no code implementations • 10 Apr 2021 • Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada
B\'ezier simplex fitting algorithms have been recently proposed to approximate the Pareto set/front of multi-objective continuous optimization problems.
1 code implementation • NeurIPS 2019 • Akinori Tanaka
Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution $p$ and the generator distribution $p_G$.
no code implementations • 17 Jun 2019 • Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada
In this paper, we analyze the asymptotic risks of those B\'ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting.
no code implementations • 17 Jun 2019 • Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara
We empirically illustrate our metric with synthetic data, and evaluate it in the context of the independence test for random processes.
no code implementations • 11 Dec 2017 • Akinori Tanaka, Akio Tomiya
Our proposing algorithm provides consistent central values of expectation values of the action density and one-point Green's function with ones from the original HMC in both the symmetric phase and broken phase within the statistical error.
