Seminars

Stochastic simulations are increasingly used to describe complex systems with uncertainties. To better characterize the uncertainty of such a simulation,…

Influential entities can occur in social media, financial markets, energy transmission, you name it. In the presence of short time…

In causal inference, true causal order and the graph of causal interaction can be uniquely determined if you have sufficient…

Quantifying differences between probability distributions is central to statistics and machine learning, primarily for comparing statistical uncertainty. However, epistemic uncertainty—stemming…

General first-order methods (GFOMs), including various gradient descent variants and approximate message passing algorithms, constitute a broad class of iterative…

In today’s data-driven landscape, harnessing vast and varied datasets provides unparalleled opportunities for knowledge extraction and informed decision-making. Yet, within…

Controlling the False Discovery Rate (FDR) with finite sample guarantee in a variable selection procedure is important for trustworthy and…

Energy-based probabilistic models are enjoying renewed popularity, given their success in applications to language and image processing. Modern methods for…

We derive a Berry-Esseen bound for the multivariate normal approximation of the Polyak-Ruppert averaged iterates in the linear stochastic approximation…

Mean-field Langevin dynamics (MFLD) is an optimization method derived by taking the mean-field limit of noisy gradient descent for two-layer…