Search Results for author: Bernhard Schoelkopf

Found 22 papers, 5 papers with code

Tinkering with black boxes: counterfactuals uncover modularity in generative models

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.

Detecting non-causal artifacts in multivariate linear regression models

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.

On the Latent Space of Wasserstein Auto-Encoders

no code implementations11 Feb 2018 Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin

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

Representation Learning

Optimizing Human Learning

no code implementations5 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?

Point Processes

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

1 code implementation27 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.

Fact Checking Misinformation +1

Wasserstein Auto-Encoders

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.

Probabilistic Active Learning of Functions in Structural Causal Models

no code implementations30 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.

Active Learning Causal Discovery

From optimal transport to generative modeling: the VEGAN cookbook

1 code implementation22 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 $P_G$.

Detecting confounding in multivariate linear models via spectral analysis

no code implementations5 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.

From Deterministic ODEs to Dynamic Structural Causal Models

no code implementations29 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.

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

no code implementations9 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).

One-Class Support Measure Machines for Group Anomaly Detection

no code implementations9 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.

Group Anomaly Detection

Adaptive nonparametric detection in cryo-electron microscopy

no code implementations29 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.

Electron Microscopy

Modeling Information Propagation with Survival Theory

no code implementations15 May 2013 Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf

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

On Causal and Anticausal Learning

1 code implementation27 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.

Transfer Learning

Kernel-based Conditional Independence Test and Application in Causal Discovery

1 code implementation14 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.

Causal Discovery

Causal inference using the algorithmic Markov condition

no code implementations23 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.

Causal Inference

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