3 citations · 7 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…