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
论文与出版物
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Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
ICML 2024 | May 2024
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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
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-
-
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
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-
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
-
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
-
Adaptive Estimator Selection for Off-Policy Evaluation
Yi-Hsun Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | February 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
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-
-
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
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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
-
-
-
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
-
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
-
Adaptive Estimator Selection for Off-Policy Evaluation
Yi-Hsun Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | February 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
-
-
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Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
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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
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Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
NeurIPS 2024 | October 2024
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Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
ICML 2024 | May 2024
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Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan Foster, Cyril Zhang, Aleksandrs Slivkins
NeurIPS 2024 | March 2024
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Scalable Online Exploration via Coverability
P. Amortila, Dylan Foster, Akshay Krishnamurthy
ICML 2024 | March 2024
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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
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Exposing Attention Glitches with Flip-Flop Language Modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
NeurIPS 2023 | October 2023
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Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
ICLR | May 2023
oral presentation
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Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
ICML 2023 | March 2023
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Statistical Learning under Heterogenous Distribution Shift
Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy
ICML 2023 | February 2023
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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
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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
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Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | April 2022
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Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
ICLR 2022 | April 2022
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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
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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
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Gone Fishing: Neural Active Learning with Fisher Embeddings
Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
NeurIPS 2021 | December 2021
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Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan Foster, Akshay Krishnamurthy
NeurIPS 2021 | December 2021
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Provable RL with Exogenous Distractors via Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
ICLR 2022 | October 2021
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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
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Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | November 2020
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Provably adaptive reinforcement learning in metric spaces
Tongyi Cao, Akshay Krishnamurthy
NeurIPS 2020 | October 2020
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Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
NeurIPS 2020 | October 2020
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FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
NeurIPS 2020 | October 2020
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Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu
ICML 2020 | July 2020
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Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
ICML 2020 | July 2020
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Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
ICML 2020 | July 2020
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Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miro Dudík
ICML 2020 | July 2020
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Efficient Contextual Bandits With Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
NeurIPS 2020 | June 2020
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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
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Adaptive Estimator Selection for Off-Policy Evaluation
Yi-Hsun Su, Pavithra Srinath, Akshay Krishnamurthy
ICML | February 2020
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Model selection for contextual bandits
Dylan Foster, Akshay Krishnamurthy, Haipeng Luo
Advances in Neural Information Processing Systems | December 2019
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Trace Reconstruction: Generalized and Parametrized
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
European Symposium on Algorithms | September 2019
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Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum
Knowledge Discovery and Data Mining | August 2019
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Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm
Tongyi Cao, Akshay Krishnamurthy
Conference on Learning Theory | June 2019
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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
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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Conference on Learning Theory | June 2019
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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
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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
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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
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Advances in Neural Information Processing Systems | December 2018
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Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
International Conference on Machine Learning | July 2018
arXiv
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Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence and Statistics | April 2018
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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
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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
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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
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An online hierarchical algorithm for extreme clustering
Ari Kobren, Akshay Krishnamurthy, Nicholas Monath, Andrew McCallum
Knowledge Discovery and Data Mining | August 2017
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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
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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
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PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford
Advances in Neural Information Processing Systems | December 2016
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Contextual Semibandits via Supervised Learning Oracles
Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík
Advances in Neural Information Processing Systems | December 2016
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Minimax Structured Normal Means Inference
IEEE International Symposium on Information Theory | July 2016
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Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire
International Conference on Machine Learning | June 2016
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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
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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
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On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
Artificial Intelligence and Statistics | May 2015
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Subspace learning from extremely compressed measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2014
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Nonparametric Estimation of Renyi Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning | July 2014
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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
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Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Akshay Krishnamurthy, Aarti Singh
Advances in Neural Information Processing Systems | December 2013
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Recovering graph-structured activations using adaptive compressive measurements
Akshay Krishnamurthy (akshaykr), James Sharpnack, Aarti Singh
Asilomar Conference on Signals, Systems, and Computers | November 2013
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Detecting activations over graphs using spanning tree wavelet bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Artificial Intelligence and Statistics | April 2013
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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
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Efficient active algorithms for hierarchical clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh
International Conference on Machine Learning | June 2012
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Robust multi-source network tomography using selective probes
Akshay Krishnamurthy, Aarti Singh
IEEE Infocom | March 2012
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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
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Contrastive Estimation Reveals Topic Posterior Information To Linear Models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
March 2020
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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
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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
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Extreme compressive sampling for covariance estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
IEEE Transactions on Information Theory | December 2018
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