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Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments
Tomoka Takei, Shunnosuke Ikeda, Yuichi Takano
Online controlled experiments, or A/B tests, are widely used to estimate causal effects on digital platforms. A central challenge is to improve experimental sensitivity, or statist…
Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation
Kensei Nosaka, Shunnosuke Ikeda, Yuichi Takano
Mean-variance portfolio optimization (MVO) is a central framework in data-driven asset management. A widely adopted approach is a two-stage framework that first predicts expected r…
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1
Collaborative analysis of decentralized confidential datasets is important, but direct sharing of original datasets is often restricted by privacy and institutional constraints. Da…
Interpretable clustering via optimal multi-way decision trees
Hayato Suzuki, Shunnosuke Ikeda, Naoki Nishimura +1
Clustering is a fundamental unsupervised learning technique for uncovering data structures to facilitate knowledge discovery and decision-making. While clustering accuracy is cruci…
Buffered AUC maximization for scoring systems via mixed-integer optimization
Moe Shiina, Shunnosuke Ikeda, Yuichi Takano
A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient. This system is highly interpretable and allo…