15 citations · 27 across the 8 of their papers we have counts for
9 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…
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…
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…
Functional structure in production networks
Carolina Mattsson, Frank W. Takes, Eelke M. Heemskerk +4
Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level…