Akshay Krishnamurthy

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University of Massachusetts Amherst ∙ Microsoft ∙ Carnegie Mellon University

Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst

Learning the Linear Quadratic Regulator from Nonlinear Observations

We introduce a new problem setting for continuous control called the LQR...
10/08/2020 ∙ by Zakaria Mhammedi, et al. ∙ 4 ∙ share

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Private Reinforcement Learning with PAC and Regret Guarantees

Motivated by high-stakes decision-making domains like personalized medic...
09/18/2020 ∙ by Giuseppe Vietri, et al. ∙ 13 ∙ share

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Contrastive learning, multi-view redundancy, and linear models

Self-supervised learning is an empirically successful approach to unsupe...
08/24/2020 ∙ by Christopher Tosh, et al. ∙ 5 ∙ share

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Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Partial observability is a common challenge in many reinforcement learni...
06/22/2020 ∙ by Chi Jin, et al. ∙ 9 ∙ share

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Information Theoretic Regret Bounds for Online Nonlinear Control

This work studies the problem of sequential control in an unknown, nonli...
06/22/2020 ∙ by Sham Kakade, et al. ∙ 14 ∙ share

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Open Problem: Model Selection for Contextual Bandits

In statistical learning, algorithms for model selection allow the learne...
06/19/2020 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Provably adaptive reinforcement learning in metric spaces

We study reinforcement learning in continuous state and action spaces en...
06/18/2020 ∙ by Tongyi Cao, et al. ∙ 0 ∙ share

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FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

In order to deal with the curse of dimensionality in reinforcement learn...
06/18/2020 ∙ by Alekh Agarwal, et al. ∙ 7 ∙ share

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Efficient Contextual Bandits with Continuous Actions

We create a computationally tractable algorithm for contextual bandits w...
06/10/2020 ∙ by Maryam Majzoubi, et al. ∙ 0 ∙ share

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Contrastive estimation reveals topic posterior information to linear models

Contrastive learning is an approach to representation learning that util...
03/04/2020 ∙ by Christopher Tosh, et al. ∙ 6 ∙ share

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

Standard game-theoretic formulations for settings like contextual pricin...
02/26/2020 ∙ by Akshay Krishnamurthy, et al. ∙ 13 ∙ share

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Adaptive Estimator Selection for Off-Policy Evaluation

We develop a generic data-driven method for estimator selection in off-p...
02/18/2020 ∙ by Yi Su, et al. ∙ 0 ∙ share

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Reward-Free Exploration for Reinforcement Learning

Exploration is widely regarded as one of the most challenging aspects of...
02/07/2020 ∙ by Chi Jin, et al. ∙ 0 ∙ share

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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models

We present two different approaches for parameter learning in several mi...
01/19/2020 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Scalable Hierarchical Clustering with Tree Grafting

We introduce Grinch, a new algorithm for large-scale, non-greedy hierarc...
12/31/2019 ∙ by Nicholas Monath, et al. ∙ 15 ∙ share

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Optimism in Reinforcement Learning with Generalized Linear Function Approximation

We design a new provably efficient algorithm for episodic reinforcement ...
12/09/2019 ∙ by Yining Wang, et al. ∙ 0 ∙ share

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Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

We present an algorithm, HOMER, for exploration and reinforcement learni...
11/13/2019 ∙ by Dipendra Misra, et al. ∙ 15 ∙ share

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Sample Complexity of Learning Mixtures of Sparse Linear Regressions

In the problem of learning mixtures of linear regressions, the goal is t...
10/30/2019 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

We consider the dynamic assortment optimization problem under the multin...
10/09/2019 ∙ by Xi Chen, et al. ∙ 0 ∙ share

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Doubly robust off-policy evaluation with shrinkage

We design a new family of estimators for off-policy evaluation in contex...
07/22/2019 ∙ by Yi Su, et al. ∙ 0 ∙ share

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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

We design a new algorithm for batch active learning with deep neural net...
06/09/2019 ∙ by Jordan T. Ash, et al. ∙ 3 ∙ share

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Model selection for contextual bandits

We introduce the problem of model selection for contextual bandits, wher...
06/03/2019 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Trace Reconstruction: Generalized and Parameterized

In the beautifully simple-to-state problem of trace reconstruction, the ...
04/21/2019 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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

We study contextual bandit learning with an abstract policy class and co...
02/05/2019 ∙ by Akshay Krishnamurthy, et al. ∙ 38 ∙ share

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Provably efficient RL with Rich Observations via Latent State Decoding

We study the exploration problem in episodic MDPs with rich observations...
01/25/2019 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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Model-Based Reinforcement Learning in Contextual Decision Processes

We study the sample complexity of model-based reinforcement learning in ...
11/21/2018 ∙ by Wen Sun, et al. ∙ 12 ∙ share

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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms

We introduce a new family of margin-based regret guarantees for adversar...
06/28/2018 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

We design a new myopic strategy for a wide class of sequential design of...
05/25/2018 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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Semiparametric Contextual Bandits

This paper studies semiparametric contextual bandits, a generalization o...
03/12/2018 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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On Polynomial Time PAC Reinforcement Learning with Rich Observations

We study the computational tractability of provably sample-efficient (PA...
03/01/2018 ∙ by Christoph Dann, et al. ∙ 0 ∙ share

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

We design new algorithms for the combinatorial pure exploration problem ...
11/21/2017 ∙ by Tongyi Cao, et al. ∙ 0 ∙ share

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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Knowledge bases (KB), both automatically and manually constructed, are o...
11/15/2017 ∙ by Rajarshi Das, et al. ∙ 0 ∙ share

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Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

We design and analyse variations of the classical Thompson sampling (TS)...
05/25/2017 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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An Online Hierarchical Algorithm for Extreme Clustering

Many modern clustering methods scale well to a large number of data item...
04/06/2017 ∙ by Ari Kobren, et al. ∙ 0 ∙ share

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Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...
03/03/2017 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Contextual Decision Processes with Low Bellman Rank are PAC-Learnable

This paper studies systematic exploration for reinforcement learning wit...
10/29/2016 ∙ by Nan Jiang, et al. ∙ 0 ∙ share

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Off-policy evaluation for slate recommendation

This paper studies the evaluation of policies that recommend an ordered ...
05/16/2016 ∙ by Adith Swaminathan, et al. ∙ 0 ∙ share

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Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

High-dimensional observations and complex real-world dynamics present ma...
03/14/2016 ∙ by David Abel, et al. ∙ 0 ∙ share

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PAC Reinforcement Learning with Rich Observations

We propose and study a new model for reinforcement learning with rich ob...
02/08/2016 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Minimax Structured Normal Means Inference

We provide a unified treatment of a broad class of noisy structure recov...
06/25/2015 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Extreme Compressive Sampling for Covariance Estimation

This paper studies the problem of estimating the covariance of a collect...
06/02/2015 ∙ by Martin Azizyan, et al. ∙ 0 ∙ share

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Contextual Semibandits via Supervised Learning Oracles

We study an online decision making problem where on each round a learner...
02/20/2015 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Learning to Search Better Than Your Teacher

Methods for learning to search for structured prediction typically imita...
02/08/2015 ∙ by Kai-Wei Chang, et al. ∙ 0 ∙ share

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Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations

We propose and analyze estimators for statistical functionals of one or ...
11/17/2014 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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On Estimating L_2^2 Divergence

We give a comprehensive theoretical characterization of a nonparametric ...
10/30/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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On the Power of Adaptivity in Matrix Completion and Approximation

We consider the related tasks of matrix completion and matrix approximat...
07/14/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Subspace Learning from Extremely Compressed Measurements

We consider learning the principal subspace of a large set of vectors fr...
04/03/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Nonparametric Estimation of Renyi Divergence and Friends

We consider nonparametric estimation of L_2, Renyi-α and Tsallis-α diver...
02/12/2014 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic

The detection of anomalous activity in graphs is a statistical problem t...
12/11/2013 ∙ by James Sharpnack, et al. ∙ 0 ∙ share

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Recovering Graph-Structured Activations using Adaptive Compressive Measurements

We study the localization of a cluster of activated vertices in a graph,...
05/01/2013 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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Akshay Krishnamurthy

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