50 citations · 173 across the 35 of their papers we have counts for
12 papers · 1 filter
Feature selection based on cluster assumption in PU learning
Motonobu Uchikoshi, Youhei Akimoto
Feature selection is essential for efficient data mining and sometimes encounters the positive-unlabeled (PU) learning scenario, where only a few positive labels are available, whi…
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto +1
A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems. However, sometimes concept-b…
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning
Rei Sato, Kazuto Fukuchi, Jun Sakuma +1
We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents. This problem assumes two environments…
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Takumi Tanabe, Rei Sato, Kazuto Fukuchi +2
In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to…
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto +1
In real-world applications of multi-class classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (…
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto +1
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X \textit{has} A, B and \textit{does not have} C' in which A, B, and C are high-lev…