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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...
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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 ...
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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...
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03/05/2023
Semi-parametric inference based on adaptively collected data
Many standard estimators, when applied to adaptively collected data, fai...
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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-...
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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...
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10/09/2022
QuTE: decentralized multiple testing on sensor networks with false discovery rate control
This paper designs methods for decentralized multiple hypothesis testing...
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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...
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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...
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05/06/2022
Optimally tackling covariate shift in RKHS-based nonparametric regression
We study the covariate shift problem in the context of nonparametric reg...
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03/24/2022
Bellman Residual Orthogonalization for Offline Reinforcement Learning
We introduce a new reinforcement learning principle that approximates th...
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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 ...
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01/21/2022
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning
Various algorithms for reinforcement learning (RL) exhibit dramatic vari...
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01/21/2022
Optimal variance-reduced stochastic approximation in Banach spaces
We study the problem of estimating the fixed point of a contractive oper...
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12/23/2021
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
We study stochastic approximation procedures for approximately solving a...
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09/24/2021
Optimal policy evaluation using kernel-based temporal difference methods
We study methods based on reproducing kernel Hilbert spaces for estimati...
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08/19/2021
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Actor-critic methods are widely used in offline reinforcement learning p...
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07/05/2021
Near-optimal inference in adaptive linear regression
When data is collected in an adaptive manner, even simple methods like o...
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06/28/2021
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning
Various algorithms in reinforcement learning exhibit dramatic variabilit...
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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...
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01/19/2021
Minimax Off-Policy Evaluation for Multi-Armed Bandits
We study the problem of off-policy evaluation in the multi-armed bandit ...
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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...
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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...
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06/17/2020
Revisiting complexity and the bias-variance tradeoff
The recent success of high-dimensional models, such as deep neural netwo...
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05/22/2020
Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a data-dep...
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05/11/2020
FedSplit: An algorithmic framework for fast federated optimization
Motivated by federated learning, we consider the hub-and-spoke model of ...
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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...
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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...
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03/16/2020
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis
We address the problem of policy evaluation in discounted Markov decisio...
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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...
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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...
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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...
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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...
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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...
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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 ...
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06/11/2019
Variance-reduced Q-learning is minimax optimal
We introduce and analyze a form of variance-reduced Q-learning. For γ-di...
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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...
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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...
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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...
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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 ...
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10/01/2018
Singularity, Misspecification, and the Convergence Rate of EM
A line of recent work has characterized the behavior of the EM algorithm...
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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...
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06/25/2018
Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations
Many applications, including rank aggregation, crowd-labeling, and graph...
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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...
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03/21/2018
From Gauss to Kolmogorov: Localized Measures of Complexity for Ellipses
The Gaussian width is a fundamental quantity in probability, statistics ...
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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...
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02/21/2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
We introduce instancewise feature selection as a methodology for model i...
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01/08/2018
Log-concave sampling: Metropolis-Hastings algorithms are fast!
We consider the problem of sampling from a strongly log-concave density ...
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01/04/2018
Approximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based o...
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12/03/2017