3 citations · 7 across the 5 of their papers we have counts for
7 papers
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…
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.…