
FlipOut: Uncovering Redundant Weights via Sign Flipping
Modern neural networks, although achieving stateoftheart results on m...
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Improving Fair Predictions Using Variational Inference In Causal Models
The importance of algorithmic fairness grows with the increasing impact ...
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Neural Ordinary Differential Equations on Manifolds
Normalizing flows are a powerful technique for obtaining reparameterizab...
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Pruning via Iterative Ranking of Sensitivity Statistics
With the introduction of SNIP [arXiv:1810.02340v2], it has been demonstr...
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Designing Data Augmentation for Simulating Interventions
Machine learning models trained with purely observational data and the p...
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Learning Robust Representations via MultiView Information Bottleneck
The information bottleneck principle provides an informationtheoretic m...
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Reparameterizing Distributions on Lie Groups
Reparameterizable densities are an important way to learn probability di...
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Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
We prove the main rules of causal calculus (also called docalculus) for...
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Sinkhorn AutoEncoders
Optimal Transport offers an alternative to maximum likelihood for learni...
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Explorations in Homeomorphic Variational AutoEncoding
The manifold hypothesis states that many kinds of highdimensional data ...
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Constraintbased Causal Discovery for NonLinear Structural Causal Models with Cycles and Latent Confounders
We address the problem of causal discovery from data, making use of the ...
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Markov Properties for Graphical Models with Cycles and Latent Variables
We investigate probabilistic graphical models that allow for both cycles...
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Patrick Forré
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Professor Faculty of Science Informatics Institute at University of Amsterdam