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cs.LG2026
ILP-BO: Integer Linear Programming-Based Black-Box Optimization
Hyakka Nakada, Shu Tanaka
Black-box Optimization (BO) is a powerful framework for optimizing expensive objective functions or unknown functions with a limited number of evaluations. A central step of standa…
cs.LG2025
Statistics of Min-max Normalized Eigenvalues in Random Matrices
Hyakka Nakada, Shu Tanaka
Random matrix theory has played an important role in various areas of pure mathematics, mathematical physics, and machine learning. From a practical perspective of data science, in…
cs.LG2024
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model
Yuya Seki, Hyakka Nakada, Shu Tanaka
This paper presents an initialization method that can approximate a given approximate Ising model with a high degree of accuracy using a factorization machine (FM), a machine learn…