5 AISTATS 2020 Accepted Papers!
5 papers co-authored by the OxCSML group members have been accepted to the main program of AISTATS 2020
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A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Adam Foster (University of Oxford); Martin Jankowiak (Uber AI Labs); Matthew O’Meara (University of Michigan); Yee Whye Teh (University of Oxford); Tom Rainforth (University of Oxford) -
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
Chris Carmona (University of Oxford); Geoff Nicholls (University of Oxford) -
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders Kaspar Märtens (University of Oxford), Christopher Yau (University of Manchester, The Alan Turing Institute)
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Neural Decomposition: Functional ANOVA with Variational Autoencoders Kaspar Märtens (University of Oxford), Christopher Yau (University of Manchester, The Alan Turing Institute)
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Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Giuseppe Di Benedetto (University of Oxford), Francois Caron (University of Oxford), Yee Whye Teh (University of Oxford)