9 citations · 19 across the 10 of their papers we have counts for
10 papers
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric data perturbation enables one-shot representation sharing for privacy-preserving collaborative learning: each participant applies a secret distance-preserving transformat…
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +1
Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bas…
Post-Processing with Projection and Rescaling Algorithms for Semidefinite Programming
Shin-ichi Kanoh, Akiko Yoshise
We propose the algorithm that solves the symmetric cone programs (SCPs) by iteratively calling the projection and rescaling methods the algorithms for solving exceptional cases of…
Riemannian Interior Point Methods for Constrained Optimization on Manifolds
Zhijian Lai, Akiko Yoshise
We extend the classical primal-dual interior point method from the Euclidean setting to the Riemannian one. Our method, named the Riemannian interior point method, is for solving R…
A New Extension of Chubanov's Method to Symmetric Cones
Shin-ichi Kanoh, Akiko Yoshise
We propose a new variant of Chubanov's method for solving the feasibility problem over the symmetric cone by extending Roos's method (2018) of solving the feasibility problem over…
Completely Positive Factorization by a Riemannian Smoothing Method
Zhijian Lai, Akiko Yoshise
Copositive optimization is a special case of convex conic programming, and it consists of optimizing a linear function over the cone of all completely positive matrices under linea…
Evaluating approximations of the semidefinite cone with trace normalized distance
Yuzhu Wang, Akiko Yoshise
We evaluate the dual cone of the set of diagonally dominant matrices (resp., scaled diagonally dominant matrices), namely (resp., ), as an approxima…
Centering ADMM for the Semidefinite Relaxation of the QAP
Shin-ichi Kanoh, Akiko Yoshise
We propose a new method for solving the semidefinite (SD) relaxation of the quadratic assignment problem (QAP), called Centering ADMM. Centering ADMM is an alternating direction me…
Polyhedral approximations of the semidefinite cone and their application
Yuzhu Wang, Akihiro Tanaka, Akiko Yoshise
We develop techniques to construct a series of sparse polyhedral approximations of the semidefinite cone. Motivated by the semidefinite (SD) bases proposed by Tanaka and Yoshise (2…
LP-based Tractable Subcones of the Semidefinite Plus Nonnegative Cone
Akihiro Tanaka, Akiko Yoshise
The authors in a previous paper devised certain subcones of the semidefinite plus nonnegative cone and showed that satisfaction of the requirements for membership of those subcones…