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01/23/2023
Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings
Rankings and ratings are commonly used to express preferences but provid...
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09/30/2022
Efficient computation of the Knowledge Gradient for Bayesian Optimization
Bayesian optimization is a powerful collection of methods for optimizing...
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06/24/2022
On the validity of bootstrap uncertainty estimates in the Mallows-Binomial model
The Mallows-Binomial distribution is the first joint statistical model f...
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01/07/2022
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Rankings and scores are two common data types used by judges to express ...
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11/14/2020
Factorized Gaussian Process Variational Autoencoders
Variational autoencoders often assume isotropic Gaussian priors and mean...
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10/26/2020
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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10/20/2020
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...
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05/31/2020
Bayesian Optimisation vs. Input Uncertainty Reduction
Simulators often require calibration inputs estimated from real world da...
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02/23/2020
ConBO: Conditional Bayesian Optimization
Bayesian optimization is a class of data efficient model based algorithm...
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10/21/2019
Bayesian Optimization Allowing for Common Random Numbers
Bayesian optimization is a powerful tool for expensive stochastic black-...
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10/03/2019
