Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
NeurIPS 2024 | October 2024
Akshay Krishnamurthy
Senior Principal Researcher
Publications
Publications by Year
-
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
ICML 2024 | May 2024
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan Foster, Cyril Zhang, Aleksandrs Slivkins
NeurIPS 2024 | March 2024
Scalable Online Exploration via Coverability
P. Amortila, Dylan Foster, Akshay Krishnamurthy
ICML 2024 | March 2024
-
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
Adam Block, Dylan Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
ICLR 2024 | October 2023
Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
ICLR | May 2023
oral presentation
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
-
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
-
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
-
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
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | November 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
NeurIPS 2020 | October 2020
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
NeurIPS 2020 | October 2020
Provably adaptive reinforcement learning in metric spaces
Tongyi Cao, Akshay Krishnamurthy
NeurIPS 2020 | October 2020
Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
August 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
-
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 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
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
-
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 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
International Conference on Learning Representations | April 2018
-
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
-
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
-
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
-
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
-
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
-
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
-
Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
Publications by Research Area
-
Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
NeurIPS 2024 | October 2024
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
ICML 2024 | May 2024
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan Foster, Cyril Zhang, Aleksandrs Slivkins
NeurIPS 2024 | March 2024
Scalable Online Exploration via Coverability
P. Amortila, Dylan Foster, Akshay Krishnamurthy
ICML 2024 | March 2024
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
Adam Block, Dylan Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
ICLR 2024 | October 2023
Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
ICLR | May 2023
oral presentation
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
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
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | November 2020
Provably adaptive reinforcement learning in metric spaces
Tongyi Cao, Akshay Krishnamurthy
NeurIPS 2020 | October 2020
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
NeurIPS 2020 | October 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
NeurIPS 2020 | October 2020
Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
August 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 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
Conference on Learning Theory | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 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
International Conference on Learning Representations | April 2018
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
-
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
NeurIPS 2020 | October 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
-
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
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
-
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
-
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Publications by Type
-
Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
NeurIPS 2024 | October 2024
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
ICML 2024 | May 2024
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan Foster, Cyril Zhang, Aleksandrs Slivkins
NeurIPS 2024 | March 2024
Scalable Online Exploration via Coverability
P. Amortila, Dylan Foster, Akshay Krishnamurthy
ICML 2024 | March 2024
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
Adam Block, Dylan Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
ICLR 2024 | October 2023
Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
ICLR | May 2023
oral presentation
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
2022 Conference on Learning Theory | May 2022
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
2022 Conference on Learning Theory | May 2022
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
Investigating the Role of Negatives in Contrastive Representation Learning
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022 International Conference on Artificial Intelligence and Statistics | February 2022
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miro Dudík, Robert E. Schapire
NeurIPS 2021 | December 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
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
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) 2020 | December 2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | November 2020
Provably adaptive reinforcement learning in metric spaces
Tongyi Cao, Akshay Krishnamurthy
NeurIPS 2020 | October 2020
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
NeurIPS 2020 | October 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
NeurIPS 2020 | October 2020
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | July 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
Eighth International Conference on Learning Representations (ICLR) | April 2020
Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning | June 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 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
Conference on Learning Theory | June 2019
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miro Dudík, John Langford
International Conference on Machine Learning | June 2019
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 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
International Conference on Learning Representations | April 2018
Off-Policy Evaluation For Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni
2017 Neural Information Processing Systems | December 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
International Conference on Machine Learning | August 2017
An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire
Conference on Learning Theory | July 2017
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Advances in Neural Information Processing Systems | December 2016
PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
Nonparametric von Mises estimators for entropies, divergences, and mutual informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Advances in Neural Information Processing Systems | December 2015
Learning to Search Better than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Hal Daumé III
International Conference on Machine Learning | July 2015
On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
Near-optimal anomaly detection in graphs using Lovasz extended scan statistic
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
Completion of high-rank ultrametric matrices using selective entries
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu
International Conference on Signal Processing and Communication | July 2012
Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
Noise Thresholds for Spectral Clustering
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2011
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
August 2020
Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
Model-Based Reinforcement Learning in Contextual Decision Processes
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
November 2018
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Corrupted Multidimensional Binary Search: Learning In The Presence Of Irrational Agents
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata
arXiv preprint arXiv:2002.11650 | February 2020
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, John Langford, Hal Daumé III
Journal of Machine Learning Research | April 2019
Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018