15 citations · 25 across the 14 of their papers we have counts for
15 papers
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…
Hierarchical Time Series Forecasting with Robust Reconciliation
Shuhei Aikawa, Aru Suzuki, Kei Yoshitake +4
This paper focuses on forecasting hierarchical time-series data, where each higher-level observation equals the sum of its corresponding lower-level time series. In such contexts,…
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
Naoki Nishimura, Ken Kobayashi, Kazuhide Nakata
Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate rev…
Learning Decision Trees and Forests with Algorithmic Recourse
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse a…
Inverse-Optimization-Based Uncertainty Set for Robust Linear Optimization
Ayaka Ueta, Mirai Tanaka, Ken Kobayashi +1
We consider solving linear optimization (LO) problems with uncertain objective coefficients. For such problems, we often employ robust optimization (RO) approaches by introducing a…
An IPW-based Unbiased Ranking Metric in Two-sided Markets
Keisho Oh, Naoki Nishimura, Minje Sung +2
In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such…
Algorithmic Recourse with Missing Values
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired…
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization
Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi +2
In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the Bézier simplex model. Also, w…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
Cardinality-constrained Distributionally Robust Portfolio Optimization
Ken Kobayashi, Yuichi Takano, Kazuhide Nakata
This paper studies a distributionally robust portfolio optimization model with a cardinality constraint for limiting the number of invested assets. We formulate this model as a mix…
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks
Tomokaze Shiratori, Ken Kobayashi, Yuichi Takano
This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such…
Bilevel Cutting-plane Algorithm for Solving Cardinality-constrained Mean-CVaR Portfolio Optimization Problems
Ken Kobayashi, Yuichi Takano, Kazuhide Nakata
This paper studies mean-risk portfolio optimization models using the conditional value-at-risk (CVaR) as a risk measure. We also employ a cardinality constraint for limiting the nu…
BRPO: Batch Residual Policy Optimization
Sungryull Sohn, Yinlam Chow, Jayden Ooi +4
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribut…
Asymptotic Risk of Bezier Simplex Fitting
Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi +1
The Bezier simplex fitting is a novel data modeling technique which exploits geometric structures of data to approximate the Pareto front of multi-objective optimization problems.…
Bezier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-objective Optimization
Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai +3
Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimiza…