65 citations · 220 across the 24 of their papers we have counts for
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cs.LG2022
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Rui Li, ST John, Arno Solin
Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common cho…
cs.LG2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Paul E. Chang, Prakhar Verma, ST John +3
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with dat…
cs.LG2022
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes
Çağlar Hızlı, ST John, Anne Juuti +3
A treatment policy defines when and what treatments are applied to affect some outcome of interest. Data-driven decision-making requires the ability to predict what happens if a po…