activity
20152023
most citedRobustness to Adversarial Perturbations in Learning from Incomplete Data

33 citations · 76 across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG20193 cited

MANGA: Method Agnostic Neural-policy Generalization and Adaptation

Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda

In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework…

cs.LG2019

Reconnaissance and Planning algorithm for constrained MDP

Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada +1

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while…

cs.LG2019

Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks

Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara +1

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP…

stat.ML201933 cited

Robustness to Adversarial Perturbations in Learning from Incomplete Data

Amir Najafi, Shin-ichi Maeda, Masanori Koyama +1

What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, thi…

cs.LG201915 cited

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis

Katsuhiko Ishiguro, Shin-ichi Maeda, Masanori Koyama

Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GN…