Abstract: Generative models such as normalizing flows and diffusion processes have transformed how we represent complex, high-dimensional data. In this talk, I introduce a framework for worst-case generation formulated as a minimax optimization problem in Wasserstein space. This perspective reveals a unified view of risk-induced generation and distributional robustness, showing that worst-case distributions arise …
Abstract: Random matrix theory is a large area with a long history, with elegant theory and a wide range of applications. However, the challenges of modern machine learning are forcing us not only to use random matrix theory in new ways, and also to chart out new directions for theory. …
Abstract: The proliferation of open-weight models trained on vast, public datasets (that often include sensitive data) introduces a critical privacy challenge: how do we erase the influence of sensitive incorrect, or obsolete data after a model is pre-trained? While machine unlearning offers a promising direction, we argue that current methods …
Abstract: Markov Chain Monte Carlo (MCMC) and local-search optimization methods have been extensively used in the practice of statistical research for many decades now. However, their exact theoretical performance has been strikingly eluding even for simple parametric estimation tasks. This is in stark contrast to other classes of estimators such …
Abstract: Mixed integer quadratic programming is the problem of minimizing a quadratic polynomial over points in a polyhedral region with some integer components. It is a natural extension of mixed integer linear programming, and it has a wide array of applications. In this talk, I will survey some recent theoretical …
Abstract: Vintage factor analysis is one important type of factor analysis that aims to first find a low-dimensional representation of the original data, and then to seek a rotation such that the rotated low-dimensional representation is scientifically meaningful. The most widely used vintage factor analysis is the Principal Component Analysis …
Abstract: In this talk I will describe recent progress in characterizing structure that emerges during the training of deep neural networks for classification. Neural Collapse is a phenomenon that emerges during the training of deep classifiers in which the top-layer feature embeddings of samples from the same class tend to …
Many applications concern sparse signals, for example, detecting anomalies from the differences between consecutive images taken by surveillance cameras. In general, anomaly events are sparse. This talk focuses on the problem of recovering a K-sparse signal in N dimensions (coordinates). Classical theories in compressed sensing say the required number of …