Bayesian (machine) learning has been playing a significant role in machine learning for a
long time due to its particular ability to embrace uncertainty, encode prior knowledge, and …

The foundation of the subject of nonparametric Bayesian inference was laid in two technical
reports: a 1969 UCLA report by Thomas S. Ferguson (later published in 1973 as a paper in …

Dependent nonparametric processes extend distributions over measures, such as the
Dirichlet process and the beta process, to give distributions over collections of measures …

Latent feature models are widely used to decompose data into a small number of
components. Bayesian nonparametric variants of these models, which use the Indian buffet …

Dimensionality reduction in multivariate time series analysis has broad applications, ranging
from financial data analysis to biomedical research. However, high levels of ambient noise …

While variational dropout approaches have been shown to be effective for network
sparsification, they are still suboptimal in the sense that they set the dropout rate for each …

We present a general construction for dependent random measures based on thinning
Poisson processes on an augmented space. The framework is not restricted to dependent …

Real-time video segmentation is a crucial task for many real-world applications such as
autonomous driving and robot control. Since state-of-the-art semantic segmentation models …

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However,
in their basic form they do not directly integrate dependency information among data arising …

Stochastic partition processes in a product space play an important role in modeling
relational data. Recent studies on the Mondrian process have introduced more flexibility into …