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of 324
pro vyhledávání: '"Lin, Lizhen"'
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
We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including genomics and medical imaging, it is natural to assume that only a few e
Externí odkaz:
http://arxiv.org/abs/2112.02580
Adversarial examples can easily degrade the classification performance in neural networks. Empirical methods for promoting robustness to such examples have been proposed, but often lack both analytical insights and formal guarantees. Recently, some r
Externí odkaz:
http://arxiv.org/abs/2111.12906
Publikováno v:
In European Journal of Surgical Oncology July 2024 50(7)