
Detecting noncausal artifacts in multivariate linear regression models
We consider linear models where d potential causes X_1,...,X_d are corre...
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On the Latent Space of Wasserstein AutoEncoders
We study the role of latent space dimensionality in Wasserstein autoenc...
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Optimizing Human Learning
Spaced repetition is a technique for efficient memorization which uses r...
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Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Online social networking sites are experimenting with the following crow...
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Wasserstein AutoEncoders
We propose the Wasserstein AutoEncoder (WAE)a new algorithm for buil...
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Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children fro...
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From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transp...
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Detecting confounding in multivariate linear models via spectral analysis
We study a model where one target variable Y is correlated with a vector...
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Causal inference for cloud computing
Cloud computing involves complex technical and economical systems and in...
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OneClass Support Measure Machines for Group Anomaly Detection
We propose oneclass support measure machines (OCSMMs) for group anomaly...
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From Ordinary Differential Equations to Structural Causal Models: the deterministic case
We show how, and under which conditions, the equilibrium states of a fir...
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Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Softthresholding Algorithm
Information spreads across social and technological networks, but often ...
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Adaptive nonparametric detection in cryoelectron microscopy
Cryoelectron microscopy (cryoEM) is an emerging experimental method to...
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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric ...
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Modeling Information Propagation with Survival Theory
Networks provide a skeleton for the spread of contagions, like, informat...
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On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
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Identifying confounders using additive noise models
We propose a method for inferring the existence of a latent common cause...
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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
In nonlinear latent variable models or dynamic models, if we consider th...
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Inferring deterministic causal relations
We consider two variables that are related to each other by an invertibl...
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Kernelbased Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
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Identifiability of Causal Graphs using Functional Models
This work addresses the following question: Under what assumptions on th...
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Detecting lowcomplexity unobserved causes
We describe a method that infers whether statistical dependences between...
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Distinguishing Cause and Effect via Second Order Exponential Models
We propose a method to infer causal structures containing both discrete ...
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Telling cause from effect based on highdimensional observations
We describe a method for inferring linear causal relations among multid...
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Statistical Learning Theory: Models, Concepts, and Results
Statistical learning theory provides the theoretical basis for many of t...
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Causal inference using the algorithmic Markov condition
Inferring the causal structure that links n observables is usually based...
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Bernhard Schoelkopf
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Director at the Max Planck Institute for Intelligent Systems