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Found 16 papers, 2 papers with code
Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis
Our main result is a non-asymptotic guarantee for VRPG algorithm.
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error.
Adaptive Linear Estimating Equations
Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes.
Semi-parametric inference based on adaptively collected data
Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals.
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning
As a consequence, we propose a data-dependent stopping rule for instance-optimal algorithms.
Optimal variance-reduced stochastic approximation in Banach spaces
We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.
Near-optimal inference in adaptive linear regression
We propose a family of online debiasing estimators to correct these distributional anomalies in least squares estimation.
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning
Various algorithms in reinforcement learning exhibit dramatic variability in their convergence rates and ultimate accuracy as a function of the problem structure.
Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case.
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis
We address the problem of policy evaluation in discounted Markov decision processes, and provide instance-dependent guarantees on the $\ell_\infty$-error under a generative model.
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i. i. d.
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback.
Theoretical guarantees for EM under misspecified Gaussian mixture models
We provide two classes of theoretical guarantees: first, we characterize the bias introduced due to the misspecification; and second, we prove that population EM converges at a geometric rate to the model projection under a suitable initialization condition.
Singularity, Misspecification, and the Convergence Rate of EM
A line of recent work has analyzed the behavior of the Expectation-Maximization (EM) algorithm in the well-specified setting, in which the population likelihood is locally strongly concave around its maximizing argument.
Convergence guarantees for a class of non-convex and non-smooth optimization problems
We also show that our algorithms can escape strict saddle points for a class of non-smooth functions, thereby generalizing known results for smooth functions.
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis
Factor analysis, a classical multivariate statistical technique is popularly used as a fundamental tool for dimensionality reduction in statistics, econometrics and data science.
