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Tomonori Izumitani

2 accepted papers

2026

Causal Newton Optimization: Online Calibration with Iterative Local Linear Modeling and Newton Updates

IJCAI 2026

Optimization in industrial systems often involves calibrating from a semi-optimized state, where global exploration methods like Reinforcement Learning (RL) or Bayesian Optimization (BO) are inefficient or unsafe. We propose Causal Newton Optimization (CNO), an online algorithm that iteratively cali

Cited by 0Scholar
2022

Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference

AISTATS 2022poster

HSIC Lasso is one of the most effective sparse nonlinear feature selection methods based on the Hilbert-Schmidt independence criterion. We propose an adaptive nonlinear feature selection method, which is based on the HSIC Lasso, that uses a stochastic model with a family of super-Gaussian prior dist…