1 citations · 1 across the 5 of their papers we have counts for
7 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)…
Model-agnostic noise reduction for high-dimensional time series data
Bram Wouters, Cees Diks
We develop a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component con…
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…
Noise reduction for functional time series
Cees Diks, Bram Wouters
A novel method for noise reduction in the setting of curve time series with error contamination is proposed, based on extending the framework of functional principal component anal…