Deriving efficient optimization methods based on stable explicit numerical methods. - Kansei Ushiyama; Shun Sato 001; Takayasu Matsuo (JSIAM Lett., 2022)

Essential convergence rate of ordinary differential equations appearing in optimization. - Kansei Ushiyama; Shun Sato 001; Takayasu Matsuo (JSIAM Lett., 2022)

A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization. - Kansei Ushiyama; Shun Sato 001; Takayasu Matsuo (NeurIPS, 2023)

A new unified framework for designing convex optimization methods with prescribed theoretical convergence estimates: A numerical analysis approach. - Kansei Ushiyama; Shun Sato 001; Takayasu Matsuo (CoRR, 2023)

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation. - Yoichi Chikahara; Kansei Ushiyama (UAI, 2024)

Analysis of continuous dynamical system models with Hessians derived from optimization methods. - Tomoya Kamijima; Shun Sato 001; Kansei Ushiyama; Takayasu Matsuo; Ken'ichiro Tanaka (JSIAM Lett., 2024)

Properties and practicability of convergence-guaranteed optimization methods derived from weak discrete gradients. - Kansei Ushiyama; Shun Sato 001; Takayasu Matsuo (Numer. Algorithms, 2024)