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03/13/2023
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is a powerful framework to infer an...
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10/04/2022
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Markov chain Monte Carlo (MCMC) is an established approach for uncertain...
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03/26/2021
Active multi-fidelity Bayesian online changepoint detection
Online algorithms for detecting changepoints, or abrupt shifts in the be...
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07/08/2020
Finite mixture models are typically inconsistent for the number of components
Scientists and engineers are often interested in learning the number of ...
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03/20/2020
Weighted Meta-Learning
Meta-learning leverages related source tasks to learn an initialization ...
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03/22/2016
Completely random measures for modeling power laws in sparse graphs
Network data appear in a number of applications, such as online social n...
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03/22/2016
Edge-exchangeable graphs and sparsity
A known failing of many popular random graph models is that the Aldous-H...
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10/28/2015
Priors on exchangeable directed graphs
Directed graphs occur throughout statistical modeling of networks, and e...
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12/05/2014