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cs.LG2026
Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation
Armand Rousselot, Joran Wendebourg, Ullrich Köthe
The performance of machine learning models depends on discovering features in the data that are relevant to solving the given task while ignoring irrelevant variation. To check and…
cs.LG2026
From Core to Detail: Unsupervised Disentanglement with Entropy-Ordered Flows
Daniel Galperin, Ullrich Köthe
The unsupervised discovery of features that are both semantically meaningful and stable across runs remains a central challenge in representation learning. We introduce entropy-ord…
cs.LG2026
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
Paul Saegert, Ullrich Köthe
Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the pred…