Akshay Krishnamurthy

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 143

Top Venues

Publications

Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. CoRR (2024)
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
Statistical Learning under Heterogenous Distribution Shift. CoRR (2023)
Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR (2023)
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
Streaming Active Learning with Deep Neural Networks. CoRR (2023)
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
Contextual Search in the Presence of Adversarial Corruptions. Oper. Res. 71 (4) (2023)
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
Exposing Attention Glitches with Flip-Flop Language Modeling. NeurIPS (2023)
Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. COLT (2023)
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
Statistical Learning under Heterogenous Distribution Shift. ICML (2023)
Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. CoRR (2023)
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
Exposing Attention Glitches with Flip-Flop Language Modeling. CoRR (2023)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. 2023 (2023)
Lequn Wang, Akshay Krishnamurthy, Aleksandrs Slivkins
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits. CoRR (2023)
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
Transformers Learn Shortcuts to Automata. ICLR (2023)
Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
Hybrid RL: Using both offline and online data can make RL efficient. ICLR (2023)
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
Streaming Active Learning with Deep Neural Networks. ICML (2023)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR (2022)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR (2022)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR (2022)
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS (2022)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML (2022)
Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. CoRR (2022)
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
Provable Reinforcement Learning with a Short-Term Memory. CoRR (2022)
Vidya K. Muthukumar, Akshay Krishnamurthy
Universal and data-adaptive algorithms for model selection in linear contextual bandits. ICML (2022)
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR (2022)
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
Transformers Learn Shortcuts to Automata. CoRR (2022)
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR (2022)
Aadirupa Saha, Akshay Krishnamurthy
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. ALT (2022)
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. COLT (2022)
Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Sparsity in Partially Controllable Linear Systems. ICML (2022)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
Investigating the Role of Negatives in Contrastive Representation Learning. AISTATS (2022)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT (2022)
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
Provable Reinforcement Learning with a Short-Term Memory. ICML (2022)
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Anti-Concentrated Confidence Bonuses For Scalable Exploration. ICLR (2022)
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR (2022)
Aadirupa Saha, Akshay Krishnamurthy
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. CoRR (2021)
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS (2021)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Gone Fishing: Neural Active Learning with Fisher Embeddings. CoRR (2021)
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Anti-Concentrated Confidence Bonuses for Scalable Exploration. CoRR (2021)
Dylan J. Foster, Akshay Krishnamurthy
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. NeurIPS (2021)
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR (2021)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
Investigating the Role of Negatives in Contrastive Representation Learning. CoRR (2021)
Vidya Muthukumar, Akshay Krishnamurthy
Universal and data-adaptive algorithms for model selection in linear contextual bandits. CoRR (2021)
Dylan J. Foster, Akshay Krishnamurthy
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. CoRR (2021)
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire
Contextual search in the presence of irrational agents. STOC (2021)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive Estimation Reveals Topic Posterior Information to Linear Models. J. Mach. Learn. Res. 22 (2021)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive learning, multi-view redundancy, and linear models. ALT (2021)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Trace Reconstruction: Generalized and Parameterized. IEEE Trans. Inf. Theory 67 (6) (2021)
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR (2021)
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR (2021)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Gone Fishing: Neural Active Learning with Fisher Embeddings. NeurIPS (2021)
Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Sparsity in Partially Controllable Linear Systems. CoRR (2021)
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. CoRR (2021)
Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy
Optimism in Reinforcement Learning with Generalized Linear Function Approximation. ICLR (2021)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR (2020)
Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. NeurIPS (2020)
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík
Doubly robust off-policy evaluation with shrinkage. ICML (2020)
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents. CoRR (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. CoRR (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. ICML (2020)
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. CoRR (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. CoRR (2020)
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
Adaptive Estimator Selection for Off-Policy Evaluation. ICML (2020)
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. NeurIPS (2020)
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
Adaptive Estimator Selection for Off-Policy Evaluation. CoRR (2020)
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu
Private Reinforcement Learning with PAC and Regret Guarantees. ICML (2020)
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR (2020)
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML (2020)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Open Problem: Model Selection for Contextual Bandits. CoRR (2020)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Open Problem: Model Selection for Contextual Bandits. COLT (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. NeurIPS (2020)
Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
Information Theoretic Regret Bounds for Online Nonlinear Control. NeurIPS (2020)
Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. CoRR (2020)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21 (2020)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. ALT (2020)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive learning, multi-view redundancy, and linear models. CoRR (2020)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS (2020)
Tongyi Cao, Akshay Krishnamurthy
Provably adaptive reinforcement learning in metric spaces. NeurIPS (2020)
Tongyi Cao, Akshay Krishnamurthy
Provably adaptive reinforcement learning in metric spaces. CoRR (2020)
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu
Private Reinforcement Learning with PAC and Regret Guarantees. CoRR (2020)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. CoRR (2020)
Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
Information Theoretic Regret Bounds for Online Nonlinear Control. CoRR (2020)
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Contrastive estimation reveals topic posterior information to linear models. CoRR (2020)
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum
Scalable Hierarchical Clustering with Tree Grafting. CoRR (2020)
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík
Doubly robust off-policy evaluation with shrinkage. CoRR (2019)
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR (2019)
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy
Optimism in Reinforcement Learning with Generalized Linear Function Approximation. CoRR (2019)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Trace Reconstruction: Generalized and Parameterized. ESA (2019)
Tongyi Cao, Akshay Krishnamurthy
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm. COLT (2019)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Sample Complexity of Learning Mixtures of Sparse Linear Regressions. CoRR (2019)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT (2019)
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford
Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20 (2019)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Sample Complexity of Learning Mixture of Sparse Linear Regressions. NeurIPS (2019)
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
Provably efficient RL with Rich Observations via Latent State Decoding. ICML (2019)
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT (2019)
Xi Chen, Akshay Krishnamurthy, Yining Wang
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers. CoRR (2019)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Model selection for contextual bandits. CoRR (2019)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Model Selection for Contextual Bandits. NeurIPS (2019)
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum
Scalable Hierarchical Clustering with Tree Grafting. KDD (2019)
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML (2019)
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Trace Reconstruction: Generalized and Parameterized. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Akshay Krishnamurthy