8 citations · 11 across the 6 of their papers we have counts for
6 papers
Uncertainty Quantification in Pairwise Difference Learning for Classification
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as…
Toward decision-aware AI for LSST-scale time-domain astronomy
C. R. Bom, A. Mahabal, F. Bianco +27
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will generate approximately (10^7) alerts per night, pushing time-domain astronomy beyond pipelines that trea…
LossVal: Efficient Data Valuation for Neural Networks
Tim Wibiral, Mohamed Karim Belaid, Maximilian Rabus +1
Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is co…
Pairwise Difference Learning for Classification
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the sta…
Optimizing Data Shapley Interaction Calculation from O(2^n) to O(t n^2) for KNN models
Mohamed Karim Belaid, Dorra El Mekki, Maximilian Rabus +1
With the rapid growth of data availability and usage, quantifying the added value of each training data point has become a crucial process in the field of artificial intelligence.…
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark
Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus +1
In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy…