| 2026 | COLT | Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum. | Nived Rajaraman, Audrey Huang, Miro Dudk, Robert E. Schapire, Dylan J. Foster, Akshay Krishnamurthy |
| 2025 | COLT | The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). | Akshay Krishnamurthy, Gene Li, Ayush Sekhari |
| 2025 | COLT | Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). | Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster |
| 2025 | ICLR | Self-Improvement in Language Models: The Sharpening Mechanism. | Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy |
| 2025 | ICLR | Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. | Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster |
| 2025 | ICLR | Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. | Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun |
| 2025 | ICLR | Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. | Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Hassan Awadallah, Alexander Rakhlin |
| 2025 | ICML | Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. | Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J. Foster |
| 2024 | AISTATS | Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits. | Lequn Wang, Akshay Krishnamurthy, Alex Slivkins |
| 2024 | COLT | Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. | Philip Amortila, Tongyi Cao, Akshay Krishnamurthy |
| 2024 | ICLR | Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. | Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang |
| 2024 | ICML | Rich-Observation Reinforcement Learning with Continuous Latent Dynamics. | Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy |
| 2024 | ICML | Scalable Online Exploration via Coverability. | Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy |
| 2023 | COLT | Learning Hidden Markov Models Using Conditional Samples. | Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2023 | ICLR | Hybrid RL: Using both offline and online data can make RL efficient. | Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun |
| 2023 | ICLR | Transformers Learn Shortcuts to Automata. | Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang |
| 2023 | ICML | Streaming Active Learning with Deep Neural Networks. | Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash |
| 2023 | ICML | Statistical Learning under Heterogenous Distribution Shift. | Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy |
| 2022 | AISTATS | Investigating the Role of Negatives in Contrastive Representation Learning. | Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra |
| 2022 | ALT | Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. | Aadirupa Saha, Akshay Krishnamurthy |
| 2022 | COLT | Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. | Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford |
| 2022 | COLT | Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. | Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu |
| 2022 | ICLR | Anti-Concentrated Confidence Bonuses For Scalable Exploration. | Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade |
| 2022 | ICLR | Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. | Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford |
| 2022 | ICML | Provable Reinforcement Learning with a Short-Term Memory. | Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi |
| 2022 | ICML | Sparsity in Partially Controllable Linear Systems. | Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2022 | ICML | Universal and data-adaptive algorithms for model selection in linear contextual bandits. | Vidya K. Muthukumar, Akshay Krishnamurthy |
| 2022 | ICML | Understanding Contrastive Learning Requires Incorporating Inductive Biases. | Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy |
| 2021 | ALT | Contrastive learning, multi-view redundancy, and linear models. | Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu |
| 2021 | ICLR | Optimism in Reinforcement Learning with Generalized Linear Function Approximation. | Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy |
| 2021 | STOC | Contextual search in the presence of irrational agents. | Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire |
| 2020 | ALT | Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. | Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal |
| 2020 | COLT | Open Problem: Model Selection for Contextual Bandits. | Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo |
| 2020 | ICLR | Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. | Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal |
| 2020 | ICML | Reward-Free Exploration for Reinforcement Learning. | Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu |
| 2020 | ICML | Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. | Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford |
| 2020 | ICML | Doubly robust off-policy evaluation with shrinkage. | Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudk |
| 2020 | ICML | Adaptive Estimator Selection for Off-Policy Evaluation. | Yi Su, Pavithra Srinath, Akshay Krishnamurthy |
| 2020 | ICML | Private Reinforcement Learning with PAC and Regret Guarantees. | Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu |
| 2019 | COLT | Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm. | Tongyi Cao, Akshay Krishnamurthy |
| 2019 | COLT | Contextual bandits with continuous actions: Smoothing, zooming, and adapting. | Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang |
| 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 | ESA | Trace Reconstruction: Generalized and Parameterized. | Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal |
| 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 | Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. | Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabs Pczos |
| 2019 | KDD | Scalable Hierarchical Clustering with Tree Grafting. | Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum |
| 2018 | AISTATS | Parallelised Bayesian Optimisation via Thompson Sampling. | Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabs Pczos |
| 2018 | ICLR | Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. | Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum |
| 2018 | ICML | Semiparametric Contextual Bandits. | Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis |
| 2017 | COLT | Open Problem: First-Order Regret Bounds for Contextual Bandits. | Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, 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 | KDD | A Hierarchical Algorithm for Extreme Clustering. | Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum |
| 2016 | ICML | Efficient Algorithms for Adversarial Contextual Learning. | Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire |
| 2016 | ISIT | Minimax structured normal means inference. | Akshay Krishnamurthy |
| 2015 | AISTATS | On Estimating L22 Divergence. | Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabs Pczos, Larry A. Wasserman |
| 2015 | ICML | Learning to Search Better than Your Teacher. | Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daum III, John Langford |
| 2014 | ACSSC | Subspace learning from extremely compressed measurements. | Martin Azizyan, Akshay Krishnamurthy, Aarti Singh |
| 2014 | ICML | Nonparametric Estimation of Renyi Divergence and Friends. | Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabs Pczos, Larry A. Wasserman |
| 2013 | ACSSC | Recovering graph-structured activations using adaptive compressive measurements. | Akshay Krishnamurthy, James Sharpnack, Aarti Singh |
| 2013 | AISTATS | Detecting Activations over Graphs using Spanning Tree Wavelet Bases. | James Sharpnack, Aarti Singh, Akshay Krishnamurthy |
| 2012 | ICML | Efficient Active Algorithms for Hierarchical Clustering. | Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh |
| 2012 | INFOCOM | Robust multi-source network tomography using selective probes. | Akshay Krishnamurthy, Aarti Singh |
| 2010 | WWW | Fine-grained privilege separation for web applications. | Akshay Krishnamurthy, Adrian Mettler, David A. Wagner |