Search Results for author:
Found 16 papers, 4 papers with code
Preventing Model Collapse in Gaussian Process Latent Variable Models
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models, commonly used for dimensionality reduction.
Online Student-$t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
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.
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 high-quality images that are indistinguishable from real images.
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features
The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling.
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Bayesian methods for GOF can be appealing due to their ability to incorporate expert knowledge through prior distributions.
Sparse Infinite Random Feature Latent Variable Modeling
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.
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e. g., whether the objective function is convex or non-convex, and the degree of non-convexity.
Latent variable modeling with random features
By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable.
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow.
Probabilistic Time of Arrival Localization
In this paper, we take a new approach for time of arrival geo-localization.
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive.
A New Class of Time Dependent Latent Factor Models with Applications
These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc.
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds.
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference.
Embarrassingly Parallel Inference for Gaussian Processes
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix.
Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern data analysis.
