Alekh Agarwal - RankMe

203 papers A* 63A 4B 1Misc 1Journal 109Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multiple-Prediction-Powered Inference. CoRR Charlie Cowen-Breen, Alekh Agarwal, Stephen Bates, William W. Cohen, Jacob Eisenstein, Amir Globerson, Adam Fisch
2025	A*	`conf`	Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. ICML Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang
2025	J	`jnl`	Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. CoRR Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang
2025	J	`jnl`	Cost-Optimal Active AI Model Evaluation. CoRR Anastasios N. Angelopoulos, Jacob Eisenstein, Jonathan Berant, Alekh Agarwal, Adam Fisch
2025	A*	`conf`	Design Considerations in Offline Preference-based RL. ICML Alekh Agarwal, Christoph Dann, Teodor Vanislavov Marinov
2025	J	`jnl`	Design Considerations in Offline Preference-based RL. CoRR Alekh Agarwal, Christoph Dann, Teodor V. Marinov
2025	J	`jnl`	Mitigating Preference Hacking in Policy Optimization with Pessimism. CoRR Dhawal Gupta, Adam Fisch, Christoph Dann, Alekh Agarwal
2025	—	`conf`	Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models. ACL (1) Lior Belenki, Alekh Agarwal, Tianze Shi, Kristina Toutanova
2025	J	`jnl`	Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models. CoRR Lior Belenki, Alekh Agarwal, Tianze Shi, Kristina Toutanova
2025	J	`jnl`	Ordering-based Conditions for Global Convergence of Policy Gradient Methods. CoRR Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans
2025	J	`jnl`	Preserving Expert-Level Privacy in Offline Reinforcement Learning. Trans. Mach. Learn. Res. Navodita Sharma, Vishnu Vinod, Abhradeep Guha Thakurta, Alekh Agarwal, Borja Balle, Christoph Dann, Aravindan Raghuveer
2025	J	`jnl`	Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation. CoRR Max Qiushi Lin, Jincheng Mei, Matin Aghaei, Michael Lu, Bo Dai, Alekh Agarwal, Dale Schuurmans, Csaba Szepesvári, Sharan Vaswani
2025	A*	`conf`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. ICLR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2025	J	`jnl`	Robust Preference Optimization through Reward Model Distillation. Trans. Mach. Learn. Res. Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal, Ahmad Beirami, Chirag Nagpal, Peter Shaw, Jonathan Berant
2025	J	`jnl`	Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. CoRR Jincheng Mei, Bo Dai, Alekh Agarwal, Sharan Vaswani, Anant Raj, Csaba Szepesvári, Dale Schuurmans
2025	A*	`conf`	Theoretical guarantees on the best-of-n alignment policy. ICML Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander Nicholas D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
2024	B	`conf`	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. ALT Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth
2024	A*	`conf`	A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal
2024	J	`jnl`	A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
2024	—	`conf`	Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. EMNLP (Findings) Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent
2024	J	`jnl`	Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent
2024	—	`conf`	Efficient End-to-End Visual Document Understanding with Rationale Distillation. NAACL-HLT Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova
2024	J	`jnl`	Model-Free Representation Learning and Exploration in Low-Rank MDPs. J. Mach. Learn. Res. Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2024	A*	`conf`	More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. ICML Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
2024	J	`jnl`	More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. CoRR Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
2024	J	`jnl`	Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization. CoRR Teodor V. Marinov, Alekh Agarwal, Mircea Trofin
2024	J	`jnl`	Preserving Expert-Level Privacy in Offline Reinforcement Learning. CoRR Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta, Alekh Agarwal, Borja Balle, Christoph Dann, Aravindan Raghuveer
2024	J	`jnl`	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. CoRR Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
2024	J	`jnl`	Robust Preference Optimization through Reward Model Distillation. CoRR Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal, Ahmad Beirami, Chirag Nagpal, Petet Shaw, Jonathan Berant
2024	A*	`conf`	Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. NeurIPS Jincheng Mei, Bo Dai, Alekh Agarwal, Sharan Vaswani, Anant Raj, Csaba Szepesvári, Dale Schuurmans
2024	J	`jnl`	Stochastic Gradient Succeeds for Bandits. CoRR Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans
2024	A*	`conf`	The Non-linear F-Design and Applications to Interactive Learning. ICML Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang
2024	J	`jnl`	Theoretical guarantees on the best-of-n alignment policy. CoRR Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
2023	J	`jnl`	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth
2023	J	`jnl`	An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR Alekh Agarwal, H. Brendan McMahan, Zheng Xu
2023	J	`jnl`	Efficient End-to-End Visual Document Understanding with Rationale Distillation. CoRR Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova
2023	J	`jnl`	Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking. CoRR Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D'Amour, Dj Dvijotham, Adam Fisch, Katherine A. Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant
2023	A*	`conf`	Learning in POMDPs is Sample-Efficient with Hindsight Observability. ICML Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
2023	J	`jnl`	Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
2023	J	`jnl`	Leveraging User-Triggered Supervision in Contextual Bandits. CoRR Alekh Agarwal, Claudio Gentile, Teodor V. Marinov
2023	A*	`conf`	Ordering-based Conditions for Global Convergence of Policy Gradient Methods. NeurIPS Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans
2023	J	`jnl`	Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments. CoRR Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, Nihar B. Shah
2023	A*	`conf`	Provable Benefits of Representational Transfer in Reinforcement Learning. COLT Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
2023	A*	`conf`	Stochastic Gradient Succeeds for Bandits. ICML Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans
2023	A*	`conf`	VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. COLT Alekh Agarwal, Yujia Jin, Tong Zhang
2022	A*	`conf`	Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun
2022	A*	`conf`	Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun
2022	A*	`conf`	Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. COLT Alekh Agarwal, Tong Zhang
2022	J	`jnl`	Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. CoRR Alekh Agarwal, Tong Zhang
2022	A*	`conf`	Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. NeurIPS Alekh Agarwal, Tong Zhang
2022	J	`jnl`	Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. CoRR Alekh Agarwal, Tong Zhang
2022	A*	`conf`	Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. COLT Alekh Agarwal, Tong Zhang
2022	J	`jnl`	Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. CoRR Alekh Agarwal, Tong Zhang
2022	A*	`conf`	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
2022	A*	`conf`	Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2022	J	`jnl`	VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. CoRR Alekh Agarwal, Yujia Jin, Tong Zhang
2021	J	`jnl`	A Contextual Bandit Bake-off. J. Mach. Learn. Res. Alberto Bietti, Alekh Agarwal, John Langford
2021	A*	`conf`	Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
2021	J	`jnl`	Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
2021	A*	`conf`	Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. COLT Andrea Zanette, Ching-An Cheng, Alekh Agarwal
2021	J	`jnl`	Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. CoRR Andrea Zanette, Ching-An Cheng, Alekh Agarwal
2021	J	`jnl`	Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2021	J	`jnl`	On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
2021	J	`jnl`	Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2021	A*	`conf`	Provably Correct Optimization and Exploration with Non-linear Policies. ICML Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang
2021	J	`jnl`	Provably Correct Optimization and Exploration with Non-linear Policies. CoRR Fei Feng, Wotao Yin, Alekh Agarwal, Lin F. Yang
2021	A*	`conf`	Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
2021	J	`jnl`	Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett
2020	A*	`conf`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2020	A*	`conf`	FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. NeurIPS Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun
2020	J	`jnl`	FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. CoRR Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun
2020	J	`jnl`	Federated Residual Learning. CoRR Alekh Agarwal, John Langford, Chen-Yu Wei
2020	A*	`conf`	Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. AAAI Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz
2020	A*	`conf`	Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. COLT Alekh Agarwal, Sham M. Kakade, Lin F. Yang
2020	A*	`conf`	Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. COLT Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
2020	J	`jnl`	Optimizing Interactive Systems via Data-Driven Objectives. CoRR Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke, Ryen W. White
2020	A*	`conf`	PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. NeurIPS Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun
2020	J	`jnl`	PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. CoRR Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun
2020	J	`jnl`	Policy Improvement from Multiple Experts. CoRR Ching-An Cheng, Andrey Kolobov, Alekh Agarwal
2020	A*	`conf`	Policy Improvement via Imitation of Multiple Oracles. NeurIPS Ching-An Cheng, Andrey Kolobov, Alekh Agarwal
2020	A*	`conf`	Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. NeurIPS Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020	J	`jnl`	Provably Good Batch Reinforcement Learning Without Great Exploration. CoRR Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2020	J	`jnl`	Reparameterized Variational Divergence Minimization for Stable Imitation. CoRR Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan
2020	A*	`conf`	Safe Reinforcement Learning via Curriculum Induction. NeurIPS Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal
2020	J	`jnl`	Safe Reinforcement Learning via Curriculum Induction. CoRR Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal
2020	A*	`conf`	Taking a hint: How to leverage loss predictors in contextual bandits? COLT Chen-Yu Wei, Haipeng Luo, Alekh Agarwal
2020	J	`jnl`	Taking a hint: How to leverage loss predictors in contextual bandits? CoRR Chen-Yu Wei, Haipeng Luo, Alekh Agarwal
2019	J	`jnl`	Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2019	A*	`conf`	Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. NeurIPS Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon
2019	J	`jnl`	Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. CoRR Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
2019	—	`conf`	Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. DGS@ICLR Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon
2019	J	`jnl`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2019	A*	`conf`	Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. ICML Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
2019	J	`jnl`	Fair Regression: Quantitative Definitions and Reduction-based Algorithms. CoRR Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
2019	J	`jnl`	Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations. CoRR Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz
2019	A*	`conf`	Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2019	J	`jnl`	Off-Policy Policy Gradient with State Distribution Correction. CoRR Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2019	A	`conf`	Off-Policy Policy Gradient with Stationary Distribution Correction. UAI Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2019	J	`jnl`	On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. CoRR Alekh Agarwal, Sham M. Kakade, Lin F. Yang
2019	J	`jnl`	Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. CoRR Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
2019	A*	`conf`	Provably efficient RL with Rich Observations via Latent State Decoding. ICML Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	J	`jnl`	Provably efficient RL with Rich Observations via Latent State Decoding. CoRR Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	A*	`conf`	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban
2019	J	`jnl`	Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban
2018	A*	`conf`	A Reductions Approach to Fair Classification. ICML Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
2018	J	`jnl`	A Reductions Approach to Fair Classification. CoRR Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach
2018	A*	`conf`	Efficient Contextual Bandits in Non-stationary Worlds. COLT Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
2018	A*	`conf`	Hierarchical Imitation and Reinforcement Learning. ICML Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
2018	J	`jnl`	Hierarchical Imitation and Reinforcement Learning. CoRR Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
2018	J	`jnl`	Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2018	A*	`conf`	On Oracle-Efficient PAC RL with Rich Observations. NeurIPS Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	J	`jnl`	On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	A*	`conf`	Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. COLT Nan Jiang, Alekh Agarwal
2018	A*	`conf`	Practical Contextual Bandits with Regression Oracles. ICML Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire
2018	J	`jnl`	Practical Contextual Bandits with Regression Oracles. CoRR Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire
2018	J	`jnl`	Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR Alberto Bietti, Alekh Agarwal, John Langford
2017	J	`jnl`	A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
2017	A*	`conf`	Active Learning for Cost-Sensitive Classification. ICML Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2017	J	`jnl`	Active Learning for Cost-Sensitive Classification. CoRR Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
2017	A*	`conf`	Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2017	A*	`conf`	Corralling a Band of Bandit Algorithms. COLT Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
2017	J	`jnl`	Efficient Contextual Bandits in Non-stationary Worlds. CoRR Haipeng Luo, Alekh Agarwal, John Langford
2017	—	`conf`	Off-policy evaluation for slate recommendation. NIPS Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
2017	A*	`conf`	Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
2017	A*	`conf`	Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. ICML Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík
2016	J	`jnl`	A Multiworld Testing Decision Service. CoRR Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, I. Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins
2016	J	`jnl`	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2016	—	`conf`	Contextual semibandits via supervised learning oracles. NIPS Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík
2016	J	`jnl`	Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR Akshay Krishnamurthy, Alekh Agarwal, John Langford
2016	J	`jnl`	Corralling a Band of Bandit Algorithms. CoRR Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
2016	—	`conf`	Efficient Second Order Online Learning by Sketching. NIPS Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
2016	J	`jnl`	Efficient Second Order Online Learning via Sketching. CoRR Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
2016	J	`jnl`	Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
2016	J	`jnl`	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
2016	J	`jnl`	Off-policy evaluation for slate recommendation. CoRR Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni
2016	J	`jnl`	Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. CoRR Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík
2016	—	`conf`	PAC Reinforcement Learning with Rich Observations. NIPS Akshay Krishnamurthy, Alekh Agarwal, John Langford
2015	A*	`conf`	A Lower Bound for the Optimization of Finite Sums. ICML Alekh Agarwal, Léon Bottou
2015	J	`jnl`	Efficient Contextual Semi-Bandit Learning. CoRR Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík
2015	—	`conf`	Efficient and Parsimonious Agnostic Active Learning. NIPS Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	J	`jnl`	Efficient and Parsimonious Agnostic Active Learning. CoRR Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	—	`conf`	Fast Convergence of Regularized Learning in Games. NIPS Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
2015	J	`jnl`	Fast Convergence of Regularized Learning in Games. CoRR Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
2015	J	`jnl`	Learning to Search Better Than Your Teacher. CoRR Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
2015	A*	`conf`	Learning to Search Better than Your Teacher. ICML Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford
2014	J	`jnl`	A reliable effective terascale linear learning system. J. Mach. Learn. Res. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
2014	A*	`conf`	Learning Sparsely Used Overcomplete Dictionaries. COLT Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
2014	A*	`conf`	Least Squares Revisited: Scalable Approaches for Multi-class Prediction. ICML Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
2014	A*	`conf`	Robust Multi-objective Learning with Mentor Feedback. COLT Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins
2014	—	`conf`	Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	J	`jnl`	Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	Misc	`conf`	Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. CISS Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2014	A*	`conf`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2013	J	`jnl`	Exact Recovery of Sparsely Used Overcomplete Dictionaries. CoRR Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
2013	J	`jnl`	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. CoRR Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
2013	J	`jnl`	Least Squares Revisited: Scalable Approaches for Multi-class Prediction. CoRR Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
2013	J	`jnl`	Para-active learning. CoRR Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford
2013	—	`conf`	Selective sampling algorithms for cost-sensitive multiclass prediction. ICML (3) Alekh Agarwal
2013	J	`jnl`	Stochastic Convex Optimization with Bandit Feedback. SIAM J. Optim. Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2013	J	`jnl`	The Generalization Ability of Online Algorithms for Dependent Data. IEEE Trans. Inf. Theory Alekh Agarwal, John C. Duchi
2012	—	`—`	Computational Trade-offs in Statistical Learning. Alekh Agarwal
2012	J	`jnl`	Contextual Bandit Learning with Predictable Rewards CoRR Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	A	`conf`	Contextual Bandit Learning with Predictable Rewards. AISTATS Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	—	`conf`	Distributed delayed stochastic optimization. CDC Alekh Agarwal, John C. Duchi
2012	J	`jnl`	Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling. IEEE Trans. Autom. Control. John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2012	—	`conf`	Dual averaging for distributed optimization. Allerton Conference John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2012	J	`jnl`	Ergodic Mirror Descent. SIAM J. Optim. John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2012	—	`conf`	FASt global convergence of gradient methods for solving regularized M-estimation. SSP Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012	J	`jnl`	Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Trans. Inf. Theory Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2012	J	`jnl`	Oracle inequalities for computationally adaptive model selection CoRR Alekh Agarwal, Peter L. Bartlett, John C. Duchi
2012	J	`jnl`	Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions CoRR Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2012	—	`conf`	Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions. NIPS Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011	J	`jnl`	A Reliable Effective Terascale Linear Learning System CoRR Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
2011	—	`conf`	Distributed Delayed Stochastic Optimization. NIPS Alekh Agarwal, John C. Duchi
2011	—	`conf`	Ergodic mirror descent. Allerton John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2011	J	`jnl`	Fast global convergence of gradient methods for high-dimensional statistical recovery CoRR Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011	A	`conf`	Learning with Missing Features. UAI Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett
2011	J	`jnl`	Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions CoRR Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011	A*	`conf`	Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. ICML Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2011	J	`jnl`	Online and Batch Learning Algorithms for Data with Missing Features CoRR Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett
2011	A*	`conf`	Oracle inequalities for computationally budgeted model selection. COLT Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard
2011	J	`jnl`	Stochastic convex optimization with bandit feedback CoRR Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2011	—	`conf`	Stochastic convex optimization with bandit feedback. NIPS Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2011	J	`jnl`	The Generalization Ability of Online Algorithms for Dependent Data CoRR Alekh Agarwal, John C. Duchi
2010	—	`conf`	Distributed Dual Averaging In Networks. NIPS John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2010	—	`conf`	Fast global convergence rates of gradient methods for high-dimensional statistical recovery. NIPS Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
2010	J	`jnl`	Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. CoRR Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2010	J	`jnl`	Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. J. Mach. Learn. Res. Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright
2010	A*	`conf`	Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. COLT Alekh Agarwal, Ofer Dekel, Lin Xiao
2010	A	`conf`	Optimal Allocation Strategies for the Dark Pool Problem. AISTATS Alekh Agarwal, Peter L. Bartlett, Max Dama
2009	J	`jnl`	A Stochastic View of Optimal Regret through Minimax Duality CoRR Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin
2009	A*	`conf`	A Stochastic View of Optimal Regret through Minimax Duality. COLT Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin
2009	—	`conf`	Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
2008	A*	`conf`	Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. ICML Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright
2007	—	`conf`	An Analysis of Inference with the Universum. NIPS Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf
2007	A*	`conf`	Learning random walks to rank nodes in graphs. ICML Alekh Agarwal, Soumen Chakrabarti
2006	—	`conf`	Learning Parameters in Entity Relationship Graphs from Ranking Preferences. PKDD Soumen Chakrabarti, Alekh Agarwal
2006	A*	`conf`	Learning to rank networked entities. KDD Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal