Dear Colleagues,
This is a reminder that the February webinar from ASA Statistical Learning and Data Science Section is this Tuesday. Dr. Mladen Kolar will discuss about solving nonlinear optimization problems with stochastic objective and deterministic constraints. Hope to see you there!
Title: Adaptive Stochastic Optimization with Constraints
Speakers: Dr. Mladen Kolar, Department of Data Sciences and Operations, University of Southern California
Date and Time: February 27, 2024, 1:00 to 2:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract: Constrained stochastic optimization problems appear widely in numerous applications in statistics, machine learning, and engineering, including constrained maximum likelihood estimation, constrained deep neural networks, physical-informed machine learning, and optimal control. I will discuss our recent work on solving nonlinear optimization problems with stochastic objective and deterministic constraints. I will describe development of adaptive algorithms based on sequential quadratic programming and their properties. The talk is based on the joint work with Yuchen Fang, Ilgee Hong, SenNa, Michael Mahoney, and Mihai Anitescu.
Presenter: Mladen Kolar is a professor in the Department of Data Sciences and Operations at the USC Marshall School of Business. Mladen earned his PhD in Machine Learning from Carnegie Mellon University in 2013. His research focuses on high-dimensional statistical methods, probabilistic graphical models, and scalable optimization methods, driven by the need to uncover interesting and scientifically meaningful structures from observational data. He currently serves as an associate editor for the Journal of Machine Learning Research, the Journal of Computational and Graphical Statistics, and the New England Journal of Statistics in Data Science.
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Zhihua Su, PhD
Associate Professor
Department of Statistics
University of Florida
zhihuasu@stat.ufl.edu------------------------------