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
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
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 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
New Perspectives on Data-Driven Optimization and Neural Network Uncertainty Quantification I will talk about some recent methodologies to understand and quantify the impact of data in relation to optimization and simulation. In the first part of the talk, we create … Read more
Active Label Acquisition in Predict-then- Optimize Framework: Feature-dependent Values of Data Points When collecting data for decision-making, the informativeness of the data is crucial. In the predict-then-optimize framework, a common method for obtaining personalized decisions, training a prediction model with … Read more
Holistic Robust Data-Driven Decisions via Distributionally Robust Optimization The design of data-driven formulations for decision-making and machine learning with good out-of-sample performance is a key challenge. The observation that good in-sample performance (training) does not guarantee good out-of-sample performance (deployment) … Read more
Advice-Augmented Algorithms for Online Matching and Resource Allocation Real life problems are full of uncertainty. How we handle it is important, since it affects the design and performance of algorithms. Often, the uncertainty is assumed to follow some known distribution, … 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
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
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
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
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
Mike Baiocchi Department of Epidemiology and Population Health Stanford University
