Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Zhang, Michael Minyi"'
Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral approach to robust beamforming is the stochastic weighted minimum mean
Externí odkaz:
http://arxiv.org/abs/2404.06055
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in modeling data with GPLVMs include inadequate kernel flexibility and impr
Externí odkaz:
http://arxiv.org/abs/2404.01697
Online Student-$t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
Autor:
Sha, Taole, Zhang, Michael Minyi
Time-dependent data often exhibit characteristics, such as non-stationarity and heavy-tailed errors, that would be inappropriate to model with the typical assumptions used in popular models. Thus, more flexible approaches are required to be able to a
Externí odkaz:
http://arxiv.org/abs/2311.00564
Generative models have emerged as a promising technique for producing high-quality images that are indistinguishable from real images. Generative adversarial networks (GANs) and variational autoencoders (VAEs) are two of the most prominent and widely
Externí odkaz:
http://arxiv.org/abs/2308.14048
The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inference for GPLVMs is computationally tractable only when the data likeliho
Externí odkaz:
http://arxiv.org/abs/2306.08352
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
Autor:
Zhang, Michael Minyi
We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet process prior. A posteriori, the number of instantiated dimensions in the l
Externí odkaz:
http://arxiv.org/abs/2205.09909
We propose a general scheme for solving convex and non-convex optimization problems on manifolds. The central idea is that, by adding a multiple of the squared retraction distance to the objective function in question, we "convexify" the objective fu
Externí odkaz:
http://arxiv.org/abs/2010.08908
Gaussian process-based latent variable models are flexible and theoretically grounded tools for nonlinear dimension reduction, but generalizing to non-Gaussian data likelihoods within this nonlinear framework is statistically challenging. Here, we us
Externí odkaz:
http://arxiv.org/abs/2006.11145
Publikováno v:
Artificial Intelligence and Statistics, 108:3685-3695, 2020
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used models like t
Externí odkaz:
http://arxiv.org/abs/2001.05591