b'Alekh Agarwal'

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

∙ 05/29/2022

Provable Benefits of Representational Transfer in Reinforcement Learning

We study the problem of representational transfer in RL, where an agent ...

Alekh Agarwal, et al. ∙

research

∙ 02/05/2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning

We propose Adversarially Trained Actor Critic (ATAC), a new model-free a...

Ching-An Cheng, et al. ∙

research

∙ 01/31/2022

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach

We present BRIEE (Block-structured Representation learning with Interlea...

Xuezhou Zhang, 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

∙ 06/13/2021

Bellman-consistent Pessimism for Offline Reinforcement Learning

The use of pessimism, when reasoning about datasets lacking exhaustive e...

Tengyang Xie, et al. ∙

research

∙ 03/24/2021

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation

Policy optimization methods are popular reinforcement learning algorithm...

Andrea Zanette, et al. ∙

research

∙ 03/22/2021

Provably Correct Optimization and Exploration with Non-linear Policies

Policy optimization methods remain a powerful workhorse in empirical Rei...

Fei Feng, et al. ∙

research

∙ 03/19/2021

Towards a Dimension-Free Understanding of Adaptive Linear Control

We study the problem of adaptive control of the linear quadratic regulat...

Juan C. Perdomo, 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

∙ 07/16/2020

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Direct policy gradient methods for reinforcement learning are a successf...

Alekh Agarwal, et al. ∙

research

∙ 07/16/2020

Provably Good Batch Reinforcement Learning Without Great Exploration

Batch reinforcement learning (RL) is important to apply RL algorithms to...

Yao Liu, et al. ∙

research

∙ 07/01/2020

Policy Improvement from Multiple Experts

Despite its promise, reinforcement learning's real-world adoption has be...

Ching-An Cheng, et al. ∙

research

∙ 06/19/2020

Optimizing Interactive Systems via Data-Driven Objectives

Effective optimization is essential for real-world interactive systems t...

Ziming Li, 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

∙ 06/18/2020

Reparameterized Variational Divergence Minimization for Stable Imitation

While recent state-of-the-art results for adversarial imitation-learning...

Dilip Arumugam, et al. ∙

research

∙ 03/28/2020

Federated Residual Learning

We study a new form of federated learning where the clients train person...

Alekh Agarwal, et al. ∙

research

∙ 03/04/2020

Taking a hint: How to leverage loss predictors in contextual bandits?

We initiate the study of learning in contextual bandits with the help of...

Chen-Yu Wei, et al. ∙

research

∙ 08/01/2019

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Policy gradient methods are among the most effective methods in challeng...

Alekh Agarwal, et al. ∙

research

∙ 06/23/2019

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

A learned generative model often produces biased statistics relative to ...

Aditya Grover, et al. ∙

research

∙ 06/10/2019

On the Optimality of Sparse Model-Based Planning for Markov Decision Processes

This work considers the sample complexity of obtaining an ϵ-optimal poli...

Alekh Agarwal, 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

∙ 05/30/2019

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

In this paper, we study the prediction of a real-valued target, such as ...

Alekh Agarwal, et al. ∙

research

∙ 05/12/2019

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Assemblies of modular subsystems are being pressed into service to perfo...

Aditya Modi, et al. ∙

research

∙ 04/17/2019

Off-Policy Policy Gradient with State Distribution Correction

We study the problem of off-policy policy optimization in Markov decisio...

Yao Liu, 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

∙ 01/02/2019

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...

Chicheng Zhang, 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

∙ 03/06/2018

A Reductions Approach to Fair Classification

We present a systematic approach for achieving fairness in a binary clas...

Alekh Agarwal, et al. ∙

research

∙ 03/03/2018

Practical Contextual Bandits with Regression Oracles

A major challenge in contextual bandits is to design general-purpose alg...

Dylan J. Foster, 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

∙ 03/01/2018

Hierarchical Imitation and Reinforcement Learning

We study the problem of learning policies over long time horizons. We pr...

Hoang M. Le, et al. ∙

research

∙ 02/12/2018

Practical Evaluation and Optimization of Contextual Bandit Algorithms

We study and empirically optimize contextual bandit learning, exploratio...

Alberto Bietti, et al. ∙

research

∙ 08/05/2017

Efficient Contextual Bandits in Non-stationary Worlds

Most contextual bandit algorithms minimize regret to the best fixed poli...

Haipeng Luo, et al. ∙

research

∙ 03/03/2017

Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...

Akshay Krishnamurthy, et al. ∙

research

∙ 12/19/2016

Corralling a Band of Bandit Algorithms

We study the problem of combining multiple bandit algorithms (that is, o...

Alekh Agarwal, et al. ∙

research

∙ 10/29/2016

Contextual Decision Processes with Low Bellman Rank are PAC-Learnable

This paper studies systematic exploration for reinforcement learning wit...

Nan Jiang, et al. ∙

research

∙ 05/16/2016

Off-policy evaluation for slate recommendation

This paper studies the evaluation of policies that recommend an ordered ...

Adith Swaminathan, et al. ∙

research

∙ 03/14/2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

High-dimensional observations and complex real-world dynamics present ma...

David Abel, et al. ∙

research

∙ 02/08/2016

PAC Reinforcement Learning with Rich Observations

We propose and study a new model for reinforcement learning with rich ob...

Akshay Krishnamurthy, et al. ∙

research

∙ 06/29/2015

Efficient and Parsimonious Agnostic Active Learning

We develop a new active learning algorithm for the streaming setting sat...

Tzu-Kuo Huang, et al. ∙

research

∙ 02/20/2015

Contextual Semibandits via Supervised Learning Oracles

We study an online decision making problem where on each round a learner...

Akshay Krishnamurthy, et al. ∙

research

∙ 02/08/2015

Learning to Search Better Than Your Teacher

Methods for learning to search for structured prediction typically imita...

Kai-Wei Chang, et al. ∙

research

∙ 10/02/2014

A Lower Bound for the Optimization of Finite Sums

This paper presents a lower bound for optimizing a finite sum of n funct...

Alekh Agarwal, et al. ∙

research

∙ 10/02/2014

Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Can we effectively learn a nonlinear representation in time comparable t...

Alekh Agarwal, et al. ∙

research

∙ 02/04/2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

We present a new algorithm for the contextual bandit learning problem, w...

Alekh Agarwal, et al. ∙

research

∙ 10/07/2013

Least Squares Revisited: Scalable Approaches for Multi-class Prediction

This work provides simple algorithms for multi-class (and multi-label) p...

Alekh Agarwal, et al. ∙

research

∙ 09/08/2013

A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries

We consider the problem of learning overcomplete dictionaries in the con...

Alekh Agarwal, et al. ∙

research

∙ 08/01/2012

Oracle inequalities for computationally adaptive model selection

We analyze general model selection procedures using penalized empirical ...

Alekh Agarwal, et al. ∙

research

∙ 07/18/2012

Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions

We develop and analyze stochastic optimization algorithms for problems i...

Alekh Agarwal, et al. ∙

Alekh Agarwal

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