Two Papers Accepted for Machine Learning Journal | Machine Learning and Data Mining Research Laboratory

A paper proposing a method for estimating intervention effects while estimating confounding variables when they are not observed, and a paper proposing a method for estimating state importance in reinforcement learning from pairwise comparisons of episodes have been accepted for publication in the Machine Learning Journal (ACML Journal Track).

Shonosuke Harada, Hisashi Kashima.
InfoCEVAE: Treatment Effect Estimation with Hidden Confounding Variables Matching.
Machine Learning, 2022.

Guoxi Zhang, Hisashi Kashima.
Learning State Importance for Preference-based Reinforcement Learning.
Machine Learning, 2022.