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ORCIDJohn Langford 0001
Person information
- affiliation: Microsoft Research, USA
Other persons with the same name
- John Langford 0002 (aka: John Warren Langford) — University of Victoria, Canada
- John Langford 0003 — University of Melbourne, Australia
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2020 – today
- 2024
[c117]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. ICLR 2024- [c116]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 - [c115]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 - [i90]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 abs/2402.06187 (2024) - [i89]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:
Position Paper: Agent AI Towards a Holistic Intelligence. CoRR abs/2403.00833 (2024) - [i88]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. CoRR abs/2403.13765 (2024) - [i87]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024) - [i86]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR abs/2409.18433 (2024) - 2023
- [j27]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) - [c114]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: 14390-14421 - [c113]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. ICML 2023: 30005-30021 - [i85]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. CoRR abs/2303.02535 (2023) - [i84]Akanksha Saran, Jacob Alber, Danielle Bragg, Cyril Zhang, John Langford:
Autocalibrating Gaze Tracking: A Demonstration through Gaze Typing. CoRR abs/2307.15039 (2023) - [i83]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 abs/2311.03534 (2023) - 2022
- [c112]Keyi Chen, John Langford, Francesco Orabona:
Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting. AAAI 2022: 6239-6247 - [c111]Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford:
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT 2022: 5062-5127 - [c110]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR 2022 - [c109]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c108]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. ICML 2022: 27428-27453 - [c107]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 - [i82]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR abs/2202.05318 (2022) - [i81]Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford:
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR abs/2206.04282 (2022) - [i80]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. CoRR abs/2206.08364 (2022) - [i79]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. CoRR abs/2207.05836 (2022) - [i78]Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR abs/2207.08229 (2022) - [i77]Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad:
Eigen Memory Trees. CoRR abs/2210.14077 (2022) - [i76]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 abs/2211.00164 (2022) - [i75]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 abs/2211.07614 (2022) - 2021
- [j26]Alberto Bietti, Alekh Agarwal, John Langford:
A Contextual Bandit Bake-off. J. Mach. Learn. Res. 22: 133:1-133:49 (2021) - [c106]Dipendra Misra, Qinghua Liu, Chi Jin, John Langford:
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR 2021 - [c105]Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi:
ChaCha for Online AutoML. ICML 2021: 11263-11273 - [c104]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. ICML 2021: 11414-11423 - [i74]Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi:
ChaCha for Online AutoML. CoRR abs/2106.04815 (2021) - [i73]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. CoRR abs/2106.04887 (2021) - [i72]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR abs/2110.08847 (2021) - 2020
- [j25]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): 15-35 (2020) - [j24]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21: 137:1-137:45 (2020) - [c103]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR 2020 - [c102]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML 2020: 6961-6971 - [c101]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. NeurIPS 2020 - [c100]Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. NeurIPS 2020 - [c99]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 - [i71]Alekh Agarwal, John Langford, Chen-Yu Wei:
Federated Residual Learning. CoRR abs/2003.12880 (2020) - [i70]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 abs/2004.03544 (2020) - [i69]Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. CoRR abs/2006.06040 (2020) - [i68]Keyi Chen, John Langford, Francesco Orabona:
Better Parameter-free Stochastic Optimization with ODE Updates for Coin-Betting. CoRR abs/2006.07507 (2020) - [i67]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 abs/2010.03799 (2020) - [i66]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 abs/2011.12715 (2020)
2010 – 2019
- 2019
- [j23]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20: 65:1-65:50 (2019) - [c98]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT 2019: 2025-2027 - [c97]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: 2898-2933 - [c96]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: 1665-1674 - [c95]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. ICML 2019: 6026-6035 - [c94]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML 2019: 7335-7344 - [c93]Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey:
Efficient Forward Architecture Search. NeurIPS 2019: 10122-10131 - [i65]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR abs/1901.00301 (2019) - [i64]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. CoRR abs/1901.09018 (2019) - [i63]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. CoRR abs/1902.01520 (2019) - [i62]Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey:
Efficient Forward Architecture Search. CoRR abs/1905.13360 (2019) - [i61]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. CoRR abs/1906.03323 (2019) - [i60]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR abs/1906.03671 (2019) - [i59]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR abs/1911.05815 (2019) - 2018
- [c92]Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. COLT 2018: 1739-1776 - [c91]Hal Daumé III, John Langford, Amr Sharaf:
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback. ICLR (Poster) 2018 - [c90]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. ICML 2018: 60-69 - [c89]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML 2018: 2063-2072 - [c88]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS 2018: 1429-1439 - [i58]Alberto Bietti, Alekh Agarwal, John Langford:
Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR abs/1802.04064 (2018) - [i57]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR abs/1803.00606 (2018) - [i56]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. CoRR abs/1803.02453 (2018) - [i55]Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. CoRR abs/1807.06473 (2018) - [i54]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR abs/1811.08540 (2018) - 2017
- [c87]John Langford:
Contextual reinforcement learning. IEEE BigData 2017: 3 - [c86]Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire:
Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT 2017: 4-7 - [c85]Dipendra Kumar Misra, John Langford, Yoav Artzi:
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. EMNLP 2017: 1004-1015 - [c84]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. ICML 2017: 923-932 - [c83]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML 2017: 1704-1713 - [c82]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. ICML 2017: 1915-1924 - [c81]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. NIPS 2017: 3632-3642 - [r2]John Langford:
Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 389-392 - [i53]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. CoRR abs/1703.01014 (2017) - [i52]Dipendra Kumar Misra, John Langford, Yoav Artzi:
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning. CoRR abs/1704.08795 (2017) - [i51]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR abs/1706.04964 (2017) - [i50]Haipeng Luo, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. CoRR abs/1708.01799 (2017) - 2016
- [j22]John Langford, Mark Guzdial:
The solution to AI, what real researchers do, and expectations for CS classrooms. Commun. ACM 59(6): 10-11 (2016) - [j21]Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Learning Reductions That Really Work. Proc. IEEE 104(1): 136-147 (2016) - [c80]Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning by Sketching. NIPS 2016: 902-910 - [c79]Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford:
A Credit Assignment Compiler for Joint Prediction. NIPS 2016: 1705-1713 - [c78]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
PAC Reinforcement Learning with Rich Observations. NIPS 2016: 1840-1848 - [c77]Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang:
Search Improves Label for Active Learning. NIPS 2016: 3342-3350 - [i49]Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning via Sketching. CoRR abs/1602.02202 (2016) - [i48]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR abs/1602.02722 (2016) - [i47]Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang:
Search Improves Label for Active Learning. CoRR abs/1602.07265 (2016) - [i46]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. CoRR abs/1605.04812 (2016) - [i45]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. CoRR abs/1606.03966 (2016) - [i44]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. CoRR abs/1606.04988 (2016) - [i43]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR abs/1610.09512 (2016) - 2015
- [j20]John Langford, Mark Guzdial:
The arbitrariness of reviews, and advice for school administrators. Commun. ACM 58(4): 12-13 (2015) - [j19]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: 389-416 (2015) - [c76]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better than Your Teacher. ICML 2015: 2058-2066 - [c75]Hal Daumé III, John Langford, Kai-Wei Chang, He He, Sudha Rao:
Hands-on Learning to Search for Structured Prediction. HLT-NAACL 2015: 1 - [c74]Anna Choromanska, John Langford:
Logarithmic Time Online Multiclass prediction. NIPS 2015: 55-63 - [c73]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. NIPS 2015: 2755-2763 - [i42]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better Than Your Teacher. CoRR abs/1502.02206 (2015) - [i41]Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Learning Reductions that Really Work. CoRR abs/1502.02704 (2015) - [i40]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Optimization. CoRR abs/1503.02834 (2015) - [i39]Kai-Wei Chang, He He, Hal Daumé III, John Langford:
Learning to Search for Dependencies. CoRR abs/1503.05615 (2015) - [i38]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. CoRR abs/1506.08669 (2015) - 2014
- [j18]John Langford, Mark Guzdial:
Finding a research job, and teaching CS in high school. Commun. ACM 57(10): 10-11 (2014) - [j17]Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A reliable effective terascale linear learning system. J. Mach. Learn. Res. 15(1): 1111-1133 (2014) - [c72]Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins:
Resourceful Contextual Bandits. COLT 2014: 1109-1134 - [c71]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: 1638-1646 - [c70]Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky:
Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS 2014: 2051-2059 - [i37]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. CoRR abs/1402.0555 (2014) - [i36]Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins:
Resourceful Contextual Bandits. CoRR abs/1402.6779 (2014) - [i35]Anna Choromanska, John Langford:
Logarithmic Time Online Multiclass prediction. CoRR abs/1406.1822 (2014) - [i34]Hal Daumé III, John Langford, Stéphane Ross:
Efficient programmable learning to search. CoRR abs/1406.1837 (2014) - [i33]Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl:
Conditional Probability Tree Estimation Analysis and Algorithms. CoRR abs/1408.2031 (2014) - [i32]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. CoRR abs/1408.2065 (2014) - [i31]Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky:
Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR abs/1410.0440 (2014) - 2013
- [c69]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. UAI 2013 - [i30]Stéphane Ross, Paul Mineiro, John Langford:
Normalized Online Learning. CoRR abs/1305.6646 (2013) - [i29]Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford:
Para-active learning. CoRR abs/1310.8243 (2013) - [i28]Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford:
Efficient Online Bootstrapping for Large Scale Learning. CoRR abs/1312.5021 (2013) - 2012
- [j16]John Langford, Ruben Ortega:
Machine learning and algorithms; agile development. Commun. ACM 55(8): 10-11 (2012) - [j15]John Langford:
Parallel machine learning on big data. XRDS 19(1): 60-62 (2012) - [j14]John Langford, Lihong Li, R. Preston McAfee, Kishore Papineni:
Cloud control: voluntary admission control for intranet traffic management. Inf. Syst. E Bus. Manag. 10(3): 295-308 (2012) - [c68]Alina Beygelzimer, John Langford, David M. Pennock:
Learning performance of prediction markets with Kelly bettors. AAMAS 2012: 1317-1318 - [c67]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI 2012: 247-254 - [c66]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. AISTATS 2012: 19-26 - [c65]Lihong Li, Wei Chu, John Langford, Taesup Moon, Xuanhui Wang:
Bandits with Generalized Linear Models. ICML On-line Trading of Exploration and Exploitation 2012: 19-36 - [i27]Alina Beygelzimer, John Langford, David M. Pennock:
Learning Performance of Prediction Markets with Kelly Bettors. CoRR abs/1201.6655 (2012) - [i26]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. CoRR abs/1202.1334 (2012) - [i25]John Langford, Roberto Oliveira, Bianca Zadrozny:
Predicting Conditional Quantiles via Reduction to Classification. CoRR abs/1206.6860 (2012) - [i24]John Langford, Joelle Pineau:
Proceedings of the 29th International Conference on Machine Learning (ICML-12). CoRR abs/1207.4676 (2012) - [i23]Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li:
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. CoRR abs/1210.4862 (2012) - 2011
- [j13]John Langford, Judy Robertson:
Conferences and video lectures; scientific educational games. Commun. ACM 54(12): 8-9 (2011) - [c64]Miroslav Dudík, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Learning. ICML 2011: 1097-1104 - [c63]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178 - [c62]Nikos Karampatziakis, John Langford:
Online Importance Weight Aware Updates. UAI 2011: 392-399 - [c61]Lihong Li, Wei Chu, John Langford, Xuanhui Wang:
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. WSDM 2011: 297-306 - [c60]Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
Contextual Bandit Algorithms with Supervised Learning Guarantees. AISTATS 2011: 19-26 - [i22]Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola:
Parallel Online Learning. CoRR abs/1103.4204 (2011) - [i21]Miroslav Dudík, John Langford, Lihong Li:
Doubly Robust Policy Evaluation and Learning. CoRR abs/1103.4601 (2011) - [i20]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011) - [i19]Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A Reliable Effective Terascale Linear Learning System. CoRR abs/1110.4198 (2011) - 2010
- [j12]John Langford, Lihong Li, Yevgeniy Vorobeychik, Jennifer Wortman:
Maintaining Equilibria During Exploration in Sponsored Search Auctions. Algorithmica 58(4): 990-1021 (2010) - [c59]John Langford:
Robust Efficient Conditional Probability Estimation. COLT 2010: 316-317 - [c58]Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. NIPS 2010: 199-207 - [c57]Alexander L. Strehl, John Langford, Lihong Li, Sham M. Kakade:
Learning from Logged Implicit Exploration Data. NIPS 2010: 2217-2225 - [c56]Lihong Li, Wei Chu, John Langford, Robert E. Schapire:
A contextual-bandit approach to personalized news article recommendation. WWW 2010: 661-670 - [r1]John Langford:
Efficient Exploration in Reinforcement Learning. Encyclopedia of Machine Learning 2010: 309-311 - [i18]Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
An Optimal High Probability Algorithm for the Contextual Bandit Problem. CoRR abs/1002.4058 (2010) - [i17]Alexander L. Strehl, John Langford, Sham M. Kakade:
Learning from Logged Implicit Exploration Data. CoRR abs/1003.0120 (2010) - [i16]Lihong Li, Wei Chu, John Langford, Robert E. Schapire:
A Contextual-Bandit Approach to Personalized News Article Recommendation. CoRR abs/1003.0146 (2010) - [i15]Lihong Li, Wei Chu, John Langford:
An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms. CoRR abs/1003.5956 (2010) - [i14]Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. CoRR abs/1006.2588 (2010) - [i13]Nikos Karampatziakis, John Langford:
Importance Weight Aware Gradient Updates. CoRR abs/1011.1576 (2010)
2000 – 2009
- 2009
- [j11]Maria-Florina Balcan, Alina Beygelzimer, John Langford:
Agnostic active learning. J. Comput. Syst. Sci. 75(1): 78-89 (2009) - [j10]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. J. Mach. Learn. Res. 10: 777-801 (2009) - [j9]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: 2615-2637 (2009) - [j8]Hal Daumé III, John Langford, Daniel Marcu:
Search-based structured prediction. Mach. Learn. 75(3): 297-325 (2009) - [j7]Nicholas J. Hopper, Luis von Ahn, John Langford:
Provably Secure Steganography. IEEE Trans. Computers 58(5): 662-676 (2009) - [c55]Alina Beygelzimer, John Langford, Pradeep Ravikumar:
Error-Correcting Tournaments. ALT 2009: 247-262 - [c54]Alina Beygelzimer, John Langford, Bianca Zadrozny:
Tutorial summary: Reductions in machine learning. ICML 2009: 12 - [c53]Sanjoy Dasgupta, John Langford:
Tutorial summary: Active learning. ICML 2009: 18 - [c52]Alina Beygelzimer, Sanjoy Dasgupta, John Langford:
Importance weighted active learning. ICML 2009: 49-56 - [c51]John Langford, Ruslan Salakhutdinov, Tong Zhang:
Learning nonlinear dynamic models. ICML 2009: 593-600 - [c50]Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg:
Feature hashing for large scale multitask learning. ICML 2009: 1113-1120 - [c49]Alina Beygelzimer, John Langford:
The offset tree for learning with partial labels. KDD 2009: 129-138 - [c48]Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang:
Multi-Label Prediction via Compressed Sensing. NIPS 2009: 772-780 - [c47]Martin Zinkevich, Alexander J. Smola, John Langford:
Slow Learners are Fast. NIPS 2009: 2331-2339 - [c46]Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl:
Conditional Probability Tree Estimation Analysis and Algorithms. UAI 2009: 51-58 - [c45]Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan:
Hash Kernels. AISTATS 2009: 496-503 - [i12]Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang:
Multi-Label Prediction via Compressed Sensing. CoRR abs/0902.1284 (2009) - [i11]Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alexander J. Smola:
Feature Hashing for Large Scale Multitask Learning. CoRR abs/0902.2206 (2009) - [i10]Alina Beygelzimer, John Langford, Pradeep Ravikumar:
Error-Correcting Tournaments. CoRR abs/0902.3176 (2009) - [i9]Alina Beygelzimer, John Langford, Yury Lifshits, Gregory B. Sorkin, Alexander L. Strehl:
Conditional Probability Tree Estimation Analysis and Algorithms. CoRR abs/0903.4217 (2009) - [i8]John Langford, Ruslan Salakhutdinov, Tong Zhang:
Learning Nonlinear Dynamic Models. CoRR abs/0905.3369 (2009) - [i7]Hal Daumé III, John Langford, Daniel Marcu:
Search-based Structured Prediction. CoRR abs/0907.0786 (2009) - 2008
- [j6]Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin:
Robust reductions from ranking to classification. Mach. Learn. 72(1-2): 139-153 (2008) - [c44]John Langford, Alexander L. Strehl, Jennifer Wortman:
Exploration scavenging. ICML 2008: 528-535 - [c43]Sharad Goel, John Langford, Alexander L. Strehl:
Predictive Indexing for Fast Search. NIPS 2008: 505-512 - [c42]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. NIPS 2008: 905-912 - [c41]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: 170-179 - [i6]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. CoRR abs/0806.4686 (2008) - [i5]Alina Beygelzimer, John Langford:
The Offset Tree for Learning with Partial Labels. CoRR abs/0812.4044 (2008) - [i4]Alina Beygelzimer, Sanjoy Dasgupta, John Langford:
Importance Weighted Active Learning. CoRR abs/0812.4952 (2008) - 2007
- [j5]Peter Grünwald, John Langford:
Suboptimal behavior of Bayes and MDL in classification under misspecification. Mach. Learn. 66(2-3): 119-149 (2007) - [c40]Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin:
Robust Reductions from Ranking to Classification. COLT 2007: 604-619 - [c39]John Langford, Tong Zhang:
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS 2007: 817-824 - [c38]Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, John Langford:
Maintaining Equilibria During Exploration in Sponsored Search Auctions. WINE 2007: 119-130 - 2006
- [c37]Jacob D. Abernethy, John Langford, Manfred K. Warmuth:
Continuous Experts and the Binning Algorithm. COLT 2006: 544-558 - [c36]Maria-Florina Balcan, Alina Beygelzimer, John Langford:
Agnostic active learning. ICML 2006: 65-72 - [c35]Alina Beygelzimer, Sham M. Kakade, John Langford:
Cover trees for nearest neighbor. ICML 2006: 97-104 - [c34]Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman:
PAC model-free reinforcement learning. ICML 2006: 881-888 - [c33]Naoki Abe, Bianca Zadrozny, John Langford:
Outlier detection by active learning. KDD 2006: 504-509 - [c32]John Langford, Roberto Oliveira, Bianca Zadrozny:
Predicting Conditional Quantiles via Reduction to Classification. UAI 2006 - 2005
- [j4]John Langford:
Tutorial on Practical Prediction Theory for Classification. J. Mach. Learn. Res. 6: 273-306 (2005) - [c31]Alina Beygelzimer, John Langford, Bianca Zadrozny:
Weighted One-Against-All. AAAI 2005: 720-725 - [c30]John Langford, Bianca Zadrozny:
Estimating Class Membership Probabilities using Classifier Learners. AISTATS 2005: 198-205 - [c29]John Langford, Alina Beygelzimer:
Sensitive Error Correcting Output Codes. COLT 2005: 158-172 - [c28]John Langford:
The Cross Validation Problem. COLT 2005: 687-688 - [c27]Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny:
Error limiting reductions between classification tasks. ICML 2005: 49-56 - [c26]Matti Kääriäinen, John Langford:
A comparison of tight generalization error bounds. ICML 2005: 409-416 - [c25]John Langford, Bianca Zadrozny:
Relating reinforcement learning performance to classification performance. ICML 2005: 473-480 - [c24]Luis von Ahn, Nicholas J. Hopper, John Langford:
Covert two-party computation. STOC 2005: 513-522 - 2004
- [j3]Luis von Ahn, Manuel Blum, John Langford:
Telling humans and computers apart automatically. Commun. ACM 47(2): 56-60 (2004) - [j2]John Langford, David A. McAllester:
Computable Shell Decomposition Bounds. J. Mach. Learn. Res. 5: 529-547 (2004) - [c23]Peter Grünwald, John Langford:
Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. COLT 2004: 331-347 - [c22]Naoki Abe, Bianca Zadrozny, John Langford:
An iterative method for multi-class cost-sensitive learning. KDD 2004: 3-11 - [c21]Arindam Banerjee, John Langford:
An objective evaluation criterion for clustering. KDD 2004: 515-520 - [i3]Peter Grünwald, John Langford:
Suboptimal behaviour of Bayes and MDL in classification under misspecification. CoRR math.ST/0406221 (2004) - [i2]Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford:
Reductions Between Classification Tasks. Electron. Colloquium Comput. Complex. TR04 (2004) - 2003
- [j1]John Langford, Avrim Blum:
Microchoice Bounds and Self Bounding Learning Algorithms. Mach. Learn. 51(2): 165-179 (2003) - [c20]Avrim Blum, John Langford:
PAC-MDL Bounds. COLT 2003: 344-357 - [c19]Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford:
CAPTCHA: Using Hard AI Problems for Security. EUROCRYPT 2003: 294-311 - [c18]Bianca Zadrozny, John Langford, Naoki Abe:
Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM 2003: 435- - [c17]Sham M. Kakade, Michael J. Kearns, John Langford:
Exploration in Metric State Spaces. ICML 2003: 306-312 - [c16]Sham M. Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz:
Correlated equilibria in graphical games. EC 2003: 42-47 - 2002
- [c15]Nicholas J. Hopper, John Langford, Luis von Ahn:
Provably Secure Steganography. CRYPTO 2002: 77-92 - [c14]Sham M. Kakade, John Langford:
Approximately Optimal Approximate Reinforcement Learning. ICML 2002: 267-274 - [c13]John Langford:
Combining Trainig Set and Test Set Bounds. ICML 2002: 331-338 - [c12]John Langford, Martin Zinkevich, Sham M. Kakade:
Competitive Analysis of the Explore/Exploit Tradeoff. ICML 2002: 339-346 - [c11]John Langford, John Shawe-Taylor:
PAC-Bayes & Margins. NIPS 2002: 423-430 - [i1]Nicholas J. Hopper, John Langford, Luis von Ahn:
Provably Secure Steganography. IACR Cryptol. ePrint Arch. 2002: 137 (2002) - 2001
- [c10]John Langford, Matthias W. Seeger, Nimrod Megiddo:
An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML 2001: 290-297 - [c9]John Langford, Rich Caruana:
(Not) Bounding the True Error. NIPS 2001: 809-816 - [c8]Sebastian Thrun, John Langford, Vandi Verma:
Risk Sensitive Particle Filters. NIPS 2001: 961-968 - 2000
- [c7]John Langford, David A. McAllester:
Computable Shell Decomposition Bounds. COLT 2000: 25-34 - [c6]Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum:
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000: 703-710
1990 – 1999
- 1999
- [c5]Avrim Blum, Adam Kalai, John Langford:
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999: 203-208 - [c4]John Langford, Avrim Blum:
Microchoice Bounds and Self Bounding Learning Algorithms. COLT 1999: 209-214 - [c3]Avrim Blum, John Langford:
Probabilistic Planning in the Graphplan Framework. ECP 1999: 319-332 - [c2]Sebastian Thrun, John Langford, Dieter Fox:
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. ICML 1999: 415-424 - 1998
- [c1]Avrim Blum, Carl Burch, John Langford:
On Learning Monotone Boolean Functions. FOCS 1998: 408-415
Coauthor Index
Miroslav Dudík
aka: Miro Dudík
aka: Miro Dudík
Daniel Hsu 0001
aka: Daniel J. Hsu
aka: Daniel J. Hsu
Dipendra Misra
aka: Dipendra Kumar Misra
aka: Dipendra Kumar Misra
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