
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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Algorithmic Recourse: from Counterfactual Explanations to Interventions
As machine learning is increasingly used to inform consequential decisio...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to samplebased stochastic optimi...
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A New DistributionFree Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel proba...
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Causality for Machine Learning
Graphical causal inference as pioneered by Judea Pearl arose from resear...
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KernelGuided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
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Kernel Stein Tests for Multiple Model Comparison
We address the problem of nonparametric multiple model comparison: give...
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Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions....
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Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environmen...
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Real Time Trajectory Prediction Using Deep Conditional Generative Models
Data driven methods for time series forecasting that quantify uncertaint...
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Reliable Real Time Ball Tracking for Robot Table Tennis
Robot table tennis systems require a vision system that can track the ba...
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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
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Disentangled State Space Representations
Sequential data often originates from diverse domains across which stati...
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On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used ...
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SemiSupervised Learning, Causality and the Conditional Cluster Assumption
While the success of semisupervised learning (SSL) is still not fully u...
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Bernhard Schölkopf
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Director at Max Planck Institute for Intelligent Systems