| 2025 | ACL | Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models. | Lior Belenki, Alekh Agarwal, Tianze Shi, Kristina Toutanova |
| 2025 | ICLR | Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning. | Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar |
| 2025 | ICML | Design Considerations in Offline Preference-based RL. | Alekh Agarwal, Christoph Dann, Teodor Vanislavov Marinov |
| 2025 | ICML | Theoretical guarantees on the best-of-n alignment policy. | Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander Nicholas D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh |
| 2025 | ICML | Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. | Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang |
| 2024 | ALT | A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. | Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth |
| 2024 | EMNLP | Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. | Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ram, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Lonard Hussenot, Olivier Bachem, Edouard Leurent |
| 2024 | ICML | The Non-linear F-Design and Applications to Interactive Learning. | Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang |
| 2024 | ICML | A Minimaximalist Approach to Reinforcement Learning from Human Feedback. | Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal |
| 2024 | ICML | More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. | Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun |
| 2024 | NAACL | Efficient End-to-End Visual Document Understanding with Rationale Distillation. | Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova |
| 2023 | COLT | Provable Benefits of Representational Transfer in Reinforcement Learning. | Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang |
| 2023 | COLT | VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. | Alekh Agarwal, Yujia Jin, Tong Zhang |
| 2023 | ICML | Learning in POMDPs is Sample-Efficient with Hindsight Observability. | Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang |
| 2023 | ICML | Stochastic Gradient Succeeds for Bandits. | Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvri, Dale Schuurmans |
| 2022 | COLT | Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. | Alekh Agarwal, Tong Zhang |
| 2022 | COLT | Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. | Alekh Agarwal, Tong Zhang |
| 2022 | ICLR | Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. | Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford |
| 2022 | ICML | Adversarially Trained Actor Critic for Offline Reinforcement Learning. | Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal |
| 2022 | ICML | Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. | Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun |
| 2021 | COLT | Towards a Dimension-Free Understanding of Adaptive Linear Control. | Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett |
| 2021 | COLT | Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. | Andrea Zanette, Ching-An Cheng, Alekh Agarwal |
| 2021 | ICML | Provably Correct Optimization and Exploration with Non-linear Policies. | Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang |
| 2020 | AAAI | Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. | Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz |
| 2020 | COLT | Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. | Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan |
| 2020 | COLT | Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. | Alekh Agarwal, Sham M. Kakade, Lin F. Yang |
| 2020 | COLT | Taking a hint: How to leverage loss predictors in contextual bandits? | Chen-Yu Wei, Haipeng Luo, Alekh Agarwal |
| 2020 | ICLR | Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. | Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal |
| 2019 | COLT | Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. | Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford |
| 2019 | ICLR | Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. | Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon |
| 2019 | ICML | Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. | Alekh Agarwal, Miroslav Dudk, Zhiwei Steven Wu |
| 2019 | ICML | Provably efficient RL with Rich Observations via Latent State Decoding. | Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudk, John Langford |
| 2019 | ICML | Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. | Chicheng Zhang, Alekh Agarwal, Hal Daum III, John Langford, Sahand Negahban |
| 2019 | UAI | Off-Policy Policy Gradient with Stationary Distribution Correction. | Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill |
| 2018 | COLT | Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. | Nan Jiang, Alekh Agarwal |
| 2018 | COLT | Efficient Contextual Bandits in Non-stationary Worlds. | Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford |
| 2018 | ICML | Hierarchical Imitation and Reinforcement Learning. | Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudk, Yisong Yue, Hal Daum III |
| 2018 | ICML | A Reductions Approach to Fair Classification. | Alekh Agarwal, Alina Beygelzimer, Miroslav Dudk, John Langford, Hanna M. Wallach |
| 2018 | ICML | Practical Contextual Bandits with Regression Oracles. | Dylan J. Foster, Alekh Agarwal, Miroslav Dudk, Haipeng Luo, Robert E. Schapire |
| 2017 | COLT | Open Problem: First-Order Regret Bounds for Contextual Bandits. | Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire |
| 2017 | COLT | Corralling a Band of Bandit Algorithms. | Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire |
| 2017 | ICML | Contextual Decision Processes with low Bellman rank are PAC-Learnable. | Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire |
| 2017 | ICML | Active Learning for Cost-Sensitive Classification. | Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daum III, John Langford |
| 2017 | ICML | Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. | Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudk |
| 2015 | ICML | A Lower Bound for the Optimization of Finite Sums. | Alekh Agarwal, Lon Bottou |
| 2015 | ICML | Learning to Search Better than Your Teacher. | Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daum III, John Langford |
| 2014 | CISS | Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. | Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright |
| 2014 | COLT | Learning Sparsely Used Overcomplete Dictionaries. | Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon |
| 2014 | COLT | Robust Multi-objective Learning with Mentor Feedback. | Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudk, Robert E. Schapire, Aleksandrs Slivkins |
| 2014 | ICML | Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. | Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire |
| 2014 | ICML | Least Squares Revisited: Scalable Approaches for Multi-class Prediction. | Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant |
| 2013 | ICML | Selective sampling algorithms for cost-sensitive multiclass prediction. | Alekh Agarwal |
| 2011 | ICML | Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. | Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright |
| 2011 | UAI | Learning with Missing Features. | Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett |
| 2010 | COLT | Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. | Alekh Agarwal, Ofer Dekel, Lin Xiao |
| 2009 | COLT | A Stochastic View of Optimal Regret through Minimax Duality. | Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin |
| 2008 | ICML | Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. | Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright |
| 2007 | ICML | Learning random walks to rank nodes in graphs. | Alekh Agarwal, Soumen Chakrabarti |
| 2006 | KDD | Learning to rank networked entities. | Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal |