1 citations · 1 across the 3 of their papers we have counts for
6 papers
Whitening Improves Robustness to Spurious Correlations in Linear Probes
Floris Holstege, Bram Wouters, Noud van Giersbergen +1
Deep neural networks tend to rely on simple features that may be spurious and thus fail to generalize. We study this problem in the setting of linear probes, where a (generalized)…
Preserving Task-Relevant Information Under Linear Concept Removal
Floris Holstege, Shauli Ravfogel, Bram Wouters
Modern neural networks often encode unwanted concepts alongside task-relevant information, leading to fairness and interpretability concerns. Existing post-hoc approaches can remov…
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Floris Holstege, Mackenzie Jorgensen, Kirtan Padh +5
Algorithms are increasingly used to automate or aid human decisions, yet recent research shows that these algorithms may exhibit bias across legally protected demographic groups. H…
Optimizing importance weighting in the presence of sub-population shifts
Floris Holstege, Bram Wouters, Noud van Giersbergen +1
A distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different w…
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Floris Holstege, Bram Wouters, Noud van Giersbergen +1
Out-of-distribution generalization in neural networks is often hampered by spurious correlations. A common strategy is to mitigate this by removing spurious concepts from the neura…
Female scholars need to achieve more for equal public recognition
Menno H. Schellekens, Floris Holstege, Taha Yasseri
Different kinds of "gender gap" have been reported in different walks of the scientific life, almost always favouring male scientists over females. In this work, for the first time…