Logistic-beta processes for modeling dependent random probabilities with beta marginals

1 code implementation • 10 Feb 2024 • Changwoo J. Lee, Alessandro Zito, Huiyan Sang, David B. Dunson

The beta distribution serves as a canonical tool for modeling probabilities and is extensively used in statistics and machine learning, especially in the field of Bayesian nonparametrics.

regression

Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization

1 code implementation • 30 Jan 2023 • Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Schafer, Matthias Katzfuss

To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) factors.

Gaussian Processes

Why the Rich Get Richer? On the Balancedness of Random Partition Models

no code implementations • 30 Jan 2022 • Changwoo J. Lee, Huiyan Sang

Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems.

Community Detection Entity Resolution +1

BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain

1 code implementation • NeurIPS 2021 • Zhao Tang Luo, Huiyan Sang, Bani Mallick

Nonparametric regression on complex domains has been a challenging task as most existing methods, such as ensemble models based on binary decision trees, are not designed to account for intrinsic geometries and domain boundaries.

Bayesian Inference regression

Row-clustering of a Point Process-valued Matrix

1 code implementation • NeurIPS 2021 • Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan

Structured point process data harvested from various platforms poses new challenges to the machine learning community.

Clustering Point Processes

T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs

1 code implementation • NeurIPS 2021 • Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang

In models dealing with graph-structured data, multivariate parameters may not only exhibit sparse patterns but have structured sparsity and smoothness in the sense that both zero and non-zero parameters tend to cluster together.

Anomaly Detection Bayesian Inference +1

Fused Spatial Point Process Intensity Estimation with Varying Coefficients on Complex Constrained Domains

1 code implementation • 22 Feb 2021 • Lihao Yin, Huiyan Sang

We propose an efficient intensity estimation algorithm to estimate the spatially varying intensity function and to study the varying relationship between intensity and explanatory variables on complex domains.

Point Processes Applications Methodology

Cognitive Learning of Statistical Primary Patterns via Bayesian Network

no code implementations • 28 Sep 2014 • Weijia Han, Huiyan Sang, Min Sheng, Jiandong Li, Shuguang Cui

How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community.