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Learning the Linear Quadratic Regulator from Nonlinear Observations

Oct 08, 2020
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

* To appear at NeurIPS 2020 

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Private Reinforcement Learning with PAC and Regret Guarantees

Sep 18, 2020
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu


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Contrastive learning, multi-view redundancy, and linear models

Aug 24, 2020
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu


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Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Jun 22, 2020
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu


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Information Theoretic Regret Bounds for Online Nonlinear Control

Jun 22, 2020
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun


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Open Problem: Model Selection for Contextual Bandits

Jun 19, 2020
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

* COLT 2020 open problem 

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Provably adaptive reinforcement learning in metric spaces

Jun 18, 2020
Tongyi Cao, Akshay Krishnamurthy


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FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Jun 18, 2020
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun


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Efficient Contextual Bandits with Continuous Actions

Jun 10, 2020
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins


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Contrastive estimation reveals topic posterior information to linear models

Mar 04, 2020
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu


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Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents

Feb 27, 2020
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata


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Adaptive Estimator Selection for Off-Policy Evaluation

Feb 18, 2020
Yi Su, Pavithra Srinath, Akshay Krishnamurthy


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Reward-Free Exploration for Reinforcement Learning

Feb 07, 2020
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu


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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models

Jan 19, 2020
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

* 22 pages, Accepted at Algorithmic Learning Theory (ALT) 2020 

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Scalable Hierarchical Clustering with Tree Grafting

Dec 31, 2019
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, Andrew McCallum

* 23 pages (appendix included), published at KDD 2019 

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Optimism in Reinforcement Learning with Generalized Linear Function Approximation

Dec 09, 2019
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy


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Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

Nov 13, 2019
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford


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Sample Complexity of Learning Mixtures of Sparse Linear Regressions

Oct 30, 2019
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

* NeurIPS 2019 

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Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

Oct 09, 2019
Xi Chen, Akshay Krishnamurthy, Yining Wang

* 27 pages, 1 figure 

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Doubly robust off-policy evaluation with shrinkage

Jul 22, 2019
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík


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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Jun 09, 2019
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal


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Model selection for contextual bandits

Jun 03, 2019
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo


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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

Feb 05, 2019
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang


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Provably efficient RL with Rich Observations via Latent State Decoding

Jan 25, 2019
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford


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Model-Based Reinforcement Learning in Contextual Decision Processes

Nov 21, 2018
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

* 30 

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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms

Nov 04, 2018
Dylan J. Foster, Akshay Krishnamurthy


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On Oracle-Efficient PAC RL with Rich Observations

Oct 31, 2018
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

* appearing at NIPS 18; full paper including appendix 

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Semiparametric Contextual Bandits

Jul 16, 2018
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis


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Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

May 25, 2018
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos


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Disagreement-based combinatorial pure exploration: Efficient algorithms and an analysis with localization

Nov 30, 2017
Tongyi Cao, Akshay Krishnamurthy


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