Stat.ML

Inference of Online Newton Methods with Nesterov's Accelerated Sketching
Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order …
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A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
In this paper, we propose a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method for solving …
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Derivative-Free Sequential Quadratic Programming for Equality-Constrained Stochastic Optimization
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints, assuming that …
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High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise
In this paper, we consider nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We …
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Online Covariance Matrix Estimation in Sketched Newton Methods
Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods …
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Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. …
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Online Statistical Inference for Proximal Stochastic Gradient Descent under Markovian Sampling
Nonsmooth stochastic optimization has emerged as a fundamental framework for modeling complex machine learning problems, particularly …
Statistical Inference of Constrained Model Estimation via Derivative-Free Stochastic Sequential Quadratic Programming
We propose a derivative-free stochastic sequential quadratic programming (DF-SSQP) method for solving nonlinear equality-constrained …
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex …
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Online Covariance Estimation in Nonsmooth Stochastic Approximation
We consider applying stochastic approximation (SA) methods to solve nonsmooth variational inclusion problems. Existing studies have …
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