120 Hanes Hall

Mike Baiocchi Department of Epidemiology and Population Health Stanford University

Generative modeling with trees and recursive partitions Trees and recursive partitions are most well-known in supervised learning for predictive tasks, such as regression and classification. Famous examples include CART and its various forms of ensembles—e.g., random forest and boosting. A … Read more

Sara Shashaani Department of Industrial and Systems Engineering North Carolina State University   Title: Adaptive Sampling with Trust-region Optimization for Nonconvex Stochastic Functions. Abstract: Simulation Optimization problems are often non-convex, derivative-free, and with stochastic noise that may be notably heteroskedastic. There is … Read more

Sajad Mordaresi Kenan-Flagler Business School University of Northing Carolina at Chapel Hill   Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences Abstract We study the dynamic assortment personalization problem of an online retailer that adaptively customizes assortments based on … Read more

TITLE: Generative Quantile Regression with Variability Penalty ABSTRACT: Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this talk, we introduce a deep learning generative model for joint quantile estimation … Read more

Peter Song, Department of Biostatistics, University of Michigan Title: Supervised Homogeneity Pursuit via Mixed Integer Optimization Abstract: Stratification is one statistical principle in data processing to mitigate the underlying population heterogeneity, which is typically handled by clustering when stratum labels are … Read more

Phase Transitions of Random Constraint Satisfaction Problems The framework of constraint satisfaction problems (CSPs) captures many fundamental problems in combinatorics and computer science, such as finding a proper coloring of a graph or solving Boolean satisfiability problems. To study the … Read more

Spherical Spin Glasses and Stochastic Gradient Descent This talk will focus on two strands of my recent research – spin glasses and stochastic gradient descent (SGD). While the contexts are different, both draw on tools from high-dimensional probability to study … Read more

Phase Transitions in Stochastic Many-Player Games My research addresses stochastic games with many players, optimal control with partial information, and, recently, optimization with random matrices. This seminar focuses on a stochastic game where the players exhibit qualitatively distinct behaviors in … Read more

Equations on Wasserstein Space and Applications to Stochastic Mean Field Control and Games The study of controlled multi-agent systems has seen increased interest in recent years, due to their ubiquity in applications coming from macroeconomics, social behavior, and telecommunications. When … Read more

Random growth models and the KPZ universality Many two-dimensional random growth models, including first- and last-passage percolation, are conjectured to fall within the KPZ universality class under mild assumptions on the underlying noise. In recent years, researchers have focused on … Read more

Optimal Scheduling of Elastic and Inelastic Jobs A wide range of modern computer systems rely on parallelism to process jobs quickly. Unlike the jobs considered in most classical scheduling problems, parallelizable jobs can be completed more quickly when they are … Read more