1 citations · 1 across the 16 of their papers we have counts for
17 papers
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…
Subset Selection for Stratified Sampling in Online Controlled Experiments
Haru Momozu, Yuki Uehara, Naoki Nishimura +6
Online controlled experiments, also known as A/B testing, are the digital equivalent of randomized controlled trials for estimating the impact of marketing campaigns on website vis…
Estimating profitable price bounds for prescriptive price optimization
Masato Inokuma, Shunnosuke Ikeda, Yuichi Takano
Pricing of products and services, which has a significant impact on consumer demand, is one of the most important factors in maximizing business profits. Prescriptive price optimiz…
Robust personalized pricing under uncertainty of purchase probabilities
Shunnosuke Ikeda, Naoki Nishimura, Noriyoshi Sukegawa +1
This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to p…
Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces
Koya Ohashi, Sho Sekine, Deddy Jobson +4
In a two-sided marketplace, network effects are crucial for competitiveness, and platforms need to retain users through advanced customer relationship management as much as possibl…
Fast solution to the fair ranking problem using the Sinkhorn algorithm
Yuki Uehara, Shunnosuke Ikeda, Naoki Nishimura +8
In two-sided marketplaces such as online flea markets, recommender systems for providing consumers with personalized item rankings play a key role in promoting transactions between…
Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics
Tomoya Yanagi, Shunnosuke Ikeda, Yuichi Takano
This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, t…
Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times
Daiki Ikuma, Shunnosuke Ikeda, Noriyoshi Sukegawa +1
This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships witho…
Privacy-preserving recommender system using the data collaboration analysis for distributed datasets
Tomoya Yanagi, Shunnosuke Ikeda, Noriyoshi Sukegawa +1
In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distribut…
Robust portfolio optimization model for electronic coupon allocation
Yuki Uehara, Naoki Nishimura, Yilin Li +6
Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally…