7 papers
Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice
Masahiro Kato
This study considers the problem of portfolio choice, where we recommend a portfolio to an investor to maximize the expected utility of their wealth. Our goal is to construct an as…
Bayesian Portfolio Optimization by Predictive Synthesis
Masahiro Kato, Kentaro Baba, Hibiki Kaibuchi +1
Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up…
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
Masahiro Kato, Yuki Ikeda, Kentaro Baba +2
In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scen…
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
Masahiro Kato, Fumiaki Kozai, Ryo Inokuchi
The estimation of average treatment effects (ATEs), defined as the difference in expected outcomes between treatment and control groups, is a central topic in causal inference. Thi…
Conformal Predictive Portfolio Selection
Masahiro Kato
This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been deve…
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
Masahiro Kato, Akihiro Oga, Wataru Komatsubara +1
This study designs an adaptive experiment for efficiently estimating average treatment effects (ATEs). In each round of our adaptive experiment, an experimenter sequentially sample…
Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
Masahiro Kato, Kota Matsui, Ryo Inokuchi
Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict t…