24 citations · 60 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Exploring the Whole Rashomon Set of Sparse Decision Trees
Rui Xin, Chudi Zhong, Zhi Chen +3
In any given machine learning problem, there may be many models that could explain the data almost equally well. However, most learning algorithms return only one of these models,…
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…
Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…