42 citations · 114 across the 6 of their papers we have counts for
4 papers · 1 filter
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices
Florian Yger, Masashi Sugiyama
Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for Sy…
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching
Marthinus Du Plessis, Masashi Sugiyama
In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias.…
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization
Gang Niu, Bo Dai, Makoto Yamada +1
We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the man…
Parametric Return Density Estimation for Reinforcement Learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima +2
Most conventional Reinforcement Learning (RL) algorithms aim to optimize decision-making rules in terms of the expected returns. However, especially for risk management purposes, o…