1.6k citations · 12.4k across the 394 of their papers we have counts for
8 papers · 2 filters
Causal and anti-causal learning in pattern recognition for neuroimaging
Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball +1
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, th…
Decoding index finger position from EEG using random forests
Sebastian Weichwald, Timm Meyer, Bernhard Schölkopf +2
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of b…
Causal interpretation rules for encoding and decoding models in neuroimaging
Sebastian Weichwald, Timm Meyer, Ozan Özdenizci +3
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements a…
Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf +1
Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines. This paper unifies the…
Invariant Models for Causal Transfer Learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner +1
Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the us…
Removing systematic errors for exoplanet search via latent causes
Bernhard Schölkopf, David W. Hogg, Dun Wang +4
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired b…