5.7k citations · 8.4k across the 42 of their papers we have counts for
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stat.ML2018★ 14 cited
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Carlos Riquelme, George Tucker, Jasper Snoek
Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to bal…
stat.ML2018★ 147 cited
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman +1
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is diffi…