Bernhard Schölkopf

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Max Planck Society

Director at Max Planck Institute for Intelligent Systems

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

An important component for generalization in machine learning is to unco...
07/17/2021 ∙ by Lukas Schott, et al. ∙ 14 ∙ share

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Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

Learning data representations that are useful for various downstream tas...
07/12/2021 ∙ by Andrea Dittadi, et al. ∙ 49 ∙ share

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Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

Source-free domain adaptation (SFDA) aims to adapt a model trained on la...
07/12/2021 ∙ by Cian Eastwood, et al. ∙ 2 ∙ share

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Backward-Compatible Prediction Updates: A Probabilistic Approach

When machine learning systems meet real world applications, accuracy is ...
07/02/2021 ∙ by Frederik Träuble, et al. ∙ 14 ∙ share

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Generalization and Robustness Implications in Object-Centric Learning

The idea behind object-centric representation learning is that natural s...
07/01/2021 ∙ by Andrea Dittadi, et al. ∙ 1 ∙ share

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Interventional Assays for the Latent Space of Autoencoders

The encoders and decoders of autoencoders effectively project the input ...
06/30/2021 ∙ by Felix Leeb, et al. ∙ 2 ∙ share

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Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics

Random features is a powerful universal function approximator that inher...
06/24/2021 ∙ by Diego Agudelo-España, et al. ∙ 9 ∙ share

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Real-time gravitational-wave science with neural posterior estimation

We demonstrate unprecedented accuracy for rapid gravitational-wave param...
06/23/2021 ∙ by Maximilian Dax, et al. ∙ 38 ∙ share

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Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects

Algorithmic recourse aims to provide actionable recommendations to indiv...
06/22/2021 ∙ by Julius von Kugelgen, et al. ∙ 12 ∙ share

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Towards Total Recall in Industrial Anomaly Detection

Being able to spot defective parts is a critical component in large-scal...
06/15/2021 ∙ by Karsten Roth, et al. ∙ 8 ∙ share

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Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted sign...
06/11/2021 ∙ by Yonggang Zhang, et al. ∙ 12 ∙ share

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Independent mechanism analysis, a new concept?

Independent component analysis provides a principled framework for unsup...
06/09/2021 ∙ by Luigi Gresele, et al. ∙ 6 ∙ share

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Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-supervised representation learning has shown remarkable success in ...
06/08/2021 ∙ by Julius von Kugelgen, et al. ∙ 11 ∙ share

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The Inductive Bias of Quantum Kernels

It has been hypothesized that quantum computers may lend themselves well...
06/07/2021 ∙ by Jonas M. Kübler, et al. ∙ 0 ∙ share

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Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Many reinforcement learning (RL) environments consist of independent ent...
06/07/2021 ∙ by Maximilian Seitzer, et al. ∙ 22 ∙ share

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DiBS: Differentiable Bayesian Structure Learning

Bayesian structure learning allows inferring Bayesian network structure ...
05/25/2021 ∙ by Lars Lorch, et al. ∙ 21 ∙ share

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Fast and Slow Learning of Recurrent Independent Mechanisms

Decomposing knowledge into interchangeable pieces promises a generalizat...
05/18/2021 ∙ by Kanika Madan, et al. ∙ 9 ∙ share

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Regret Bounds for Gaussian-Process Optimization in Large Domains

The goal of this paper is to characterize Gaussian-Process optimization ...
04/29/2021 ∙ by Manuel Wüthrich, et al. ∙ 11 ∙ share

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Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases

Simulating the spread of infectious diseases in human communities is cri...
03/24/2021 ∙ by Arash Mehrjou, et al. ∙ 0 ∙ share

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A prior-based approximate latent Riemannian metric

Stochastic generative models enable us to capture the geometric structur...
03/09/2021 ∙ by Georgios Arvanitidis, et al. ∙ 16 ∙ share

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Learning with Hyperspherical Uniformity

Due to the over-parameterization nature, neural networks are a powerful ...
03/02/2021 ∙ by Weiyang Liu, et al. ∙ 4 ∙ share

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Nonlinear Invariant Risk Minimization: A Causal Approach

Due to spurious correlations, machine learning systems often fail to gen...
02/24/2021 ∙ by Chaochao Lu, et al. ∙ 38 ∙ share

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Multi-Sided Matching Markets with Consistent Preferences and Cooperative Partners

We introduce a variant of the three-sided stable matching problem for a ...
02/23/2021 ∙ by Maximilian Mordig, et al. ∙ 0 ∙ share

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Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...
02/22/2021 ∙ by Bernhard Schölkopf, et al. ∙ 44 ∙ share

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From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing

We study the function approximation aspect of distributionally robust op...
02/16/2021 ∙ by Jia-Jie Zhu, et al. ∙ 5 ∙ share

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Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

We propose to analyse the conditional distributional treatment effect (C...
02/16/2021 ∙ by Junhyung Park, et al. ∙ 8 ∙ share

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Bayesian Quadrature on Riemannian Data Manifolds

Riemannian manifolds provide a principled way to model nonlinear geometr...
02/12/2021 ∙ by Christian Fröhlich, et al. ∙ 14 ∙ share

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An Optimal Witness Function for Two-Sample Testing

We propose data-dependent test statistics based on a one-dimensional wit...
02/10/2021 ∙ by Jonas M. Kübler, et al. ∙ 4 ∙ share

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Two-Sided Matching Markets in the ELLIS 2020 PhD Program

The ELLIS PhD program is a European initiative that supports excellent y...
01/28/2021 ∙ by Maximilian Mordig, et al. ∙ 0 ∙ share

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Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation

Reinforcement learning (RL) algorithms usually require a substantial amo...
12/16/2020 ∙ by Chaochao Lu, et al. ∙ 20 ∙ share

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Assaying Large-scale Testing Models to Interpret Covid-19 Case Numbers. A Cross-country Study

Large-scale testing is considered key to assessing the state of the curr...
12/03/2020 ∙ by Michel Besserve, et al. ∙ 0 ∙ share

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PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation

During the ongoing COVID-19 pandemic, there have been burgeoning efforts...
11/16/2020 ∙ by Gilles Barthe, et al. ∙ 0 ∙ share

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On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...
10/27/2020 ∙ by Andrea Dittadi, et al. ∙ 24 ∙ share

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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...
10/27/2020 ∙ by Francesco Locatello, et al. ∙ 27 ∙ share

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Function Contrastive Learning of Transferable Representations

Few-shot-learning seeks to find models that are capable of fast-adaptati...
10/14/2020 ∙ by Muhammad Waleed Gondal, et al. ∙ 8 ∙ share

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On the Fairness of Causal Algorithmic Recourse

While many recent works have studied the problem of algorithmic fairness...
10/13/2020 ∙ by Julius von Kugelgen, et al. ∙ 3 ∙ share

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Physically constrained causal noise models for high-contrast imaging of exoplanets

The detection of exoplanets in high-contrast imaging (HCI) data hinges o...
10/12/2020 ∙ by Timothy D. Gebhard, et al. ∙ 0 ∙ share

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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...
10/08/2020 ∙ by Ossama Ahmed, et al. ∙ 5 ∙ share

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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

Machine learning is increasingly used to inform decision-making in sensi...
10/08/2020 ∙ by Amir-Hossein Karimi, et al. ∙ 13 ∙ share

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Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning

Humans show an innate ability to learn the regularities of the world thr...
10/07/2020 ∙ by Sumedh A. Sontakke, et al. ∙ 45 ∙ share

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Learning explanations that are hard to vary

In this paper, we investigate the principle that `good explanations are ...
09/01/2020 ∙ by Giambattista Parascandolo, et al. ∙ 40 ∙ share

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Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease c...
08/31/2020 ∙ by Patrick Schwab, et al. ∙ 0 ∙ share

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Learning Dynamical Systems using Local Stability Priors

A coupled computational approach to simultaneously learn a vector field ...
08/23/2020 ∙ by Arash Mehrjou, et al. ∙ 36 ∙ share

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TriFinger: An Open-Source Robot for Learning Dexterity

Dexterous object manipulation remains an open problem in robotics, despi...
08/08/2020 ∙ by Manuel Wüthrich, et al. ∙ 1 ∙ share

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Geometrically Enriched Latent Spaces

A common assumption in generative models is that the generator immerses ...
08/02/2020 ∙ by Georgios Arvanitidis, et al. ∙ 20 ∙ share

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A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

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Causal analysis of Covid-19 spread in Germany

In this work, we study the causal relations among German regions in term...
07/23/2020 ∙ by Atalanti A. Mastakouri, et al. ∙ 0 ∙ share

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Bloom Origami Assays: Practical Group Testing

We study the problem usually referred to as group testing in the context...
07/21/2020 ∙ by Louis Abraham, et al. ∙ 0 ∙ share

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S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...
07/13/2020 ∙ by Nasim Rahaman, et al. ∙ 5 ∙ share

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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. ∙ 47 ∙ share

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Bernhard Schölkopf

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