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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 - [i92]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) - [i91]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) - [i90]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. CoRR abs/2403.13765 (2024) - [i89]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024) - [i88]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) - [i87]Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang:
EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM? CoRR abs/2410.04571 (2024) - [i86]Edward S. Hu, Kwangjun Ahn, Qinghua Liu, Haoran Xu, Manan Tomar, Ada Langford, Dinesh Jayaraman, Alex Lamb, John Langford:
Learning to Achieve Goals with Belief State Transformers. CoRR abs/2410.23506 (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]