
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
read it

Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data i...
read it

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...
read it

Structural Autoencoders Improve Representations for Generation and Transfer
We study the problem of structuring a learned representation to signific...
read it

Kernel Distributionally Robust Optimization
This paper is an indepth investigation of using kernel methods to immun...
read it

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations...
read it

Learning to Play Table Tennis From Scratch using Muscular Robots
Dynamic tasks like table tennis are relatively easy to learn for humans ...
read it

Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems
Learning controllers merely based on a performance metric has been prove...
read it

Learning Kernel Tests Without Data Splitting
Modern largescale kernelbased tests such as maximum mean discrepancy (...
read it

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...
read it

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...
read it

Crackovid: Optimizing Group Testing
We study the problem usually referred to as group testing in the context...
read it

Towards causal generative scene models via competition of experts
Learning how to model complex scenes in a modular way with recombinable ...
read it

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...
read it

A theory of independent mechanisms for extrapolation in generative models
Deep generative models reproduce complex empirical data but cannot extra...
read it

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...
read it

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Gaussian processes are an important regression tool with excellent analy...
read it

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
Neurophysiological studies are typically conducted in laboratories with ...
read it

Testing Goodness of Fit of Conditional Density Models with Kernels
We propose two nonparametric statistical tests of goodness of fit for co...
read it

Algorithmic Recourse: from Counterfactual Explanations to Interventions
As machine learning is increasingly used to inform consequential decisio...
read it

WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
read it

A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to samplebased stochastic optimi...
read it

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...
read it

Causality for Machine Learning
Graphical causal inference as pioneered by Judea Pearl arose from resear...
read it

KernelGuided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
read it

Kernel Stein Tests for Multiple Model Comparison
We address the problem of nonparametric multiple model comparison: give...
read it

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions....
read it

Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environmen...
read it

Real Time Trajectory Prediction Using Deep Conditional Generative Models
Data driven methods for time series forecasting that quantify uncertaint...
read it

Reliable Real Time Ball Tracking for Robot Table Tennis
Robot table tennis systems require a vision system that can track the ba...
read it

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with structurally disent...
read it

Disentangled State Space Representations
Sequential data often originates from diverse domains across which stati...
read it

On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
read it

Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used ...
read it

SemiSupervised Learning, Causality and the Conditional Cluster Assumption
While the success of semisupervised learning (SSL) is still not fully u...
read it

Optimal Decision Making Under Strategic Behavior
We are witnessing an increasing use of datadriven predictive models to ...
read it

The Incomplete Rosetta Stone Problem: Identifiability Results for MultiView Nonlinear ICA
We consider the problem of recovering a common latent source with indepe...
read it

Kernel Mean Matching for Content Addressability of GANs
We propose a novel procedure which adds "contentaddressability" to any ...
read it

Consequential Ranking Algorithms and Longterm Welfare
Ranking models are typically designed to provide rankings that optimize ...
read it

Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
read it

Convolutional neural networks: a magic bullet for gravitationalwave detection?
In the last few years, machine learning techniques, in particular convol...
read it

From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoreticallybacked framework...
read it

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variabl...
read it

Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data
It is commonplace to encounter nonstationary or heterogeneous data. Such...
read it

AReS and MaRS  Adversarial and MMDMinimizing Regression for SDEs
Stochastic differential equations are an important modeling class in man...
read it

ODIN: ODEInformed Regression for Parameter and State Inference in TimeContinuous Dynamical Systems
Parameter inference in ordinary differential equations is an important p...
read it

Bayesian Online Detection and Prediction of Change Points
Online detection of instantaneous changes in the generative process of a...
read it

Improving Consequential Decision Making under Imperfect Predictions
Consequential decisions are increasingly informed by sophisticated data...
read it

GeNet: Deep Representations for Metagenomics
We introduce GeNet, a method for shotgun metagenomic classification from...
read it

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
Modern implicit generative models such as generative adversarial network...
read it
Bernhard Schölkopf
is this you? claim profile
Director at Max Planck Institute for Intelligent Systems