1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
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…
Optimizing watermarks for large language models
Bram Wouters
With the rise of large language models (LLMs) and concerns about potential misuse, watermarks for generative LLMs have recently attracted much attention. An important aspect of suc…
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…