
Pyfectious: An individuallevel simulator to discover optimal containment polices for epidemic diseases
Simulating the spread of infectious diseases in human communities is cri...
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A priorbased approximate latent Riemannian metric
Stochastic generative models enable us to capture the geometric structur...
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Learning with Hyperspherical Uniformity
Due to the overparameterization nature, neural networks are a powerful ...
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Nonlinear Invariant Risk Minimization: A Causal Approach
Due to spurious correlations, machine learning systems often fail to gen...
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MultiSided Matching Markets with Consistent Preferences and Cooperative Partners
We introduce a variant of the threesided stable matching problem for a ...
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Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing
We study the function approximation aspect of distributionally robust op...
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Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and UStatistic Regression
We propose to analyse the conditional distributional treatment effect (C...
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Bayesian Quadrature on Riemannian Data Manifolds
Riemannian manifolds provide a principled way to model nonlinear geometr...
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An Optimal Witness Function for TwoSample Testing
We propose datadependent test statistics based on a onedimensional wit...
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TwoSided Matching Markets in the ELLIS 2020 PhD Program
The ELLIS PhD program is a European initiative that supports excellent y...
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SampleEfficient Reinforcement Learning via CounterfactualBased Data Augmentation
Reinforcement learning (RL) algorithms usually require a substantial amo...
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Assaying Largescale Testing Models to Interpret Covid19 Case Numbers. A Crosscountry Study
Largescale testing is considered key to assessing the state of the curr...
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PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation
During the ongoing COVID19 pandemic, there have been burgeoning efforts...
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On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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Function Contrastive Learning of Transferable Representations
Fewshotlearning seeks to find models that are capable of fastadaptati...
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On the Fairness of Causal Algorithmic Recourse
While many recent works have studied the problem of algorithmic fairness...
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Physically constrained causal noise models for highcontrast imaging of exoplanets
The detection of exoplanets in highcontrast imaging (HCI) data hinges o...
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CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Despite recent successes of reinforcement learning (RL), it remains a ch...
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decisionmaking in sensi...
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Causal Curiosity: RL Agents Discovering Selfsupervised Experiments for Causal Representation Learning
Humans show an innate ability to learn the regularities of the world thr...
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Learning explanations that are hard to vary
In this paper, we investigate the principle that `good explanations are ...
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Realtime Prediction of COVID19 related Mortality using Electronic Health Records
Coronavirus Disease 2019 (COVID19) is an emerging respiratory disease c...
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Learning Dynamical Systems using Local Stability Priors
A coupled computational approach to simultaneously learn a vector field ...
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TriFinger: An OpenSource Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despi...
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Geometrically Enriched Latent Spaces
A common assumption in generative models is that the generator immerses ...
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A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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Causal analysis of Covid19 spread in Germany
In this work, we study the causal relations among German regions in term...
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Bloom Origami Assays: Practical Group Testing
We study the problem usually referred to as group testing in the context...
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S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a datagenerating process by means of appropr...
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Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
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Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data i...
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Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
Despite impressive progress in the last decade, it still remains an open...
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Structural Autoencoders Improve Representations for Generation and Transfer
We study the problem of structuring a learned representation to signific...
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Kernel Distributionally Robust Optimization
This paper is an indepth investigation of using kernel methods to immun...
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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations...
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Learning to Play Table Tennis From Scratch using Muscular Robots
Dynamic tasks like table tennis are relatively easy to learn for humans ...
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Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems
Learning controllers merely based on a performance metric has been prove...
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Learning Kernel Tests Without Data Splitting
Modern largescale kernelbased tests such as maximum mean discrepancy (...
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Necessary and sufficient conditions for causal feature selection in time series with latent common causes
We study the identification of direct and indirect causes on time series...
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Simpson's paradox in Covid19 case fatality rates: a mediation analysis of agerelated causal effects
We point out an example of Simpson's paradox in COVID19 case fatality r...
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Crackovid: Optimizing Group Testing
We study the problem usually referred to as group testing in the context...
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Towards causal generative scene models via competition of experts
Learning how to model complex scenes in a modular way with recombinable ...
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A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment
We introduce a novel modeling framework for studying epidemics that is s...
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A theory of independent mechanisms for extrapolation in generative models
Deep generative models reproduce complex empirical data but cannot extra...
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WorstCase Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
In order to anticipate rare and impactful events, we propose to quantify...
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SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Gaussian processes are an important regression tool with excellent analy...
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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
Neurophysiological studies are typically conducted in laboratories with ...
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Testing Goodness of Fit of Conditional Density Models with Kernels
We propose two nonparametric statistical tests of goodness of fit for co...
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Bernhard Schölkopf
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Director at Max Planck Institute for Intelligent Systems