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stat.ML2026
Inference for Newton Methods with Accelerated Sketch-and-Project via Random Scaling
Xinchen Du, Elizaveta Rebrova, Michał Dereziński +1
We study an online sketched Newton method that approximates the Newton direction at each step via a state-of-the-art sketching solver, called the generalized accelerated sketch-and…
stat.ML2025
Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
Yihang Gao, Michael K. Ng, Michael W. Mahoney +1
We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and…
stat.ML2025
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Xinchen Du, Wanrong Zhu, Wei Biao Wu +1
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biolo…