Akshay Krishnamurthy

research

∙ 07/17/2022

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models

A person walking along a city street who tries to model all aspects of t...

Alex Lamb, et al. ∙

research

∙ 06/21/2022

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

We study reward-free reinforcement learning (RL) under general non-linea...

Jinglin Chen, et al. ∙

research

∙ 06/09/2022

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information

In real-world reinforcement learning applications the learner's observat...

Yonathan Efroni, et al. ∙

research

∙ 03/08/2022

A Sharp Characterization of Linear Estimators for Offline Policy Evaluation

Offline policy evaluation is a fundamental statistical problem in reinfo...

Juan C. Perdomo, et al. ∙

research

∙ 02/28/2022

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Contrastive learning is a popular form of self-supervised learning that ...

Nikunj Saunshi, et al. ∙

research

∙ 02/08/2022

Provable Reinforcement Learning with a Short-Term Memory

Real-world sequential decision making problems commonly involve partial ...

Yonathan Efroni, et al. ∙

research

∙ 11/21/2021

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation

We consider the offline reinforcement learning problem, where the aim is...

Dylan J. Foster, et al. ∙

research

∙ 11/08/2021

Universal and data-adaptive algorithms for model selection in linear contextual bandits

Model selection in contextual bandits is an important complementary prob...

Vidya Muthukumar, et al. ∙

research

∙ 10/21/2021

Anti-Concentrated Confidence Bonuses for Scalable Exploration

Intrinsic rewards play a central role in handling the exploration-exploi...

Jordan T. Ash, et al. ∙

research

∙ 10/17/2021

Provable RL with Exogenous Distractors via Multistep Inverse Dynamics

Many real-world applications of reinforcement learning (RL) require the ...

Yonathan Efroni, et al. ∙

research

∙ 10/12/2021

Sparsity in Partially Controllable Linear Systems

A fundamental concept in control theory is that of controllability, wher...

Yonathan Efroni, et al. ∙

research

∙ 07/05/2021

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination

A recurring theme in statistical learning, online learning, and beyond i...

Dylan J. Foster, et al. ∙

research

∙ 07/03/2021

Bayesian decision-making under misspecified priors with applications to meta-learning

Thompson sampling and other Bayesian sequential decision-making algorith...

Max Simchowitz, et al. ∙

research

∙ 06/18/2021

Investigating the Role of Negatives in Contrastive Representation Learning

Noise contrastive learning is a popular technique for unsupervised repre...

Jordan T. Ash, et al. ∙

research

∙ 06/17/2021

Gone Fishing: Neural Active Learning with Fisher Embeddings

There is an increasing need for effective active learning algorithms tha...

Jordan T. Ash, et al. ∙

research

∙ 02/14/2021

Model-free Representation Learning and Exploration in Low-rank MDPs

The low rank MDP has emerged as an important model for studying represen...

Aditya Modi, et al. ∙

research

∙ 10/08/2020

Learning the Linear Quadratic Regulator from Nonlinear Observations

We introduce a new problem setting for continuous control called the LQR...

Zakaria Mhammedi, et al. ∙

research

∙ 09/18/2020

Private Reinforcement Learning with PAC and Regret Guarantees

Motivated by high-stakes decision-making domains like personalized medic...

Giuseppe Vietri, et al. ∙

research

∙ 08/24/2020

Contrastive learning, multi-view redundancy, and linear models

Self-supervised learning is an empirically successful approach to unsupe...

Christopher Tosh, et al. ∙

research

∙ 06/22/2020

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Partial observability is a common challenge in many reinforcement learni...

Chi Jin, et al. ∙

research

∙ 06/22/2020

Information Theoretic Regret Bounds for Online Nonlinear Control

This work studies the problem of sequential control in an unknown, nonli...

Sham Kakade, et al. ∙

research

∙ 06/19/2020

Open Problem: Model Selection for Contextual Bandits

In statistical learning, algorithms for model selection allow the learne...

Dylan J. Foster, et al. ∙

research

∙ 06/18/2020

Provably adaptive reinforcement learning in metric spaces

We study reinforcement learning in continuous state and action spaces en...

Tongyi Cao, et al. ∙

research

∙ 06/18/2020

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

In order to deal with the curse of dimensionality in reinforcement learn...

Alekh Agarwal, et al. ∙

research

∙ 06/10/2020

Efficient Contextual Bandits with Continuous Actions

We create a computationally tractable algorithm for contextual bandits w...

Maryam Majzoubi, et al. ∙

research

∙ 03/04/2020

Contrastive estimation reveals topic posterior information to linear models

Contrastive learning is an approach to representation learning that util...

Christopher Tosh, et al. ∙

research

∙ 02/26/2020

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

Standard game-theoretic formulations for settings like contextual pricin...

Akshay Krishnamurthy, et al. ∙

research

∙ 02/18/2020

Adaptive Estimator Selection for Off-Policy Evaluation

We develop a generic data-driven method for estimator selection in off-p...

Yi Su, et al. ∙

research

∙ 02/07/2020

Reward-Free Exploration for Reinforcement Learning

Exploration is widely regarded as one of the most challenging aspects of...

Chi Jin, et al. ∙

research

∙ 01/19/2020

Algebraic and Analytic Approaches for Parameter Learning in Mixture Models

We present two different approaches for parameter learning in several mi...

Akshay Krishnamurthy, et al. ∙

research

∙ 12/31/2019

Scalable Hierarchical Clustering with Tree Grafting

We introduce Grinch, a new algorithm for large-scale, non-greedy hierarc...

Nicholas Monath, et al. ∙

research

∙ 12/09/2019

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

We design a new provably efficient algorithm for episodic reinforcement ...

Yining Wang, et al. ∙

research

∙ 11/13/2019

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

We present an algorithm, HOMER, for exploration and reinforcement learni...

Dipendra Misra, et al. ∙

research

∙ 10/30/2019

Sample Complexity of Learning Mixtures of Sparse Linear Regressions

In the problem of learning mixtures of linear regressions, the goal is t...

Akshay Krishnamurthy, et al. ∙

research

∙ 10/09/2019

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

We consider the dynamic assortment optimization problem under the multin...

Xi Chen, et al. ∙

research

∙ 07/22/2019

Doubly robust off-policy evaluation with shrinkage

We design a new family of estimators for off-policy evaluation in contex...

Yi Su, et al. ∙

research

∙ 06/09/2019

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

We design a new algorithm for batch active learning with deep neural net...

Jordan T. Ash, et al. ∙

research

∙ 06/03/2019

Model selection for contextual bandits

We introduce the problem of model selection for contextual bandits, wher...

Dylan J. Foster, et al. ∙

research

∙ 04/21/2019

Trace Reconstruction: Generalized and Parameterized

In the beautifully simple-to-state problem of trace reconstruction, the ...

Akshay Krishnamurthy, et al. ∙

research

∙ 02/05/2019

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

We study contextual bandit learning with an abstract policy class and co...

Akshay Krishnamurthy, et al. ∙

research

∙ 01/25/2019

Provably efficient RL with Rich Observations via Latent State Decoding

We study the exploration problem in episodic MDPs with rich observations...

Simon S. Du, et al. ∙

research

∙ 11/21/2018

Model-Based Reinforcement Learning in Contextual Decision Processes

We study the sample complexity of model-based reinforcement learning in ...

Wen Sun, et al. ∙

research

∙ 06/28/2018

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms

We introduce a new family of margin-based regret guarantees for adversar...

Dylan J. Foster, et al. ∙

research

∙ 05/25/2018

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...

Kirthevasan Kandasamy, et al. ∙

research

∙ 03/12/2018

Semiparametric Contextual Bandits

This paper studies semiparametric contextual bandits, a generalization o...

Akshay Krishnamurthy, et al. ∙

research

∙ 03/01/2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations

We study the computational tractability of provably sample-efficient (PA...

Christoph Dann, et al. ∙

research

∙ 11/21/2017

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

We design new algorithms for the combinatorial pure exploration problem ...

Tongyi Cao, et al. ∙

research

∙ 11/15/2017

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...

Rajarshi Das, et al. ∙

research

∙ 05/25/2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

We design and analyse variations of the classical Thompson sampling (TS)...

Kirthevasan Kandasamy, et al. ∙

research

∙ 04/06/2017

An Online Hierarchical Algorithm for Extreme Clustering

Many modern clustering methods scale well to a large number of data item...

Ari Kobren, et al. ∙

Akshay Krishnamurthy

Featured Co-authors