8 papers · 1 filter
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…
Safe screening rules for portfolio optimization with linear and cardinality constraints
Nanari Wada, Shunnosuke Ikeda, Yuichi Takano +1
In portfolio optimization, a cardinality constraint, which limits the number of assets held, plays a key role in cutting down monitoring and transaction costs. However, the resulti…
Cross-validated feature selection using least-squares support vector machines for binary classification
Masaharu Mori, Shunnosuke Ikeda, Ryuta Tamura +2
Support vector machines (SVMs) are widely used for binary classification. However, irrelevant or redundant features can degrade classification accuracy and model interpretability,…
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…
Distributionally robust optimization for recommendation selection
Tomoya Yanagi, Shunnosuke Ikeda, Ken Kobayashi +1
Recommender systems play an essential role in online services by providing personalized item lists to support users' decision-making processes. While collaborative filtering method…
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…