Modeling continuous dynamical systems from discretely sampled observations is a
fundamental problem in data science. Often, such dynamics are the result of non-local …

Bayesian optimization (BO) has established itself as a leading strategy for efficiently
optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian …

In this paper, we tackle the Bayesian estimation of point process intensity as a function of
covariates. We propose a novel augmentation of permanental process called augmented …

Survival or time-to-event data with time-varying covariates are common in practice, and
exploring the non-stationarity in covariates is essential to accurately analyzing the nonlinear …

Understanding the intricate workings of the brain remains one of science's grand
challenges. At its core, the brain operates as a dynamic system, with neural oscillations and …