On a Method to Construct Exponential Families by Representation Theory. - Koichi Tojo; Taro Yoshino (GSI, 2019)

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. - Takeshi Teshima; Isao Ishikawa; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (NeurIPS, 2020)

An Exponential Family on the Upper Half Plane and Its Conjugate Prior. - Koichi Tojo; Taro Yoshino (SPIGL, 2020)

Universal Approximation Property of Neural Ordinary Differential Equations. - Takeshi Teshima; Koichi Tojo; Masahiro Ikeda; Isao Ishikawa; Kenta Oono (CoRR, 2020)

A q-Analogue of the Family of Poincaré Distributions on the Upper Half Plane. - Koichi Tojo; Taro Yoshino (GSI (1), 2023)

Universal Approximation Property of Invertible Neural Networks. - Isao Ishikawa; Takeshi Teshima; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (J. Mach. Learn. Res., 2023)