Alekh Agarwal

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Microsoft ∙ berkeley college

Researcher in the New York lab of Microsoft Research at Microsoft, Postdoctoral Researcher at Microsoft from 2012-2014, PhD in Computer Science from UC Berkeley

Bellman-consistent Pessimism for Offline Reinforcement Learning

The use of pessimism, when reasoning about datasets lacking exhaustive e...
06/13/2021 ∙ by Tengyang Xie, et al. ∙ 0 ∙ share

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Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation

Policy optimization methods are popular reinforcement learning algorithm...
03/24/2021 ∙ by Andrea Zanette, et al. ∙ 0 ∙ share

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Provably Correct Optimization and Exploration with Non-linear Policies

Policy optimization methods remain a powerful workhorse in empirical Rei...
03/22/2021 ∙ by Fei Feng, et al. ∙ 1 ∙ share

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Towards a Dimension-Free Understanding of Adaptive Linear Control

We study the problem of adaptive control of the linear quadratic regulat...
03/19/2021 ∙ by Juan C. Perdomo, et al. ∙ 0 ∙ share

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

The low rank MDP has emerged as an important model for studying represen...
02/14/2021 ∙ by Aditya Modi, et al. ∙ 0 ∙ share

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

Direct policy gradient methods for reinforcement learning are a successf...
07/16/2020 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Provably Good Batch Reinforcement Learning Without Great Exploration

Batch reinforcement learning (RL) is important to apply RL algorithms to...
07/16/2020 ∙ by Yao Liu, et al. ∙ 11 ∙ share

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Policy Improvement from Multiple Experts

Despite its promise, reinforcement learning's real-world adoption has be...
07/01/2020 ∙ by Ching-An Cheng, et al. ∙ 0 ∙ share

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Optimizing Interactive Systems via Data-Driven Objectives

Effective optimization is essential for real-world interactive systems t...
06/19/2020 ∙ by Ziming Li, 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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Reparameterized Variational Divergence Minimization for Stable Imitation

While recent state-of-the-art results for adversarial imitation-learning...
06/18/2020 ∙ by Dilip Arumugam, et al. ∙ 0 ∙ share

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Federated Residual Learning

We study a new form of federated learning where the clients train person...
03/28/2020 ∙ by Alekh Agarwal, et al. ∙ 4 ∙ share

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

We initiate the study of learning in contextual bandits with the help of...
03/04/2020 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Policy gradient methods are among the most effective methods in challeng...
08/01/2019 ∙ by Alekh Agarwal, et al. ∙ 3 ∙ share

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

A learned generative model often produces biased statistics relative to ...
06/23/2019 ∙ by Aditya Grover, et al. ∙ 8 ∙ share

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

This work considers the sample complexity of obtaining an ϵ-optimal poli...
06/10/2019 ∙ by Alekh Agarwal, 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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Fair Regression: Quantitative Definitions and Reduction-based Algorithms

In this paper, we study the prediction of a real-valued target, such as ...
05/30/2019 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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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...
05/12/2019 ∙ by Aditya Modi, et al. ∙ 17 ∙ share

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Off-Policy Policy Gradient with State Distribution Correction

We study the problem of off-policy policy optimization in Markov decisio...
04/17/2019 ∙ by Yao Liu, et al. ∙ 0 ∙ 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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Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...
01/02/2019 ∙ by Chicheng Zhang, et al. ∙ 6 ∙ 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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A Reductions Approach to Fair Classification

We present a systematic approach for achieving fairness in a binary clas...
03/06/2018 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Practical Contextual Bandits with Regression Oracles

A major challenge in contextual bandits is to design general-purpose alg...
03/03/2018 ∙ by Dylan J. Foster, 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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Hierarchical Imitation and Reinforcement Learning

We study the problem of learning policies over long time horizons. We pr...
03/01/2018 ∙ by Hoang M. Le, et al. ∙ 0 ∙ share

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Practical Evaluation and Optimization of Contextual Bandit Algorithms

We study and empirically optimize contextual bandit learning, exploratio...
02/12/2018 ∙ by Alberto Bietti, et al. ∙ 0 ∙ share

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Efficient Contextual Bandits in Non-stationary Worlds

Most contextual bandit algorithms minimize regret to the best fixed poli...
08/05/2017 ∙ by Haipeng Luo, 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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Corralling a Band of Bandit Algorithms

We study the problem of combining multiple bandit algorithms (that is, o...
12/19/2016 ∙ by Alekh Agarwal, 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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Efficient and Parsimonious Agnostic Active Learning

We develop a new active learning algorithm for the streaming setting sat...
06/29/2015 ∙ by Tzu-Kuo Huang, 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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A Lower Bound for the Optimization of Finite Sums

This paper presents a lower bound for optimizing a finite sum of n funct...
10/02/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Can we effectively learn a nonlinear representation in time comparable t...
10/02/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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

We present a new algorithm for the contextual bandit learning problem, w...
02/04/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Least Squares Revisited: Scalable Approaches for Multi-class Prediction

This work provides simple algorithms for multi-class (and multi-label) p...
10/07/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries

We consider the problem of learning overcomplete dictionaries in the con...
09/08/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Oracle inequalities for computationally adaptive model selection

We analyze general model selection procedures using penalized empirical ...
08/01/2012 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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

We develop and analyze stochastic optimization algorithms for problems i...
07/18/2012 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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A Reliable Effective Terascale Linear Learning System

We present a system and a set of techniques for learning linear predicto...
10/19/2011 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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The Generalization Ability of Online Algorithms for Dependent Data

We study the generalization performance of online learning algorithms tr...
10/11/2011 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Ergodic Mirror Descent

We generalize stochastic subgradient descent methods to situations in wh...
05/24/2011 ∙ by John C. Duchi, et al. ∙ 0 ∙ share

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Distributed Delayed Stochastic Optimization

We analyze the convergence of gradient-based optimization algorithms tha...
04/28/2011 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Fast global convergence of gradient methods for high-dimensional statistical recovery

Many statistical M-estimators are based on convex optimization problems ...
04/25/2011 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Alekh Agarwal

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