454 citations · 5.5k across the 247 of their papers we have counts for
6 papers · 1 filter
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
Syogo Mori, Voot Tangkaratt, Tingting Zhao +2
The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free R…
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
Marthinus Christoffel du Plessis, Masashi Sugiyama
We consider the unsupervised learning problem of assigning labels to unlabeled data. A naive approach is to use clustering methods, but this works well only when data is properly c…
Semi-Supervised Information-Maximization Clustering
Daniele Calandriello, Gang Niu, Masashi Sugiyama
Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorit…
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Song Liu, John A. Quinn, Michael U. Gutmann +2
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two da…
Density Ratio Hidden Markov Models
John A. Quinn, Masashi Sugiyama
Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing.…
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration
Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt +2
The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge in…