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Interpretable preference learning: a game theoretic framework for large
  margin on-line feature and rule learning

Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

19 December 2018
Mirko Polato
F. Aiolli
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning"

2 / 2 papers shown
Title
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic
  Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELMXAI
178
423
0
20 Jan 2022
Bilevel Learning Model Towards Industrial Scheduling
Bilevel Learning Model Towards Industrial Scheduling
Longkang Li
Hui-Ling Zhen
Mingxuan Yuan
Jiawen Lu
Xialiang Tong
Jia Zeng
Jun Wang
D. Schnieders
AI4CE
16
1
0
10 Aug 2020
1