Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Lin, Lizhen"'
We propose the first Bayesian methods for detecting change points in high-dimensional mean and covariance structures. These methods are constructed using pairwise Bayes factors, leveraging modularization to identify significant changes in individual
Externí odkaz:
http://arxiv.org/abs/2411.14864
In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potent
Externí odkaz:
http://arxiv.org/abs/2410.02025
We consider a class of conditional forward-backward diffusion models for conditional generative modeling, that is, generating new data given a covariate (or control variable). To formally study the theoretical properties of these conditional generati
Externí odkaz:
http://arxiv.org/abs/2409.20124
We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with pot
Externí odkaz:
http://arxiv.org/abs/2307.09210
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 loading matrix to be inferred. The novelty is to introduce a certain depen
Externí odkaz:
http://arxiv.org/abs/2305.18488
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 facilitate a complete characterization of uncertainty through the post
Externí odkaz:
http://arxiv.org/abs/2304.11251
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 its distributional form is not available. Bayesian methods for GOF ca
Externí odkaz:
http://arxiv.org/abs/2303.02637
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds. Specifically, extrinsic deep neural networks (eDNNs) preserve geometric features on manifolds by utilizing an equivariant embed
Externí odkaz:
http://arxiv.org/abs/2302.08606
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and quantify the uncertainty in
Externí odkaz:
http://arxiv.org/abs/2212.13886
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, communit
Externí odkaz:
http://arxiv.org/abs/2203.02090