Bernhard Schölkopf

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Max Planck Society

Director at Max Planck Institute for Intelligent Systems

Causal Feature Selection via Orthogonal Search

The problem of inferring the direct causal parents of a response variabl...
07/06/2020 ∙ by Anant Raj, et al. ∙ 46 ∙ share

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Relative gradient optimization of the Jacobian term in unsupervised deep learning

Learning expressive probabilistic models correctly describing the data i...
06/26/2020 ∙ by Luigi Gresele, et al. ∙ 13 ∙ share

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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...
06/14/2020 ∙ by Frederik Träuble, et al. ∙ 14 ∙ share

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Structural Autoencoders Improve Representations for Generation and Transfer

We study the problem of structuring a learned representation to signific...
06/14/2020 ∙ by Felix Leeb, et al. ∙ 26 ∙ share

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Kernel Distributionally Robust Optimization

This paper is an in-depth investigation of using kernel methods to immun...
06/12/2020 ∙ by Jia-Jie Zhu, et al. ∙ 0 ∙ share

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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Recent work has discussed the limitations of counterfactual explanations...
06/11/2020 ∙ by Amir-Hossein Karimi, et al. ∙ 19 ∙ share

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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 ...
06/10/2020 ∙ by Dieter Büchler, et al. ∙ 0 ∙ share

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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...
06/06/2020 ∙ by Arash Mehrjou, et al. ∙ 19 ∙ share

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Learning Kernel Tests Without Data Splitting

Modern large-scale kernel-based tests such as maximum mean discrepancy (...
06/03/2020 ∙ by Jonas M. Kübler, et al. ∙ 4 ∙ share

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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...
05/18/2020 ∙ by Atalanti A. Mastakouri, et al. ∙ 4 ∙ share

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Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects

We point out an example of Simpson's paradox in COVID-19 case fatality r...
05/14/2020 ∙ by Julius von Kugelgen, et al. ∙ 0 ∙ share

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Crackovid: Optimizing Group Testing

We study the problem usually referred to as group testing in the context...
05/13/2020 ∙ by Louis Abraham, et al. ∙ 5 ∙ share

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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 ...
04/27/2020 ∙ by Julius von Kugelgen, et al. ∙ 4 ∙ share

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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...
04/15/2020 ∙ by Lars Lorch, et al. ∙ 4 ∙ share

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A theory of independent mechanisms for extrapolation in generative models

Deep generative models reproduce complex empirical data but cannot extra...
04/01/2020 ∙ by Michel Besserve, et al. ∙ 7 ∙ share

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Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

In order to anticipate rare and impactful events, we propose to quantify...
03/31/2020 ∙ by Jia-Jie Zhu, et al. ∙ 0 ∙ share

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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...
03/05/2020 ∙ by Emmanouil Angelis, et al. ∙ 0 ∙ share

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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Neurophysiological studies are typically conducted in laboratories with ...
02/26/2020 ∙ by Matthias R. Hohmann, et al. ∙ 0 ∙ share

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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...
02/24/2020 ∙ by Wittawat Jitkrittum, et al. ∙ 1 ∙ share

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Algorithmic Recourse: from Counterfactual Explanations to Interventions

As machine learning is increasingly used to inform consequential decisio...
02/14/2020 ∙ by Amir-Hossein Karimi, et al. ∙ 7 ∙ share

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Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by obs...
02/07/2020 ∙ by Francesco Locatello, et al. ∙ 20 ∙ share

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A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

We apply kernel mean embedding methods to sample-based stochastic optimi...
01/28/2020 ∙ by Jia-Jie Zhu, et al. ∙ 0 ∙ share

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A New Distribution-Free 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...
11/25/2019 ∙ by Jia-Jie Zhu, et al. ∙ 17 ∙ share

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Causality for Machine Learning

Graphical causal inference as pioneered by Judea Pearl arose from resear...
11/24/2019 ∙ by Bernhard Schölkopf, et al. ∙ 55 ∙ share

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Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

Modern implicit generative models such as generative adversarial network...
10/29/2019 ∙ by Arash Mehrjou, et al. ∙ 16 ∙ share

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Kernel Stein Tests for Multiple Model Comparison

We address the problem of non-parametric multiple model comparison: give...
10/27/2019 ∙ by Jen Ning Lim, et al. ∙ 16 ∙ share

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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....
10/09/2019 ∙ by Julius von Kugelgen, et al. ∙ 39 ∙ share

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Recurrent Independent Mechanisms

Learning modular structures which reflect the dynamics of the environmen...
09/24/2019 ∙ by Anirudh Goyal, et al. ∙ 35 ∙ share

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Real Time Trajectory Prediction Using Deep Conditional Generative Models

Data driven methods for time series forecasting that quantify uncertaint...
09/09/2019 ∙ by Sebastián Gómez-González, et al. ∙ 1 ∙ share

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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...
08/20/2019 ∙ by Sebastián Gómez-González, et al. ∙ 0 ∙ share

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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...
06/07/2019 ∙ by Muhammad Waleed Gondal, et al. ∙ 3 ∙ share

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Disentangled State Space Representations

Sequential data often originates from diverse domains across which stati...
06/07/2019 ∙ by Đorđe Miladinović, et al. ∙ 5 ∙ share

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On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...
05/31/2019 ∙ by Francesco Locatello, et al. ∙ 6 ∙ share

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Quantum Mean Embedding of Probability Distributions

The kernel mean embedding of probability distributions is commonly used ...
05/31/2019 ∙ by Jonas M. Kübler, et al. ∙ 0 ∙ share

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Semi-Supervised Learning, Causality and the Conditional Cluster Assumption

While the success of semi-supervised learning (SSL) is still not fully u...
05/28/2019 ∙ by Julius von Kugelgen, et al. ∙ 7 ∙ share

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Optimal Decision Making Under Strategic Behavior

We are witnessing an increasing use of data-driven predictive models to ...
05/22/2019 ∙ by Moein Khajehnejad, et al. ∙ 5 ∙ share

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The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

We consider the problem of recovering a common latent source with indepe...
05/16/2019 ∙ by Luigi Gresele, et al. ∙ 9 ∙ share

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Kernel Mean Matching for Content Addressability of GANs

We propose a novel procedure which adds "content-addressability" to any ...
05/14/2019 ∙ by Wittawat Jitkrittum, et al. ∙ 7 ∙ share

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Consequential Ranking Algorithms and Long-term Welfare

Ranking models are typically designed to provide rankings that optimize ...
05/13/2019 ∙ by Behzad Tabibian, et al. ∙ 9 ∙ share

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Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...
05/03/2019 ∙ by Francesco Locatello, et al. ∙ 12 ∙ share

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Convolutional neural networks: a magic bullet for gravitational-wave detection?

In the last few years, machine learning techniques, in particular convol...
04/18/2019 ∙ by Timothy D. Gebhard, et al. ∙ 32 ∙ share

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From Variational to Deterministic Autoencoders

Variational Autoencoders (VAEs) provide a theoretically-backed framework...
03/29/2019 ∙ by Partha Ghosh, et al. ∙ 28 ∙ share

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Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

The problem of inferring the direct causal parents of a response variabl...
03/06/2019 ∙ by Anant Raj, et al. ∙ 6 ∙ share

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Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data

It is commonplace to encounter nonstationary or heterogeneous data. Such...
03/05/2019 ∙ by Biwei Huang, et al. ∙ 0 ∙ share

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AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in man...
02/22/2019 ∙ by Gabriele Abbati, et al. ∙ 12 ∙ share

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ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Parameter inference in ordinary differential equations is an important p...
02/17/2019 ∙ by Philippe Wenk, et al. ∙ 10 ∙ share

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Bayesian Online Detection and Prediction of Change Points

Online detection of instantaneous changes in the generative process of a...
02/12/2019 ∙ by Diego Agudelo-España, et al. ∙ 10 ∙ share

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Improving Consequential Decision Making under Imperfect Predictions

Consequential decisions are increasingly informed by sophisticated data-...
02/08/2019 ∙ by Niki Kilbertus, et al. ∙ 6 ∙ share

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GeNet: Deep Representations for Metagenomics

We introduce GeNet, a method for shotgun metagenomic classification from...
01/30/2019 ∙ by Mateo Rojas-Carulla, et al. ∙ 34 ∙ share

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Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Modern implicit generative models such as generative adversarial network...
01/26/2019 ∙ by Arash Mehrjou, et al. ∙ 20 ∙ share

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Bernhard Schölkopf

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