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08/27/2023
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy
Generative models have emerged as a promising technique for producing hi...
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06/14/2023
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features
The Gaussian process latent variable model (GPLVM) is a popular probabil...
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03/05/2023
A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks
A classic inferential problem in statistics is the two-sample hypothesis...
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05/20/2022
Sparse Infinite Random Feature Latent Variable Modeling
We propose a non-linear, Bayesian non-parametric latent variable model w...
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10/18/2020
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
We propose a general scheme for solving convex and non-convex optimizati...
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06/19/2020
Latent variable modeling with random features
Gaussian process-based latent variable models are flexible and theoretic...
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01/15/2020
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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10/15/2019
Probabilistic Time of Arrival Localization
In this paper, we take a new approach for time of arrival geo-localizati...
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05/24/2019
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimatin...
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04/18/2019
A New Class of Time Dependent Latent Factor Models with Applications
In many applications, observed data are influenced by some combination o...
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10/26/2018
Communication Efficient Parallel Algorithms for Optimization on Manifolds
The last decade has witnessed an explosion in the development of models,...
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05/19/2017
Accelerated Inference for Latent Variable Models
Inference of latent feature models in the Bayesian nonparametric setting...
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10/19/2016
