Instructor: UW-Madison
SILO: Few-Step Likelihoods of Flow and Diffusion Models: Applications to Images and Molecules
Abstract Flow and diffusion models define processes that generate samples from progressively less noisy versions of the data distribution. I will present a line of work on estimating the marginal probability densities along these processes, culminating in the density of the data distribution itself. Our methods are both sample-efficient to …
SILO: Dynamic Assortment Optimization: Fluid Relaxations, Submodularity, and Approximation Algorithms
Abstract Assortment and inventory decisions lie at the core of supply chain and retail operations. In many settings, these decisions are complicated by customer substitution: when a preferred product is unavailable, customers may switch to other products, and as inventory is depleted over time, the demand faced by the remaining …
SILO: Uncertainty Quantification for Foundation Models under Data and Compute Constraints
Abstract Foundation models are increasingly used in settings where labeled data are scarce and adaptation must be fast and lightweight. But scarce data must often do double duty: we need labeled examples both to adapt a foundation model to a new task and to evaluate how much we can trust …
SILO: Highlights from UW–Madison’s data science partnership with American Family Insurance
Welcome: Ramya Korlakai Vinayak, Assistant Professor, Electrical and Computer Engineering Talk 1 Title: Safer Driving Through Optimized Telematics-Based Feedback Presenters: Fengxu Li, undergraduate student, Industrial and Systems Engineering & Rahul Shenoy, PhD student, Industrial and Systems Engineering Abstract: This study evaluated a behavioral analytics algorithm that delivered personalized, telematics-based nudges …
SILO: Bayesian Optimization Beyond the Black Box: Leveraging Computational Structure for Efficient and Scalable Decision-Making
Abstract Bayesian optimization (BO) is a principled framework for optimizing expensive, noisy objective functions, but traditional BO treats the system as a black box and learns only through input-output queries. In many scientific and engineering settings, this assumption is unnecessarily restrictive; valuable computational structure is often available, even if the …
SILO: First-Order Algorithms for Large-Scale Optimization
Abstract: It is well known that for nonconvex unconstrained optimization with Lipschitz smoothness, gradient descent and stochastic gradient descent are the optimal first-order algorithms in the deterministic and stochastic settings, respectively. This naturally raises two questions: In the constrained setting, is it possible to design algorithms that achieve the same …
SILO: Searching for architectures and BERT moments in specialized AI applications
Abstract: In 2018, advances in architecture design and self-supervised learning led to the “BERT moment” in natural language processing, in which supervised learning workflows were permanently supplanted by the pretraining and fine-tuning of massive Transformer models. This spurred scientists in more specialized areas—e.g. genomics, satellite imaging, and time series forecasting—to develop …
SILO: Stable Estimators for Fast Private Statistics
Abstract: We will discuss a new set of techniques for stable statistical estimation, leading to fast and near-optimal private algorithms for mean estimation, covariance estimation, and linear regression. The analysis proceeds by constructing a stabilizing wrapper around a greedy outlier-removal process. We will also discuss connections with a recent line …