3 papers
stat.ML2026
EB-RANSAC: Random Sample Consensus based on Energy-Based Model
Muneki Yasuda, Nao Watanabe, Kaiji Sekimoto
Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In this study, an energy-based…
stat.ML2025
Effective Method for Inverse Ising Problem under Missing Observations in Restricted Boltzmann Machines
Kaiji Sekimoto, Muneki Yasuda
Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem…
stat.ML2020
Spatial Monte Carlo Integration with Annealed Importance Sampling
Muneki Yasuda, Kaiji Sekimoto
Evaluating expectations on an Ising model (or Boltzmann machine) is essential for various applications, including statistical machine learning. However, in general, the evaluation…