Yuta Shikuri

Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network

2020 · arXiv (Cornell University)

Algorithms and hardware for solving quadratic unconstrained binary optimization (QUBO) problems have made significant recent progress. This advancement has focused attention on formulating combinatorial optimization problems as quadratic polynomials. To improve the performance of solving …

Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood

2024 · arXiv (Cornell University)

Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit from …