1.6k citations · 12.4k across the 394 of their papers we have counts for
7 papers · 2 filters
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet +1
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty i…
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl +1
This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems. We propose formulations to represent, compare, an…
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf +1
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform thos…
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović +7
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notori…
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
Julius von Kügelgen, Alexander Mey, Marco Loog +1
While the success of semi-supervised learning (SSL) is still not fully understood, Schölkopf et al. (2012) have established a link to the principle of independent causal mechanisms…
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou +2
We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple…
Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
Anant Raj, Luigi Gresele, Michel Besserve +2
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent wor…