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Partial Wasserstein and Maximum Mean Discrepancy distances for bridging
  the gap between outlier detection and drift detection
v1v2 (latest)

Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection

9 June 2021
T. Viehmann
ArXiv (abs)PDFHTML

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
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation LearningACM 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
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
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
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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