454 citations · 5.5k across the 247 of their papers we have counts for
19 papers · 1 filter
Hierarchical Policy Search via Return-Weighted Density Estimation
Takayuki Osa, Masashi Sugiyama
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL). Hierarchical RL (HRL) tackles this problem by learning a hier…
Fully adaptive algorithm for pure exploration in linear bandits
Liyuan Xu, Junya Honda, Masashi Sugiyama
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parame…
Binary Classification from Positive-Confidence Data
Takashi Ishida, Gang Niu, Masashi Sugiyama
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confide…
Variational Inference based on Robust Divergences
Futoshi Futami, Issei Sato, Masashi Sugiyama
Robustness to outliers is a central issue in real-world machine learning applications. While replacing a model to a heavy-tailed one (e.g., from Gaussian to Student-t) is a standar…
Good Arm Identification via Bandit Feedback
Hideaki Kano, Junya Honda, Kentaro Sakamaki +3
We consider a novel stochastic multi-armed bandit problem called {\em good arm identification} (GAI), where a good arm is defined as an arm with expected reward greater than or equ…
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically…