1 code implementation • 4 Mar 2021 • Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
To this end, we derive a representation theorem for proper losses in supervised learning, which dualizes the Savage representation.
no code implementations • 6 Jan 2021 • Hiroaki Sasaki, Takashi Takenouchi
Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation.
no code implementations • 10 Jun 2020 • Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems.
no code implementations • 1 Nov 2019 • Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvärinen
We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.
no code implementations • 13 Mar 2019 • Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method that can be applied without target data.
no code implementations • 23 Jan 2019 • Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
The parameter estimation of unnormalized models is a challenging problem.
no code implementations • NeurIPS 2015 • Takashi Takenouchi, Takafumi Kanamori
In this paper, we propose a novel parameter estimator for probabilistic models on discrete space.