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no code implementations • ICLR 2019 • Michel Besserve, Remy Sun, Bernhard Schoelkopf

Deep generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are important tools to capture and investigate the properties of complex empirical data.

no code implementations • ICML 2018 • Dominik Janzing, Bernhard Schoelkopf

We consider linear models where $d$ potential causes $X_1,..., X_d$ are correlated with one target quantity $Y$ and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes.

no code implementations • 11 Feb 2018 • Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin

We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs).

no code implementations • ICLR 2018 • Michel Besserve, Dominik Janzing, Bernhard Schoelkopf

Generative models are important tools to capture and investigate the properties of complex empirical data.

no code implementations • 5 Dec 2017 • Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schoelkopf, Manuel Gomez-Rodriguez

Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?

1 code implementation • 27 Nov 2017 • Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schoelkopf, Manuel Gomez-Rodriguez

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.

10 code implementations • ICLR 2018 • Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf

We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution.

no code implementations • 30 Jun 2017 • Paul K. Rubenstein, Ilya Tolstikhin, Philipp Hennig, Bernhard Schoelkopf

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified.

1 code implementation • 22 May 2017 • Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, Bernhard Schoelkopf

We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.

no code implementations • 5 Apr 2017 • Dominik Janzing, Bernhard Schoelkopf

We study a model where one target variable Y is correlated with a vector X:=(X_1,..., X_d) of predictor variables being potential causes of Y.

no code implementations • 29 Aug 2016 • Paul K. Rubenstein, Stephan Bongers, Bernhard Schoelkopf, Joris M. Mooij

Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.

no code implementations • 4 Mar 2016 • Philipp Geiger, Lucian Carata, Bernhard Schoelkopf

Cloud computing involves complex technical and economical systems and interactions.

no code implementations • 9 Aug 2014 • Joris Mooij, Dominik Janzing, Bernhard Schoelkopf

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).

no code implementations • 9 Aug 2014 • Krikamol Muandet, Bernhard Schoelkopf

We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.

no code implementations • 12 May 2014 • Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schoelkopf

Can we recover the hidden network structures from these observed cascades?

no code implementations • 29 Nov 2013 • Mikhail Langovoy, Michael Habeck, Bernhard Schoelkopf

Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies.

no code implementations • 26 Sep 2013 • Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schoelkopf

We propose a kernel method to identify finite mixtures of nonparametric product distributions.

no code implementations • 15 May 2013 • Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf

Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases.

1 code implementation • 27 Jun 2012 • Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij

We consider the problem of function estimation in the case where an underlying causal model can be inferred.

no code implementations • 15 Mar 2012 • Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schoelkopf

We consider two variables that are related to each other by an invertible function.

1 code implementation • 14 Feb 2012 • Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf

Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.

no code implementations • 23 Apr 2008 • Dominik Janzing, Bernhard Schoelkopf

We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs.

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