Seminars

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…

Estimating the full (conditional) distribution is crucial to many applications. However, existing methods such as quantile regression typically struggle with…

Modern data science pipelines are often severely constrained in resources, both statistical (e.g., poor-quality input data due to outliers) as…