Chang, He He, Sudha Rao, Hands-on Learning to Search for Structured Prediction. HLT-NAACL, 2015
  69. Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford, Learning to Search Better than Your Teacher. ICML, 2015
  70. Kai-Wei Chang, He He, Hal Daumé III, John Langford, Learning to Search for Dependencies. arXiv, 2015
  71. Anna Choromanska, John Langford, Logarithmic Time Online Multiclass prediction. NIPS, 2015
  72. John Langford, Mark Guzdial, The arbitrariness of reviews, and advice for school administrators. Commun. ACM 58, 2015
  73. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford, A reliable effective terascale linear learning system. J. Mach. Learn. Res. 15, 2014
  74. John Langford, Mark Guzdial, Finding a research job, and teaching CS in high school. Commun. ACM 57, 2014
  75. Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins, Resourceful Contextual Bandits. COLT, 2014
  76. Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky, Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS, 2014
  77. Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML, 2014
  78. Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford, Efficient Online Bootstrapping for Large Scale Learning. arXiv, 2013
  79. Stéphane Ross, Paul Mineiro, John Langford, Normalized Online Learning. UAI, 2013
  80. Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford, Para-active learning. arXiv, 2013
  81. Lihong Li, Wei Chu, John Langford, Taesup Moon, Xuanhui Wang, Bandits with Generalized Linear Models. ICML On-line Trading of Exploration and Exploitation, 2012
  82. John Langford, Lihong Li, R. Preston McAfee, Kishore Papineni, Cloud control: voluntary admission control for intranet traffic management. Inf. Syst. E Bus. Manag. 10, 2012
  83. Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire, Contextual Bandit Learning with Predictable Rewards. AISTATS, 2012
  84. Alina Beygelzimer, John Langford, David M. Pennock, Learning performance of prediction markets with Kelly bettors. AAMAS, 2012
  85. John Langford, Ruben Ortega, Machine learning and algorithms; agile development. Commun. ACM 55, 2012
  86. John Langford, Parallel machine learning on big data. XRDS 19, 2012
  87. John Langford, Joelle Pineau, Proceedings of the 29th International Conference on Machine Learning (ICML-12). arXiv, 2012
  88. Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li, Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI, 2012
  89. John Langford, Judy Robertson, Conferences and video lectures; scientific educational games. Commun. ACM 54, 2011
  90. Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire, Contextual Bandit Algorithms with Supervised Learning Guarantees. AISTATS, 2011
  91. Miroslav Dudík, John Langford, Lihong Li, Doubly Robust Policy Evaluation and Learning. ICML, 2011
  92. Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang, Efficient Optimal Learning for Contextual Bandits. UAI, 2011
  93. Nikos Karampatziakis, John Langford, Online Importance Weight Aware Updates. UAI, 2011
  94. Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola, Parallel Online Learning. arXiv, 2011
  95. Lihong Li, Wei Chu, John Langford, Xuanhui Wang, Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. WSDM, 2011
  96. Lihong Li, Wei Chu, John Langford, Robert E. Schapire, A contextual-bandit approach to personalized news article recommendation. WWW, 2010
  97. Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang, Agnostic Active Learning Without Constraints. NIPS, 2010
  98. Alexander L. Strehl, John Langford, Lihong Li, Sham M. Kakade, Learning from Logged Implicit Exploration Data. NIPS, 2010
  99. John Langford, Lihong Li, Yevgeniy Vorobeychik, Jennifer Wortman, Maintaining Equilibria During Exploration in Sponsored Search Auctions. Algorithmica 58, 2010
  100. John Langford, Robust Efficient Conditional Probability Estimation. COLT, 2010
  101. Maria-Florina Balcan, Alina Beygelzimer, John Langford, Agnostic active learning. J. Comput. Syst. Sci. 75, 2009
  102. Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl, Conditional Probability Tree Estimation Analysis and Algorithms. UAI, 2009
  103. Alina Beygelzimer, John Langford, Pradeep Ravikumar, Error-Correcting Tournaments. ALT, 2009
  104. Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg, Feature hashing for large scale multitask learning. ICML, 2009
  105. Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan, Hash Kernels. AISTATS, 2009
  106. Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan, Hash Kernels for Structured Data. J. Mach. Learn. Res. 10, 2009
  107. Alina Beygelzimer, Sanjoy Dasgupta, John Langford, Importance weighted active learning. ICML, 2009
  108. John Langford, Ruslan Salakhutdinov, Tong Zhang, Learning nonlinear dynamic models. ICML, 2009
  109. Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang, Multi-Label Prediction via Compressed Sensing. NIPS, 2009
  110. Nicholas J. Hopper, Luis von Ahn, John Langford, Provably Secure Steganography. IEEE Trans. Computers 58, 2009
  111. Hal Daumé III, John Langford, Daniel Marcu, Search-based structured prediction. Mach. Learn. 75, 2009
  112. Martin Zinkevich, Alexander J. Smola, John Langford, Slow Learners are Fast. NIPS, 2009
  113. John Langford, Lihong Li, Tong Zhang, Sparse Online Learning via Truncated Gradient. J. Mach. Learn. Res. 10, 2009
  114. Alina Beygelzimer, John Langford, The offset tree for learning with partial labels. KDD, 2009
  115. Sanjoy Dasgupta, John Langford, Tutorial summary: Active learning. ICML, 2009
  116. Alina Beygelzimer, John Langford, Bianca Zadrozny, Tutorial summary: Reductions in machine learning. ICML, 2009
  117. John Langford, Alexander L. Strehl, Jennifer Wortman, Exploration scavenging. ICML, 2008
  118. Sharad Goel, John Langford, Alexander L. Strehl, Predictive Indexing for Fast Search. NIPS, 2008
  119. Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin, Robust reductions from ranking to classification. Mach. Learn. 72, 2008
  120. Nicolas S. Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock, Self-financed wagering mechanisms for forecasting. EC, 2008
  121. Peter Grünwald, John Langford, Suboptimal behavior of Bayes and MDL in classification under misspecification. Mach. Learn. 66, 2007
  122. John Langford, Tong Zhang, The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS, 2007
  123. Jacob D. Abernethy, John Langford, Manfred K. Warmuth, Continuous Experts and the Binning Algorithm. COLT, 2006
  124. Alina Beygelzimer, Sham M. Kakade, John Langford, Cover trees for nearest neighbor. ICML, 2006
  125. Naoki Abe, Bianca Zadrozny, John Langford, Outlier detection by active learning. KDD, 2006
  126. Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman, PAC model-free reinforcement learning. ICML, 2006
  127. John Langford, Roberto Oliveira, Bianca Zadrozny, Predicting Conditional Quantiles via Reduction to Classification. UAI, 2006
  128. Matti Kääriäinen, John Langford, A comparison of tight generalization error bounds. ICML, 2005
  129. Luis von Ahn, Nicholas J. Hopper, John Langford, Covert two-party computation. STOC, 2005
  130. Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny, Error limiting reductions between classification tasks. ICML, 2005
  131. John Langford, Bianca Zadrozny, Estimating Class Membership Probabilities using Classifier Learners. AISTATS, 2005
  132. John Langford, Bianca Zadrozny, Relating reinforcement learning performance to classification performance. ICML, 2005
  133. John Langford, Alina Beygelzimer, Sensitive Error Correcting Output Codes. COLT, 2005
  134. John Langford, The Cross Validation Problem. COLT, 2005
  135. John Langford, Tutorial on Practical Prediction Theory for Classification. J. Mach. Learn. Res. 6, 2005
  136. Alina Beygelzimer, John Langford, Bianca Zadrozny, Weighted One-Against-All. AAAI, 2005
  137. Naoki Abe, Bianca Zadrozny, John Langford, An iterative method for multi-class cost-sensitive learning. KDD, 2004
  138. Arindam Banerjee, John Langford, An objective evaluation criterion for clustering. KDD, 2004
  139. John Langford, David A. McAllester, Computable Shell Decomposition Bounds. J. Mach. Learn. Res. 5, 2004
  140. Luis von Ahn, Manuel Blum, John Langford, Telling humans and computers apart automatically. Commun. ACM 47, 2004
  141. Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford, CAPTCHA: Using Hard AI Problems for Security. EUROCRYPT, 2003
  142. Sham M. Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz, Correlated equilibria in graphical games. EC, 2003
  143. Bianca Zadrozny, John Langford, Naoki Abe, Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM, 2003
  144. Sham M. Kakade, Michael J. Kearns, John Langford, Exploration in Metric State Spaces. ICML, 2003
  145. John Langford, Avrim Blum, Microchoice Bounds and Self Bounding Learning Algorithms. Mach. Learn. 51, 2003
  146. Avrim Blum, John Langford, PAC-MDL Bounds. COLT, 2003
  147. Sham M. Kakade, John Langford, Approximately Optimal Approximate Reinforcement Learning. ICML, 2002
  148. John Langford, Combining Trainig Set and Test Set Bounds. ICML, 2002
  149. John Langford, Martin Zinkevich, Sham M. Kakade, Competitive Analysis of the Explore/Exploit Tradeoff. ICML, 2002
  150. John Langford, John Shawe-Taylor, PAC-Bayes & Margins. NIPS, 2002
  151. John Langford, Rich Caruana, (Not) Bounding the True Error. NIPS, 2001
  152. John Langford, Matthias W. Seeger, Nimrod Megiddo, An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML, 2001
  153. Sebastian Thrun, John Langford, Vandi Verma, Risk Sensitive Particle Filters. NIPS, 2001
  154. Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum, FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML, 2000
  155. Avrim Blum, Adam Kalai, John Langford, Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT, 1999
  156. Sebastian Thrun, John Langford, Dieter Fox, Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. ICML, 1999
  157. Avrim Blum, John Langford, Probabilistic Planning in the Graphplan Framework. ECP, 1999
  158. Avrim Blum, Carl Burch, John Langford, On Learning Monotone Boolean Functions. FOCS, 1998