4 citations · 14 across the 7 of their papers we have counts for
8 papers
Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence
Takashi Takenouchi
Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of the normalization constant of…
Lower-Bounded Proper Losses for Weakly Supervised Classification
Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
This paper discusses the problem of weakly supervised classification, in which instances are given weak labels that are produced by some label-corruption process. The goal is to de…
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation
Hiroaki Sasaki, Takashi Takenouchi
Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification pro…
Regret Minimization for Causal Inference on Large Treatment Space
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi +1
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from biase…
Robust contrastive learning and nonlinear ICA in the presence of outliers
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti +1
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practic…
Zero-shot Domain Adaptation Based on Attribute Information
Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a sp…
Unified estimation framework for unnormalized models with statistical efficiency
Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi +1
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing c…
Graph-based Composite Local Bregman Divergences on Discrete Sample Spaces
Takafumi Kanamori, Takashi Takenouchi
One of the most common methods for statistical inference is the maximum likelihood estimator (MLE). The MLE needs to compute the normalization constant in statistical models, and i…