72 citations · 174 across the 15 of their papers we have counts for
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stat.ML2016
Bayesian Learning of Kernel Embeddings
Seth Flaxman, Dino Sejdinovic, John P. Cunningham +1
Kernel methods are one of the mainstays of machine learning, but the problem of kernel learning remains challenging, with only a few heuristics and very little theory. This is of p…
stat.ML2016
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate B…