1.6k citations · 12.4k across the 394 of their papers we have counts for
6 papers · 2 filters
Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris Mooij, Dominik Janzing +1
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional exper…
Kernel Mean Estimation and Stein's Effect
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur +2
A mean function in reproducing kernel Hilbert space, or a kernel mean, is an important part of many applications ranging from kernel principal component analysis to Hilbert-space e…
The Randomized Dependence Coefficient
David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
We introduce the Randomized Dependence Coefficient (RDC), a measure of non-linear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-Rényi…
One-Class Support Measure Machines for Group Anomaly Detection
Krikamol Muandet, Bernhard Schölkopf
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points. The OCSMMs generalize wel…
Domain Generalization via Invariant Feature Representation
Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Doma…
Semi-Supervised Domain Adaptation with Non-Parametric Copulas
David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a p…