463 citations · 5.1k across the 190 of their papers we have counts for
5 papers · 2 filters
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song, Ronald E. Parr, Lawrence Carin
The impact of softmax on the value function itself in reinforcement learning (RL) is often viewed as problematic because it leads to sub-optimal value (or Q) functions and interfer…
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
Hongteng Xu, Wenlin Wang, Wei Liu +1
We propose a novel Wasserstein method with a distillation mechanism, yielding joint learning of word embeddings and topics. The proposed method is based on the fact that the Euclid…
Certified Adversarial Robustness with Additive Noise
Bai Li, Changyou Chen, Wenlin Wang +1
The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data…
Policy Optimization as Wasserstein Gradient Flows
Ruiyi Zhang, Changyou Chen, Chunyuan Li +1
Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total…
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu, Shuyang Dai, Zhe Gan +5
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed m…