Zobrazeno 1 - 10
of 650
pro vyhledávání: '"von Kügelgen A"'
Autor:
Brady, Jack, von Kügelgen, Julius, Lachapelle, Sébastien, Buchholz, Simon, Kipf, Thomas, Brendel, Wieland
Learning disentangled representations of concepts and re-composing them in unseen ways is crucial for generalizing to out-of-domain situations. However, the underlying properties of concepts that enable such disentanglement and compositional generali
Externí odkaz:
http://arxiv.org/abs/2411.07784
Autor:
von Kügelgen, Julius
Publikováno v:
University of Cambridge, 2024
Causal models provide rich descriptions of complex systems as sets of mechanisms by which each variable is influenced by its direct causes. They support reasoning about manipulating parts of the system and thus hold promise for addressing some of the
Externí odkaz:
http://arxiv.org/abs/2406.13371
Autor:
Xu, Danru, Yao, Dingling, Lachapelle, Sébastien, Taslakian, Perouz, von Kügelgen, Julius, Locatello, Francesco, Magliacane, Sara
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations. We instead consider a partially observed sett
Externí odkaz:
http://arxiv.org/abs/2403.08335
Autor:
Ghosh, Shubhangi, Gresele, Luigi, von Kügelgen, Julius, Besserve, Michel, Schölkopf, Bernhard
Independent Mechanism Analysis (IMA) seeks to address non-identifiability in nonlinear Independent Component Analysis (ICA) by assuming that the Jacobian of the mixing function has orthogonal columns. As typical in ICA, previous work focused on the c
Externí odkaz:
http://arxiv.org/abs/2312.13438
Autor:
Eastwood, Cian, von Kügelgen, Julius, Ericsson, Linus, Bouchacourt, Diane, Vincent, Pascal, Schölkopf, Bernhard, Ibrahim, Mark
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown at training time, it is difficult to deduce a priori which attributes
Externí odkaz:
http://arxiv.org/abs/2311.08815
Autor:
Laumann, Felix, von Kügelgen, Julius, Park, Junhyung, Schölkopf, Bernhard, Barahona, Mauricio
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering.Such measurements can be viewed as realisations of an
Externí odkaz:
http://arxiv.org/abs/2311.08743
Autor:
Yao, Dingling, Xu, Danru, Lachapelle, Sébastien, Magliacane, Sara, Taslakian, Perouz, Martius, Georg, von Kügelgen, Julius, Locatello, Francesco
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture
Externí odkaz:
http://arxiv.org/abs/2311.04056
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights. Whereas the classical interventional interpretation of counterfactuals has been studied extensively, backtracking
Externí odkaz:
http://arxiv.org/abs/2310.07665
Autor:
Eastwood, Cian, Singh, Shashank, Nicolicioiu, Andrei Liviu, Vlastelica, Marin, von Kügelgen, Julius, Schölkopf, Bernhard
To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label chan
Externí odkaz:
http://arxiv.org/abs/2307.09933
Publikováno v:
UAI 2023
We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments. We show that the statistical efficiency in terms of expected squared error can be improved
Externí odkaz:
http://arxiv.org/abs/2306.06002