44 citations · 71 across the 11 of their papers we have counts for
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cs.LG2020★ 1 cited
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Ted Moskovitz, Michael Arbel, Ferenc Huszar +1
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally…
stat.ML2020★ 1 cited
Amortised Learning by Wake-Sleep
Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa +1
Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning f…