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08/26/2023
Learning variational autoencoders via MCMC speed measures
Variational autoencoders (VAEs) are popular likelihood-based generative ...
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11/14/2022
A Bayesian framework for genome-wide inference of DNA methylation levels
DNA methylation is an important epigenetic mark that has been studied ex...
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06/25/2022
Missing data patterns in runners' careers: do they matter?
Predicting the future performance of young runners is an important resea...
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06/23/2022
Doubly-online changepoint detection for monitoring health status during sports activities
We provide an online framework for analyzing data recorded by smart watc...
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03/04/2022
Variance Reduction for Metropolis-Hastings Samplers
We introduce a general framework that constructs estimators with reduced...
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12/13/2021
How Good are Low-Rank Approximations in Gaussian Process Regression?
We provide guarantees for approximate Gaussian Process (GP) regression r...
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10/27/2021
Entropy-based adaptive Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCM...
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06/26/2021
Detecting anomalies in heterogeneous population-scale VAT networks
Anomaly detection in network science is the method to determine aberrant...
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05/30/2021
Scalable and Interpretable Marked Point Processes
We introduce a novel inferential framework for marked point processes th...
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04/07/2021
Inference for partially observed Riemannian Ornstein–Uhlenbeck diffusions of covariance matrices
We construct a generalization of the Ornstein–Uhlenbeck processes on the...
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03/08/2021
Flexible marked spatio-temporal point processes with applications to event sequences from association football
We develop a new family of marked point processes by focusing the charac...
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04/03/2020
Faster Gaussian Processes via Deep Embeddings
Gaussian processes provide a probabilistic framework for quantifying unc...
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11/04/2019
Gradient-based Adaptive Markov Chain Monte Carlo
We introduce a gradient-based learning method to automatically adapt Mar...
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04/15/2019
Copula-like Variational Inference
This paper considers a new family of variational distributions motivated...
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03/28/2019
Bayesian prediction of jumps in large panels of time series data
We take a new look at the problem of disentangling the volatility and ju...
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07/06/2018
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme...
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05/30/2018
Bayesian forecasting of mortality rates using latent Gaussian models
We provide forecasts for mortality rates by using two different approach...
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05/23/2018