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Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection
9 June 2021
T. Viehmann
Re-assign community
ArXiv (abs)
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Papers citing
"Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection"
4 / 4 papers shown
Title
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2024
Miaomiao Cai
Min Hou
Lei Chen
Le Wu
Haoyue Bai
Yong Li
Meng Wang
69
19
0
17 Nov 2025
Learning to Land Anywhere: Transferable Generative Models for Aircraft Trajectories
Olav Finne Praesteng Larsen
Massimiliano Ruocco
Michail Spitieris
Abdulmajid Murad
Martina Ragosta
136
0
0
06 Nov 2025
Generalization Analysis for Bayesian Optimal Experiment Design under Model Misspecification
Roubing Tang
Sabina J. Sloman
Samuel Kaski
CML
118
0
0
09 Jun 2025
Frouros: A Python library for drift detection in machine learning systems
Jaime Céspedes-Sisniega
Álvaro López-García
143
2
0
14 Aug 2022
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