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05/30/2023
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
Generative Models (GMs) have attracted considerable attention due to the...
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03/07/2023
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
This paper investigates when the importance weighting (IW) correction is...
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03/01/2023
Continuous-Time Functional Diffusion Processes
We introduce functional diffusion processes (FDPs), which generalize tra...
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02/09/2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Autoencoders and their variants are among the most widely used models in...
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10/18/2022
Locally Smoothed Gaussian Process Regression
We develop a novel framework to accelerate Gaussian process regression (...
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06/10/2022
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Score-based diffusion models are a class of generative models whose dyna...
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04/12/2022
Local Random Feature Approximations of the Gaussian Kernel
A fundamental drawback of kernel-based statistical models is their limit...
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02/04/2022
Complex-to-Real Random Features for Polynomial Kernels
Kernel methods are ubiquitous in statistical modeling due to their theor...
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01/21/2022
Improved Random Features for Dot Product Kernels
Dot product kernels, such as polynomial and exponential (softmax) kernel...
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06/30/2021
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
We revisit the theoretical properties of Hamiltonian stochastic differen...
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06/11/2021
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian ...
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11/25/2020
All You Need is a Good Functional Prior for Bayesian Deep Learning
The Bayesian treatment of neural networks dictates that a prior distribu...
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11/10/2020
Sparse within Sparse Gaussian Processes using Neighbor Information
Approximations to Gaussian processes based on inducing variables, combin...
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10/19/2020
Learning Optimal Conditional Priors For Disentangled Representations
A large part of the literature on learning disentangled representations ...
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06/09/2020
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
In this work we define a unified mathematical framework to deepen our un...
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06/08/2020
A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection...
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03/16/2020
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Accurate travel products price forecasting is a highly desired feature t...
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03/06/2020
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations
Variational inference techniques based on inducing variables provide an ...
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11/29/2019
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
Variational inference offers scalable and flexible tools to tackle intra...
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11/15/2019
LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method - LIBRE - to learn an interpretable classifier...
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10/22/2019
Kernel computations from large-scale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a succ...
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10/21/2019
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Large scale machine learning is increasingly relying on distributed opti...
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06/06/2019
Deep Compositional Spatial Models
Nonstationary, anisotropic spatial processes are often used when modelli...
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05/27/2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Over-parameterized models, such as DeepNets and ConvNets, form a class o...
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02/28/2019
A comparative evaluation of novelty detection algorithms for discrete sequences
The identification of anomalies in temporal data is a core component of ...
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10/29/2018
Variational Calibration of Computer Models
Bayesian calibration of black-box computer models offers an established ...
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10/18/2018
Good Initializations of Variational Bayes for Deep Models
Stochastic variational inference is an established way to carry out appr...
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05/28/2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classi...
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05/26/2018
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in application...
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02/15/2018
Constraining the Dynamics of Deep Probabilistic Models
We introduce a novel generative formulation of deep probabilistic models...
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01/16/2018
Assessing Bayesian Nonparametric Log-Linear Models: an application to Disclosure Risk estimation
We present a method for identification of models with good predictive pe...
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08/17/2017
Pseudo-extended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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04/24/2017
Entropic Trace Estimates for Log Determinants
The scalable calculation of matrix determinants has been a bottleneck to...
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04/05/2017
Bayesian Inference of Log Determinants
The log-determinant of a kernel matrix appears in a variety of machine l...
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10/18/2016
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process mode...
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10/14/2016
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Proces...
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07/07/2016
Mini-Batch Spectral Clustering
The cost of computing the spectrum of Laplacian matrices hinders the app...
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02/22/2016
Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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06/12/2015
MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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01/22/2015
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
In applications of Gaussian processes where quantification of uncertaint...
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11/28/2013
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC
Kernel methods have revolutionized the fields of pattern recognition and...
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10/02/2013
