Seminars

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…

This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to…

Traditional analysis of a periodic time series assumes its pattern remains the same. However, some recent empirical studies in climatology…

Subtitle: From the Perspective of Statistical Education Required at Universities In recent years, the importance of data science education at…