Alekh Agarwal

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All published works

Action	Title	Year	Authors
+ PDF Chat	Preserving Expert-Level Privacy in Offline Reinforcement Learning	2024	Navodita Sharma Vishnu Vinod Abhradeep Thakurta Alekh Agarwal Borja Balle Christoph Dann Aravindan Raghuveer
+ PDF Chat	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning	2024	Amrith Setlur Chirag Nagpal Adam Fisch Xinyang Geng Jacob Eisenstein Rishabh Agarwal Alekh Agarwal Jonathan Berant Aviral Kumar
+ PDF Chat	Robust Preference Optimization through Reward Model Distillation	2024	Adam Fisch Jacob Eisenstein Vicky Zayats Alekh Agarwal Ahmad Beirami Chirag Nagpal P. D. Shaw Jonathan Berant
+ PDF Chat	Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization	2024	Teodor V. Marinov Alekh Agarwal Mircea Trofin
+ PDF Chat	Stochastic Gradient Succeeds for Bandits	2024	Jincheng Mei Zixin Zhong Bo Dai Alekh Agarwal Csaba Szepesvári Dale Schuurmans
+ PDF Chat	More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning	2024	Kaiwen Wang Owen Oertell Alekh Agarwal Nathan Kallus W. Sun
+	Theoretical guarantees on the best-of-n alignment policy	2024	Ahmad Beirami Alekh Agarwal Jonathan Berant Alexander D’Amour Jacob Eisenstein Chirag Nagpal Ananda Theertha Suresh
+	A Minimaximalist Approach to Reinforcement Learning from Human Feedback	2024	Gokul Swamy Christoph Dann Rahul Kidambi Zhiwei Steven Wu Alekh Agarwal
+ PDF Chat	Efficient End-to-End Visual Document Understanding with Rationale Distillation	2024	Zhu Wang Alekh Agarwal Mandar Joshi Robin Jia Jesse Thomason Kristina Toutanova
+	Leveraging User-Triggered Supervision in Contextual Bandits	2023	Alekh Agarwal Claudio Gentile Teodor V. Marinov
+	Learning in POMDPs is Sample-Efficient with Hindsight Observability	2023	Jonathan Lee Alekh Agarwal Christoph Dann Tong Zhang
+	An Empirical Evaluation of Federated Contextual Bandit Algorithms	2023	Alekh Agarwal H. Brendan McMahan Zheng Xu
+	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks	2023	Jacob Abernethy Alekh Agarwal Teodor V. Marinov Manfred K. Warmuth
+	Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments	2023	Alexander Goldberg Ivan Stelmakh Kyunghyun Cho Alice Oh Alekh Agarwal Danielle Belgrave Nihar B. Shah
+	Efficient End-to-End Visual Document Understanding with Rationale Distillation	2023	Zhu Wang Alekh Agarwal Mandar Joshi Robin Jia Jesse Thomason Kristina Toutanova
+	Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking	2023	Jacob Eisenstein Chirag Nagpal Alekh Agarwal Ahmad Beirami Alex D’Amour Dj Dvijotham Adam Fisch Katherine Heller Stephen Pfohl Deepak Ramachandran
+	Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach	2022	Xuezhou Zhang Yuda Song Masatoshi Uehara Mengdi Wang Alekh Agarwal Wen Sun
+	Minimax Regret Optimization for Robust Machine Learning under Distribution Shift	2022	Alekh Agarwal Tong Zhang
+	Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling	2022	Alekh Agarwal Tong Zhang
+	Adversarially Trained Actor Critic for Offline Reinforcement Learning	2022	Ching-An Cheng Tengyang Xie Nan Jiang Alekh Agarwal
+	Provable Benefits of Representational Transfer in Reinforcement Learning	2022	Alekh Agarwal Yuda Song Wen Sun Kaiwen Wang Mengdi Wang Xuezhou Zhang
+	Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity	2022	Alekh Agarwal Tong Zhang
+	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL	2022	Jing‐Lin Chen Aditya Modi Akshay Krishnamurthy Nan Jiang Alekh Agarwal
+	VO$Q$L: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation	2022	Alekh Agarwal Yujia Jin Tong Zhang
+	Provable RL with Exogenous Distractors via Multistep Inverse Dynamics	2021	Yonathan Efroni Dipendra Misra Akshay Krishnamurthy Alekh Agarwal John Langford
+ PDF Chat	Bellman-consistent Pessimism for Offline Reinforcement Learning	2021	Tengyang Xie Ching-An Cheng Nan Jiang Paul Mineiro Alekh Agarwal
+	Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation	2021	Andrea Zanette Ching-An Cheng Alekh Agarwal
+	Provably Correct Optimization and Exploration with Non-linear Policies	2021	Fei Feng Wotao Yin Alekh Agarwal Lin F. Yang
+	Model-free Representation Learning and Exploration in Low-rank MDPs.	2021	Aditya Modi Jing‐Lin Chen Akshay Krishnamurthy Nan Jiang Alekh Agarwal
+	Towards a Dimension-Free Understanding of Adaptive Linear Control	2021	Juan C. Perdomo Max Simchowitz Alekh Agarwal Peter L. Bartlett
+	Provably Correct Optimization and Exploration with Non-linear Policies	2021	Fei Feng Wotao Yin Alekh Agarwal Lin F. Yang
+ PDF Chat	A Contextual Bandit Bake-off	2021	Alberto Bietti Alekh Agarwal John Langford
+	Bellman-consistent Pessimism for Offline Reinforcement Learning	2021	Tengyang Xie Ching-An Cheng Nan Jiang Paul Mineiro Alekh Agarwal
+	Provable RL with Exogenous Distractors via Multistep Inverse Dynamics	2021	Yonathan Efroni Dipendra Misra Akshay Krishnamurthy Alekh Agarwal John Langford
+	Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation	2021	Andrea Zanette Ching-An Cheng Alekh Agarwal
+	Model-free Representation Learning and Exploration in Low-rank MDPs	2021	Aditya Modi Jing‐Lin Chen Akshay Krishnamurthy Nan Jiang Alekh Agarwal
+	FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs	2020	Alekh Agarwal Sham M. Kakade Akshay Krishnamurthy Wen Sun
+	PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning	2020	Alekh Agarwal Mikael Henaff Sham M. Kakade Wen Sun
+	Policy Improvement from Multiple Experts	2020	Ching-An Cheng Andrey Kolobov Alekh Agarwal
+ PDF Chat	Policy Improvement via Imitation of Multiple Oracles	2020	Ching-An Cheng Andrey Kolobov Alekh Agarwal
+	Reparameterized Variational Divergence Minimization for Stable Imitation	2020	Dilip Arumugam Debadeepta Dey Alekh Agarwal Aslı Çelikyılmaz Elnaz Nouri Bill Dolan
+ PDF Chat	Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations	2020	Aditya Modi Debadeepta Dey Alekh Agarwal Adith Swaminathan Besmira Nushi Sean Andrist Eric Horvitz
+	Federated Residual Learning.	2020	Alekh Agarwal John Langford Chen-Yu Wei
+	Taking a hint: How to leverage loss predictors in contextual bandits?	2020	Chen-Yu Wei Haipeng Luo Alekh Agarwal
+	FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs	2020	Alekh Agarwal Sham M. Kakade Akshay Krishnamurthy W. Sun
+	Safe Reinforcement Learning via Curriculum Induction	2020	Matteo Turchetta Andrey Kolobov Shital Shah Andreas Krause Alekh Agarwal
+	Provably Good Batch Reinforcement Learning Without Great Exploration	2020	Yao Liu Adith Swaminathan Alekh Agarwal Emma Brunskill
+	PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning	2020	Alekh Agarwal Mikael Henaff Sham M. Kakade Wen Sun
+	Policy Improvement via Imitation of Multiple Oracles	2020	Ching-An Cheng Andrey Kolobov Alekh Agarwal
+	Optimizing Interactive Systems via Data-Driven Objectives	2020	Ziming Li Julia Kiseleva Alekh Agarwal Maarten de Rijke Ryen W. White
+	Reparameterized Variational Divergence Minimization for Stable Imitation	2020	Dilip Arumugam Debadeepta Dey Alekh Agarwal Aslı Çelikyılmaz Elnaz Nouri Bill Dolan
+	Federated Residual Learning	2020	Alekh Agarwal John Langford Chen-Yu Wei
+	Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes	2019	Alekh Agarwal Sham M. Kakade Jason D. Lee Gaurav Mahajan
+	On the Optimality of Sparse Model-Based Planning for Markov Decision Processes.	2019	Alekh Agarwal Sham M. Kakade Lin F. Yang
+	Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal	2019	Alekh Agarwal Sham M. Kakade Lin F. Yang
+	Fair Regression: Quantitative Definitions and Reduction-based Algorithms	2019	Alekh Agarwal Miroslav Dudı́k Zhiwei Steven Wu
+	Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations	2019	Aditya Modi Debadeepta Dey Alekh Agarwal Adith Swaminathan Besmira Nushi Sean Andrist Eric Horvitz
+	Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting	2019	Aditya Grover Jiaming Song Ashish Kapoor Kenneth Tran Alekh Agarwal Eric Horvitz Stefano Ermon
+	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback	2019	Chicheng Zhang Alekh Agarwal Hal Daumé John Langford Sahand Negahban
+	Off-Policy Policy Gradient with State Distribution Correction	2019	Yao Liu Adith Swaminathan Alekh Agarwal Emma Brunskill
+	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	2019	Jordan T. Ash Chicheng Zhang Akshay Krishnamurthy John Langford Alekh Agarwal
+	Provably efficient RL with Rich Observations via Latent State Decoding	2019	Simon S. Du Akshay Krishnamurthy Nan Jiang Alekh Agarwal Miroslav Dudı́k John Langford
+	Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting	2019	Aditya Grover Jiaming Song Alekh Agarwal Kenneth Tran Ashish Kapoor Eric Horvitz Stefano Ermon
+	Fair Regression: Quantitative Definitions and Reduction-based Algorithms	2019	Alekh Agarwal Miroslav Dudík Zhiwei Steven Wu
+	On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift	2019	Alekh Agarwal Sham M. Kakade Jason D. Lee Gaurav Mahajan
+	Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal	2019	Alekh Agarwal Sham M. Kakade Lin F. Yang
+	Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations	2019	Aditya Modi Debadeepta Dey Alekh Agarwal Adith Swaminathan Besmira Nushi Sean Andrist Eric Horvitz
+ PDF Chat	A Contextual Bandit Bake-off	2018	Alberto Bietti Alekh Agarwal John Langford
+	Model-Based Reinforcement Learning in Contextual Decision Processes.	2018	Wen Sun Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford
+	A Reductions Approach to Fair Classification	2018	Alekh Agarwal Alina Beygelzimer Miroslav Dudı́k John Langford Hanna Wallach
+	Hierarchical Imitation and Reinforcement Learning	2018	Hoang Le Nan Jiang Alekh Agarwal Miroslav Dudı́k Yisong Yue Hal Daumé
+	On Polynomial Time PAC Reinforcement Learning with Rich Observations.	2018	Christoph Dann Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford Robert E. Schapire
+	Practical Contextual Bandits with Regression Oracles	2018	Dylan J. Foster Alekh Agarwal Miroslav Dudı́k Haipeng Luo Robert E. Schapire
+	A Contextual Bandit Bake-off	2018	Alberto Bietti Alekh Agarwal John Langford
+	Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches	2018	Wen Sun Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford
+	On Oracle-Efficient PAC RL with Rich Observations	2018	Christoph Dann Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford Robert E. Schapire
+	Learning Data-Driven Objectives to Optimize Interactive Systems	2018	Ziming Li Julia Kiseleva Alekh Agarwal Maarten de Rijke
+	Hierarchical Imitation and Reinforcement Learning	2018	Hoang M. Le Nan Jiang Alekh Agarwal Miroslav Dudı́k Yisong Yue Hal Daumé
+	A Reductions Approach to Fair Classification	2018	Alekh Agarwal Alina Beygelzimer Miroslav Dudı́k John Langford Hanna Wallach
+	Active Learning for Cost-Sensitive Classification	2017	Akshay Krishnamurthy Alekh Agarwal Tzu-Kuo Huang Hal Daumé John Langford
+	Active Learning for Cost-Sensitive Classification	2017	Akshay Krishnamurthy Alekh Agarwal Tzu-Kuo Huang Hal Daumé John Langford
+	Efficient Contextual Bandits in Non-stationary Worlds	2017	Haipeng Luo Chen-Yu Wei Alekh Agarwal John Langford
+	Corralling a Band of Bandit Algorithms	2016	Alekh Agarwal Haipeng Luo Behnam Neyshabur Robert E. Schapire
+	Efficient second order online learning by sketching	2016	Haipeng Luo Alekh Agarwal Nicolò Cesa‐Bianchi John Langford
+	Optimal and Adaptive Off-policy Evaluation in Contextual Bandits	2016	Yu-Xiang Wang Alekh Agarwal Miroslav Dudı́k
+	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable	2016	Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford Robert E. Schapire
+	A Multiworld Testing Decision Service.	2016	Alekh Agarwal Sarah Bird Markus Cozowicz Luong Hoang John Langford Stephen Lee Jiaji Li Dan Melamed Gal Oshri Oswaldo Ribas
+	Off-policy evaluation for slate recommendation	2016	Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miroslav Dudı́k John Langford Damien Jose Imed Zitouni
+	Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations	2016	Akshay Krishnamurthy Alekh Agarwal John Langford
+	PAC Reinforcement Learning with Rich Observations	2016	Akshay Krishnamurthy Alekh Agarwal John Langford
+	Efficient Second Order Online Learning by Sketching	2016	Haipeng Luo Alekh Agarwal Nicolò Cesa‐Bianchi John Langford
+	Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains	2016	David Abel Alekh Agarwal Fernando Díaz Akshay Krishnamurthy Robert E. Schapire
+	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable	2016	Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford Robert E. Schapire
+	Making Contextual Decisions with Low Technical Debt	2016	Alekh Agarwal Sarah Bird Markus Cozowicz Luong Hoang John Langford Stephen Lee Jiaji Li Dan Melamed Gal Oshri Oswaldo Ribas
+	Optimal and Adaptive Off-policy Evaluation in Contextual Bandits	2016	Yu-Xiang Wang Alekh Agarwal Miroslav Dudík
+ PDF Chat	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization	2016	Alekh Agarwal Animashree Anandkumar Prateek Jain Praneeth Netrapalli
+	Off-policy evaluation for slate recommendation	2016	Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miroslav Dudı́k John Langford Damien Jose Imed Zitouni
+	PAC Reinforcement Learning with Rich Observations	2016	Akshay Krishnamurthy Alekh Agarwal John Langford
+	Corralling a Band of Bandit Algorithms	2016	Alekh Agarwal Haipeng Luo Behnam Neyshabur Robert E. Schapire
+	Efficient Contextual Semi-Bandit Learning.	2015	Akshay Krishnamurthy Alekh Agarwal Miroslav Dudı́k
+	Contextual Semibandits via Supervised Learning Oracles	2015	Akshay Krishnamurthy Alekh Agarwal Miroslav Dudı́k
+	Efficient and Parsimonious Agnostic Active Learning	2015	Tzu-Kuo Huang Alekh Agarwal Daniel Hsu John Langford Robert E. Schapire
+	Learning to Search Better Than Your Teacher	2015	Kai-Wei Chang Akshay Krishnamurthy Alekh Agarwal Hal Daumé John Langford
+	Contextual Semibandits via Supervised Learning Oracles	2015	Akshay Krishnamurthy Alekh Agarwal Miroslav Dudı́k
+	Fast Convergence of Regularized Learning in Games	2015	Vasilis Syrgkanis Alekh Agarwal Haipeng Luo Robert E. Schapire
+	Scalable Non-linear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+	Scalable Nonlinear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+	Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions	2014	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits	2014	Alekh Agarwal Daniel Hsu Satyen Kale John Langford Lihong Li Robert E. Schapire
+	A Lower Bound for the Optimization of Finite Sums	2014	Alekh Agarwal Léon Bottou
+	Scalable Nonlinear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+	Para-active learning	2013	Alekh Agarwal Léon Bottou Miroslav Dudı́k John Langford
+	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization	2013	Alekh Agarwal Animashree Anandkumar Prateek Jain Praneeth Netrapalli
+	Least Squares Revisited: Scalable Approaches for Multi-class Prediction	2013	Alekh Agarwal Sham M. Kakade Nikos Karampatziakis Le Song Gregory Valiant
+	Exact Recovery of Sparsely Used Overcomplete Dictionaries.	2013	Alekh Agarwal Animashree Anandkumar Praneeth Netrapalli
+	A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries	2013	Alekh Agarwal Animashree Anandkumar Praneeth Netrapalli
+ PDF Chat	Stochastic Convex Optimization with Bandit Feedback	2013	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+	Least Squares Revisited: Scalable Approaches for Multi-class Prediction	2013	Alekh Agarwal Sham M. Kakade Nikos Karampatziakis Le Song Gregory Valiant
+	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization	2013	Alekh Agarwal Animashree Anandkumar Prateek Jain Praneeth Netrapalli
+	Para-active learning	2013	Alekh Agarwal Léon Bottou Miroslav Dudı́k John Langford
+	Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+ PDF Chat	Distributed delayed stochastic optimization	2012	Alekh Agarwal John C. Duchi
+	Fast global convergence of gradient methods for high-dimensional statistical recovery	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+ PDF Chat	The Generalization Ability of Online Algorithms for Dependent Data	2012	Alekh Agarwal John C. Duchi
+	FASt global convergence of gradient methods for solving regularized M-estimation	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Contextual Bandit Learning with Predictable Rewards	2012	Alekh Agarwal Miroslav Dudı́k Satyen Kale John Langford Robert E. Schapire
+ PDF Chat	Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization	2012	Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright
+	Oracle inequalities for computationally adaptive model selection	2012	Alekh Agarwal Peter L. Bartlett John C. Duchi
+	SUPPLEMENTARY MATERIAL: FAST GLOBAL CONVERGENCE OF GRADIENT METHODS FOR HIGH-DIMENSIONAL STATISTICAL RECOVERY	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions	2012	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Contextual Bandit Learning with Predictable Rewards	2012	Alekh Agarwal Miroslav Dudı́k Satyen Kale John Langford Robert E. Schapire
+	Oracle inequalities for computationally budgeted model selection	2011	Alekh Agarwal John C. Duchi Peter L. Bartlett Clément Levrard
+	A Reliable Effective Terascale Linear Learning System	2011	Alekh Agarwal Olivier Chapelle Miroslav Dudı́k John Langford
+	The Generalization Ability of Online Algorithms for Dependent Data	2011	Alekh Agarwal John C. Duchi
+	Stochastic convex optimization with bandit feedback	2011	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+ PDF Chat	Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling	2011	John C. Duchi Alekh Agarwal Martin J. Wainwright
+	Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions	2011	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Ergodic Subgradient Descent	2011	John C. Duchi Alekh Agarwal Mikael Johansson Michael I. Jordan
+	Distributed Delayed Stochastic Optimization	2011	Alekh Agarwal John C. Duchi
+	Fast global convergence of gradient methods for high-dimensional statistical recovery	2011	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Online and Batch Learning Algorithms for Data with Missing Features	2011	Afshin Rostamizadeh Alekh Agarwal Peter L. Bartlett
+	Ergodic Mirror Descent	2011	John C. Duchi Alekh Agarwal Mikael Johansson Michael I. Jordan
+	A Reliable Effective Terascale Linear Learning System	2011	Alekh Agarwal Olivier Chapelle Miroslav Dudı́k John Langford
+	Stochastic convex optimization with bandit feedback	2011	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+	The Generalization Ability of Online Algorithms for Dependent Data	2011	Alekh Agarwal John C. Duchi
+	Distributed Delayed Stochastic Optimization	2011	Alekh Agarwal John C. Duchi
+	Fast global convergence of gradient methods for high-dimensional statistical recovery	2011	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Fast global convergence rates of gradient methods for high-dimensional statistical recovery	2010	Alekh Agarwal Sahand Negahban Martin J. Wainwright
+	Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization	2010	Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright
+	Information-theoretic lower bounds on the oracle complexity of convex optimization	2010	Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright
+	Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes	2010	Pradeep Ravikumar Alekh Agarwal Martin J. Wainwright
+	Optimal Allocation Strategies for the Dark Pool Problem	2010	Alekh Agarwal Peter L. Bartlett Max Dama
+	Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization	2010	Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright
+	A Stochastic View of Optimal Regret through Minimax Duality	2009	Jacob Abernethy Alekh Agarwal Peter L. Bartlett Alexander Rakhlin
+	Message-passing for graph-structured linear programs	2008	Pradeep Ravikumar Alekh Agarwal Martin J. Wainwright

Common Coauthors

Coauthor	Papers Together
John Langford	42
Akshay Krishnamurthy	27
Miroslav Dudík	21
Martin J. Wainwright	19
Sham M. Kakade	14
Sahand Negahban	13
Robert E. Schapire	13
John C. Duchi	11
Nan Jiang	10
Peter L. Bartlett	10
Ching-An Cheng	8
Haipeng Luo	8
Daniel Hsu	8
Adith Swaminathan	7
Pradeep Ravikumar	6
Hal Daumé	6
Aditya Modi	6
Praneeth Netrapalli	5
Animashree Anandkumar	5
Wen Sun	5
Debadeepta Dey	5
Eric Horvitz	5
Alina Beygelzimer	5
Lin F. Yang	5
Christoph Dann	5
Andrey Kolobov	4
Jacob Eisenstein	4
Tong Zhang	4
Alexander Rakhlin	4
Jonathan Berant	4
Chirag Nagpal	4
Chen-Yu Wei	4
Satyen Kale	3
Besmira Nushi	3
Nan Jiang	3
Teodor V. Marinov	3
Ahmad Beirami	3
Dean P. Foster	3
Alberto Bietti	3
Jing‐Lin Chen	3
Léon Bottou	3
Adam Fisch	3
Sean Andrist	3
Zhiwei Steven Wu	3
Tengyang Xie	3
Matus Telgarsky	3
Tzu-Kuo Huang	3
Julia Kiseleva	2
Alex Slivkins	2
Gal Oshri	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Gradient methods for minimizing composite objective function	2007	Yu. Nesterov	13
+	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable	2016	Nan Jiang Akshay Krishnamurthy Alekh Agarwal John Langford Robert E. Schapire	11
+ PDF Chat	Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls	2011	Garvesh Raskutti Martin J. Wainwright Bin Yu	10
+	Convex Optimization	2004	Stephen Boyd Lieven Vandenberghe	10
+	Nonlinear Programming	1995	Dimitri P. Bertsekas	10
+ PDF Chat	Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling	2011	John C. Duchi Alekh Agarwal Martin J. Wainwright	9
+	Convex analysis and minimization algorithms	1993	Jean‐Baptiste Hiriart‐Urruty Claude Lemaréchal	9
+	A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers	2012	Sahand Negahban Pradeep Ravikumar Martin J. Wainwright Bin Yu	9
+	Primal-dual subgradient methods for convex problems	2007	Yurii Nesterov	9
+	Restricted Eigenvalue Properties for Correlated Gaussian Designs	2010	Garvesh Raskutti Martin J. Wainwright Bin Yu	7
+	Contextual Bandit Algorithms with Supervised Learning Guarantees	2010	Alina Beygelzimer John Langford Lihong Li Lev Reyzin Robert E. Schapire	7
+	Robust Stochastic Approximation Approach to Stochastic Programming	2009	Arkadi Nemirovski Anatoli Juditsky Guanghui Lan Alexander Shapiro	7
+	Mirror descent and nonlinear projected subgradient methods for convex optimization	2003	Amir Beck Marc Teboulle	7
+	Online convex programming and generalized infinitesimal gradient ascent	2003	Martin Zinkevich	7
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	7
+ PDF Chat	A contextual-bandit approach to personalized news article recommendation	2010	Lihong Li Wei Chu John Langford Robert E. Schapire	7
+	Normalized Online Learning	2013	Stéphane Ross Paul Mineiro John Langford	7
+	Provably Efficient Reinforcement Learning with Linear Function Approximation	2019	Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan	7
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	7
+	Reinforcement and Imitation Learning via Interactive No-Regret Learning	2014	Stéphane Ross J. Andrew Bagnell	7
+ PDF Chat	Simultaneous analysis of Lasso and Dantzig selector	2009	Peter J. Bickel Ya’acov Ritov Alexandre B. Tsybakov	7
+ PDF Chat	Estimation of (near) low-rank matrices with noise and high-dimensional scaling	2011	Sahand Negahban Martin J. Wainwright	6
+	Restricted strong convexity and weighted matrix completion: Optimal bounds with noise	2010	Sahand Negahban Martin J. Wainwright	6
+ PDF Chat	Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization	2010	Benjamin Recht Maryam Fazel Pablo A. Parrilo	6
+	Gradient descent with sparsification	2009	Rahul Garg Rohit Khandekar	6
+	ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES	1933	W. R THOMPSON	6
+	Is Q-learning Provably Efficient?	2018	Chi Jin Zeyuan Allen-Zhu Sébastien Bubeck Michael I. Jordan	6
+	Primal-dual subgradient methods for minimizing uniformly convex functions	2014	Anatoli B. Juditsky Yuri Nesterov	5
+	Composite objective mirror descent	2010	John C. Duchi Shai Shalev‐Shwartz Yoram Singer Ambuj Tewari	5
+	Grouped and Hierarchical Model Selection through Composite Absolute Penalties	2007	Peng Zhao Guilherme V. Rocha Bin Yu	5
+ PDF Chat	Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path	2007	András Antos Csaba Szepesvári Rémi Munos	5
+	Doubly Robust Policy Evaluation and Learning	2011	Miroslav Dudík John Langford Lihong Li	5
+	A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers	2009	Sahand Negahban Bin Yu Martin J. Wainwright Pradeep Ravikumar	5
+	Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound	2019	Lin F. Yang Mengdi Wang	5
+ PDF Chat	Robust principal component analysis?	2011	Emmanuel J. Candès Xiaodong Li Yi Ma John Wright	5
+	A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning	2010	Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell	5
+	Approximate Policy Iteration Schemes: A Comparison	2014	Bruno Scherrer	4
+	Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence	2008	Elaine Hale Wotao Yin Yin Zhang	4
+	A Simpler Approach to Matrix Completion	2009	Benjamin Recht	4
+	Reconstruction from anisotropic random measurements	2011	Mark Rudelson Shuheng Zhou	4
+	Probability in Banach Spaces: Isoperimetry and Processes	1991	Michel Ledoux Michel Talagrand	4
+ PDF Chat	Weighted sums of certain dependent random variables	1967	Kazuoki Azuma	4
+ PDF Chat	Robust PCA via Outlier Pursuit	2012	Huan Xu Constantine Caramanis Sujay Sanghavi	4
+	Adaptive Bound Optimization for Online Convex Optimization	2010	H. Brendan McMahan M. J. V. Streeter	4
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	4
+	A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems	2009	Amir Beck Marc Teboulle	4
+	High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity	2012	Po‐Ling Loh Martin J. Wainwright	4
+ PDF Chat	Information-theoretic determination of minimax rates of convergence	1999	Yuhong Yang Andrew R. Barron	4
+ PDF Chat	On the conditions used to prove oracle results for the Lasso	2009	Sara A. van de Geer Peter Bühlmann	4
+	Error bounds and convergence analysis of feasible descent methods: a general approach	1993	Zhi-Quan Luo Paul Tseng	4