100 citations · 204 across the 32 of their papers we have counts for
9 papers · 1 filter
Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
Jeunghun Oh, Kyeongwon Lee, Jaeyong Lee +1
We study posterior contraction rates for sparse Bayesian Kolmogorov-Arnold networks (KANs) over anisotropic Besov spaces, providing a statistical foundation of KANs from a Bayesian…
A Bayesian sparse factor model with adaptive posterior concentration
Ilsang Ohn, Lizhen Lin, Yongdai Kim
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the load…
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Forough Fazeli-Asl, Michael Minyi Zhang, Lizhen Lin
A classic inferential statistical problem is the goodness-of-fit (GOF) test. Such a test can be challenging when the hypothesized parametric model has an intractable likelihood and…
Intrinsic and extrinsic deep learning on manifolds
Yihao Fang, Ilsang Ohn, Vijay Gupta +1
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds. Specifically, extrinsic deep neural networks (eDNNs) pres…
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
Minwoo Chae, Dongha Kim, Yongdai Kim +1
We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More specifically, a deep genera…
Network Distance Based on Laplacian Flows on Graphs
Dianbin Bao, Kisung You, Lizhen Lin
Distance plays a fundamental role in measuring similarity between objects. Various visualization techniques and learning tasks in statistics and machine learning such as shape matc…
Robust and Parallel Bayesian Model Selection
Michael Minyi Zhang, Henry Lam, Lizhen Lin
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets t…
On clustering network-valued data
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a gre…