Martin J. Wainwright

research

∙ 09/14/2023

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

Key challenges in running a retail business include how to select produc...

0 Junhui Cai, et al. ∙

research

∙ 09/04/2023

Challenges of the inconsistency regime: Novel debiasing methods for missing data models

We study semi-parametric estimation of the population mean when data is ...

0 Michael Celentano, et al. ∙

research

∙ 03/22/2023

Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints

Estimation problems with constrained parameter spaces arise in various s...

0 Reese Pathak, et al. ∙

research

∙ 03/05/2023

Semi-parametric inference based on adaptively collected data

Many standard estimators, when applied to adaptively collected data, fai...

0 Licong Lin, et al. ∙

research

∙ 11/07/2022

Policy evaluation from a single path: Multi-step methods, mixing and mis-specification

We study non-parametric estimation of the value function of an infinite-...

0 Yaqi Duan, et al. ∙

research

∙ 10/20/2022

Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces

We present and analyze the Krylov-Bellman Boosting (KBB) algorithm for p...

0 Eric Xia, et al. ∙

research

∙ 10/09/2022

QuTE: decentralized multiple testing on sensor networks with false discovery rate control

This paper designs methods for decentralized multiple hypothesis testing...

0 Aaditya Ramdas, et al. ∙

research

∙ 09/26/2022

Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency

The problem of estimating a linear functional based on observational dat...

0 Wenlong Mou, et al. ∙

research

∙ 06/01/2022

Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning

The Q-learning algorithm is a simple and widely-used stochastic approxim...

0 Andrea Zanette, et al. ∙

research

∙ 05/06/2022

Optimally tackling covariate shift in RKHS-based nonparametric regression

We study the covariate shift problem in the context of nonparametric reg...

0 Cong Ma, et al. ∙

research

∙ 03/24/2022

Bellman Residual Orthogonalization for Offline Reinforcement Learning

We introduce a new reinforcement learning principle that approximates th...

0 Andrea Zanette, et al. ∙

research

∙ 02/06/2022

A new similarity measure for covariate shift with applications to nonparametric regression

We study covariate shift in the context of nonparametric regression. We ...

0 Reese Pathak, et al. ∙

research

∙ 01/21/2022

Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

Various algorithms for reinforcement learning (RL) exhibit dramatic vari...

3 Koulik Khamaru, et al. ∙

research

∙ 01/21/2022

Optimal variance-reduced stochastic approximation in Banach spaces

We study the problem of estimating the fixed point of a contractive oper...

8 Wenlong Mou, et al. ∙

research

∙ 12/23/2021

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation

We study stochastic approximation procedures for approximately solving a...

0 Wenlong Mou, et al. ∙

research

∙ 09/24/2021

Optimal policy evaluation using kernel-based temporal difference methods

We study methods based on reproducing kernel Hilbert spaces for estimati...

1 Yaqi Duan, et al. ∙

research

∙ 08/19/2021

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning

Actor-critic methods are widely used in offline reinforcement learning p...

0 Andrea Zanette, et al. ∙

research

∙ 07/05/2021

Near-optimal inference in adaptive linear regression

When data is collected in an adaptive manner, even simple methods like o...

0 Koulik Khamaru, et al. ∙

research

∙ 06/28/2021

Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning

Various algorithms in reinforcement learning exhibit dramatic variabilit...

5 Koulik Khamaru, et al. ∙

research

∙ 05/05/2021

Preference learning along multiple criteria: A game-theoretic perspective

The literature on ranking from ordinal data is vast, and there are sever...

0 Kush Bhatia, et al. ∙

research

∙ 01/19/2021

Minimax Off-Policy Evaluation for Multi-Armed Bandits

We study the problem of off-policy evaluation in the multi-armed bandit ...

0 Cong Ma, et al. ∙

research

∙ 12/09/2020

Optimal oracle inequalities for solving projected fixed-point equations

Linear fixed point equations in Hilbert spaces arise in a variety of set...

0 Wenlong Mou, et al. ∙

research

∙ 08/28/2020

ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm

The theory and practice of stochastic optimization has focused on stocha...

19 Chris Junchi Li, et al. ∙

research

∙ 06/17/2020

Revisiting complexity and the bias-variance tradeoff

The recent success of high-dimensional models, such as deep neural netwo...

26 Raaz Dwivedi, et al. ∙

research

∙ 05/22/2020

Instability, Computational Efficiency and Statistical Accuracy

Many statistical estimators are defined as the fixed point of a data-dep...

13 Nhat Ho, et al. ∙

research

∙ 05/11/2020

FedSplit: An algorithmic framework for fast federated optimization

Motivated by federated learning, we consider the hub-and-spoke model of ...

0 Reese Pathak, et al. ∙

research

∙ 05/07/2020

Lower bounds in multiple testing: A framework based on derandomized proxies

The large bulk of work in multiple testing has focused on specifying pro...

36 Max Rabinovich, et al. ∙

research

∙ 04/09/2020

On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

We undertake a precise study of the asymptotic and non-asymptotic proper...

3 Wenlong Mou, et al. ∙

research

∙ 03/16/2020

Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis

We address the problem of policy evaluation in discounted Markov decisio...

21 Koulik Khamaru, et al. ∙

research

∙ 12/11/2019

Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing

We study the problem of sampling from the power posterior distribution i...

27 Wenlong Mou, et al. ∙

research

∙ 10/01/2019

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

We consider the problem of sampling from a density of the form p(x) ∝(-f...

24 Wenlong Mou, et al. ∙

research

∙ 09/19/2019

Value function estimation in Markov reward processes: Instance-dependent ℓ_∞-bounds for policy evaluation

Markov reward processes (MRPs) are used to model stochastic phenomena ar...

0 Ashwin Pananjady, et al. ∙

research

∙ 09/03/2019

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

We show that diffusion processes can be exploited to study the posterior...

0 Wenlong Mou, et al. ∙

research

∙ 08/28/2019

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-or...

12 Wenlong Mou, et al. ∙

research

∙ 07/25/2019

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

We present an improved analysis of the Euler-Maruyama discretization of ...

1 Wenlong Mou, et al. ∙

research

∙ 06/11/2019

Variance-reduced Q-learning is minimax optimal

We introduce and analyze a form of variance-reduced Q-learning. For γ-di...

0 Martin J. Wainwright, et al. ∙

research

∙ 05/29/2019

Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients

Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte C...

0 Yuansi Chen, et al. ∙

research

∙ 05/15/2019

Stochastic approximation with cone-contractive operators: Sharp ℓ_∞-bounds for Q-learning

Motivated by the study of Q-learning algorithms in reinforcement learnin...

0 Martin J. Wainwright, et al. ∙

research

∙ 02/01/2019

Challenges with EM in application to weakly identifiable mixture models

We study a class of weakly identifiable location-scale mixture models fo...

0 Raaz Dwivedi, et al. ∙

research

∙ 12/20/2018

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

We study derivative-free methods for policy optimization over the class ...

0 Dhruv Malik, et al. ∙

research

∙ 10/01/2018

Singularity, Misspecification, and the Convergence Rate of EM

A line of recent work has characterized the behavior of the EM algorithm...

0 Raaz Dwivedi, et al. ∙

research

∙ 08/08/2018

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

We study instancewise feature importance scoring as a method for model i...

0 Jianbo Chen, et al. ∙

research

∙ 06/25/2018

Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations

Many applications, including rank aggregation, crowd-labeling, and graph...

0 Cheng Mao, et al. ∙

research

∙ 04/25/2018

Convergence guarantees for a class of non-convex and non-smooth optimization problems

We consider the problem of finding critical points of functions that are...

0 Koulik Khamaru, et al. ∙

research

∙ 03/21/2018

From Gauss to Kolmogorov: Localized Measures of Complexity for Ellipses

The Gaussian width is a fundamental quantity in probability, statistics ...

0 Yuting Wei, et al. ∙

research

∙ 02/27/2018

Breaking the 1/√(n) Barrier: Faster Rates for Permutation-based Models in Polynomial Time

Many applications, including rank aggregation and crowd-labeling, can be...

0 Cheng Mao, et al. ∙

research

∙ 02/21/2018

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

We introduce instancewise feature selection as a methodology for model i...

0 Jianbo Chen, et al. ∙

research

∙ 01/08/2018

Log-concave sampling: Metropolis-Hastings algorithms are fast!

We consider the problem of sampling from a strongly log-concave density ...

0 Raaz Dwivedi, et al. ∙

research

∙ 01/04/2018

Approximate Ranking from Pairwise Comparisons

A common problem in machine learning is to rank a set of n items based o...

0 Reinhard Heckel, et al. ∙

research

∙ 12/03/2017

The local geometry of testing in ellipses: Tight control via localized Kolmogorov widths

We study the local geometry of testing a mean vector within a high-dimen...

0 Yuting Wei, et al. ∙

Martin J. Wainwright

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