3 citations · 8 across the 5 of their papers we have counts for
12 papers
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression
Masahiro Kato, Masaaki Imaizumi
We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning f…
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
Masahiro Kato, Masaaki Imaizumi, Kentaro Minami
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ra…
Scalable Personalised Item Ranking through Parametric Density Estimation
Riku Togashi, Masahiro Kato, Mayu Otani +2
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pair…
Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability
Masahiro Kato
Adaptive experiments, including efficient average treatment effect estimation and multi-armed bandit algorithms, have garnered attention in various applications, such as social exp…
Density-Ratio Based Personalised Ranking from Implicit Feedback
Riku Togashi, Masahiro Kato, Mayu Otani +1
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopti…
ATRO: Adversarial Training with a Rejection Option
Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara
This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algor…