Krishnamurthy, Akshay

89 publications

COLT 2025 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

ICLR 2025 Computationally Efficient RL Under Linear Bellman Completeness for Deterministic Dynamics Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun

ICLR 2025 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

ICLR 2025 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

ICML 2025 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

ICLR 2025 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

COLT 2025 The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract) Akshay Krishnamurthy, Gene Li, Ayush Sekhari

ICLR 2024 Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression Adam Block, Dylan J Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang

NeurIPS 2024 Can Large Language Models Explore In-Context? Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins

ICMLW 2024 Can Large Language Models Explore In-Context? Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, Aleksandrs Slivkins

COLT 2024 Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning Philip Amortila, Tongyi Cao, Akshay Krishnamurthy

JMLR 2024 Model-Free Representation Learning and Exploration in Low-Rank MDPs Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

AISTATS 2024 Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits Lequn Wang, Akshay Krishnamurthy, Alex Slivkins

NeurIPS 2024 Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi

ICML 2024 Rich-Observation Reinforcement Learning with Continuous Latent Dynamics Yuda Song, Lili Wu, Dylan J Foster, Akshay Krishnamurthy

ICML 2024 Scalable Online Exploration via Coverability Philip Amortila, Dylan J Foster, Akshay Krishnamurthy

NeurIPSW 2024 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

JMLR 2023 A Complete Characterization of Linear Estimators for Offline Policy Evaluation Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade

NeurIPS 2023 Exposing Attention Glitches with Flip-Flop Language Modeling Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

ICMLW 2023 Exposing Attention Glitches with Flip-Flop Language Modeling Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

TMLR 2023 Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J Foster, Lekan P Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

ICLR 2023 Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun

COLT 2023 Learning Hidden Markov Models Using Conditional Samples Gaurav Mahajan, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

ICML 2023 Statistical Learning Under Heterogeneous Distribution Shift Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy

ICML 2023 Streaming Active Learning with Deep Neural Networks Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash

ICLR 2023 Transformers Learn Shortcuts to Automata Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

AISTATS 2022 Investigating the Role of Negatives in Contrastive Representation Learning Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra

ICLR 2022 Anti-Concentrated Confidence Bonuses for Scalable Exploration Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

ALT 2022 Efficient and Optimal Algorithms for Contextual Dueling Bandits Under Realizability Aadirupa Saha, Akshay Krishnamurthy

NeurIPSW 2022 Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun

COLT 2022 Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu

NeurIPS 2022 On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

ICML 2022 Provable Reinforcement Learning with a Short-Term Memory Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

ICLR 2022 Provably Filtering Exogenous Distractors Using Multistep Inverse Dynamics Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

COLT 2022 Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Yonathan Efroni, Dylan J Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford

ICML 2022 Sparsity in Partially Controllable Linear Systems Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

ICML 2022 Understanding Contrastive Learning Requires Incorporating Inductive Biases Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy

ICML 2022 Universal and Data-Adaptive Algorithms for Model Selection in Linear Contextual Bandits Vidya K Muthukumar, Akshay Krishnamurthy

NeurIPS 2021 Bayesian Decision-Making Under Misspecified Priors with Applications to Meta-Learning Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire

JMLR 2021 Contrastive Estimation Reveals Topic Posterior Information to Linear Models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

ALT 2021 Contrastive Learning, Multi-View Redundancy, and Linear Models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

NeurIPS 2021 Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination Dylan J Foster, Akshay Krishnamurthy

NeurIPS 2021 Gone Fishing: Neural Active Learning with Fisher Embeddings Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade

ICLR 2021 Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy

ICML 2020 Adaptive Estimator Selection for Off-Policy Evaluation Yi Su, Pavithra Srinath, Akshay Krishnamurthy

ALT 2020 Algebraic and Analytic Approaches for Parameter Learning in Mixture Models Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

JMLR 2020 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

ICLR 2020 Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal

ICML 2020 Doubly Robust Off-Policy Evaluation with Shrinkage Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudik

NeurIPS 2020 Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

NeurIPS 2020 FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun

NeurIPS 2020 Information Theoretic Regret Bounds for Online Nonlinear Control Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

ICML 2020 Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford

NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

COLT 2020 Open Problem: Model Selection for Contextual Bandits Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

ICML 2020 Private Reinforcement Learning with PAC and Regret Guarantees Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu

NeurIPS 2020 Provably Adaptive Reinforcement Learning in Metric Spaces Tongyi Cao, Akshay Krishnamurthy

ICML 2020 Reward-Free Exploration for Reinforcement Learning Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu

NeurIPS 2020 Sample-Efficient Reinforcement Learning of Undercomplete POMDPs Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

JMLR 2019 Active Learning for Cost-Sensitive Classification Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé Iii, John Langford

COLT 2019 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

COLT 2019 Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm Tongyi Cao, Akshay Krishnamurthy

NeurIPS 2019 Model Selection for Contextual Bandits Dylan J Foster, Akshay Krishnamurthy, Haipeng Luo

COLT 2019 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

ICML 2019 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

ICML 2019 Provably Efficient RL with Rich Observations via Latent State Decoding Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford

NeurIPS 2019 Sample Complexity of Learning Mixture of Sparse Linear Regressions Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

NeurIPS 2018 Contextual Bandits with Surrogate Losses: Margin Bounds and Efficient Algorithms Dylan J Foster, Akshay Krishnamurthy

ICLR 2018 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

NeurIPS 2018 On Oracle-Efficient PAC RL with Rich Observations Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

AISTATS 2018 Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

ICML 2018 Semiparametric Contextual Bandits Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

ICML 2017 Active Learning for Cost-Sensitive Classification Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé, John Langford

ICML 2017 Contextual Decision Processes with Low Bellman Rank Are PAC-Learnable Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

NeurIPS 2017 Off-Policy Evaluation for Slate Recommendation Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni

COLT 2017 Open Problem: First-Order Regret Bounds for Contextual Bandits Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire

NeurIPS 2016 Contextual Semibandits via Supervised Learning Oracles Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik

ICML 2016 Efficient Algorithms for Adversarial Contextual Learning Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire

NeurIPS 2016 Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire

NeurIPS 2016 PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford

ICML 2015 Learning to Search Better than Your Teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé, John Langford

NeurIPS 2015 Nonparametric Von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M Robins

AISTATS 2015 On Estimating L22 Divergence Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

ICML 2014 Nonparametric Estimation of Renyi Divergence and Friends Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

AISTATS 2013 Detecting Activations over Graphs Using Spanning Tree Wavelet Bases James Sharpnack, Aarti Singh, Akshay Krishnamurthy

NeurIPS 2013 Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh

NeurIPS 2013 Near-Optimal Anomaly Detection in Graphs Using Lovasz Extended Scan Statistic James L Sharpnack, Akshay Krishnamurthy, Aarti Singh

ICML 2012 Efficient Active Algorithms for Hierarchical Clustering Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh

NeurIPS 2011 Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh