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1 code implementation • 16 Mar 2023 • Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

Component-based development is one of the core principles behind modern software engineering practices.

no code implementations • 11 Mar 2023 • Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf

We then use hyperspherical uniformity (which characterizes the degree of uniformity on the unit hypersphere) as a unified framework to quantify these two objectives.

no code implementations • 3 Mar 2023 • Simon Guist, Jan Schneider, Alexander Dittrich, Vincent Berenz, Bernhard Schölkopf, Dieter Büchler

Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort.

no code implementations • 21 Feb 2023 • Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt, Bernhard Schölkopf, Zeynep Akata

Bayesian deep learning approaches that allow uncertainty estimation for regression problems often converge slowly and yield poorly calibrated uncertainty estimates that can not be effectively used for quantification.

no code implementations • 10 Feb 2023 • Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf

Modern machine learning approaches excel in static settings where a large amount of i. i. d.

2 code implementations • 7 Feb 2023 • Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf, Setareh Maghsudi

Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome.

no code implementations • 31 Jan 2023 • Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf

Our goal is to understand the implications of passive symmetries for machine learning: Which passive symmetries play a role (e. g., permutation symmetry in graph neural networks)?

1 code implementation • 27 Jan 2023 • Flavio Schneider, Zhijing Jin, Bernhard Schölkopf

In our work, we investigate the potential of diffusion models for text-conditional music generation.

1 code implementation • 26 Jan 2023 • Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato

It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks.

no code implementations • 20 Jan 2023 • Simon Buchholz, Jonas M. Kübler, Bernhard Schölkopf

Multi armed bandits are one of the theoretical pillars of reinforcement learning.

1 code implementation • 12 Jan 2023 • Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello

Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions.

no code implementations • 20 Dec 2022 • Justus Mattern, Zhijing Jin, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf

Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.

1 code implementation • 14 Dec 2022 • Armin Kekić, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.

no code implementations • 13 Dec 2022 • Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

Explainability has become a central requirement for the development, deployment, and adoption of machine learning (ML) models and we are yet to understand what explanation methods can and cannot do.

no code implementations • 16 Nov 2022 • Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy.

2 code implementations • 7 Nov 2022 • Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

We perform a comprehensive experimental evaluation on synthetic data that demonstrates that FED-CD enables effective aggregation of decentralized data for causal discovery without direct sample sharing, even when the contributing distributed data sets cover disjoint sets of interventions.

no code implementations • 4 Nov 2022 • Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.

no code implementations • 31 Oct 2022 • Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i. e., a pool of finite samples), which greatly limits the teacher's capability.

no code implementations • 29 Oct 2022 • Ziyu Wang, Yucen Luo, Yueru Li, Jun Zhu, Bernhard Schölkopf

For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used.

1 code implementation • 21 Oct 2022 • Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Schölkopf, Mrinmaya Sachan

By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.

no code implementations • 14 Oct 2022 • Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities.

1 code implementation • 13 Oct 2022 • Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.

no code implementations • 11 Oct 2022 • Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence.

1 code implementation • 4 Oct 2022 • Zhijing Jin, Sydney Levine, Fernando Gonzalez, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, Bernhard Schölkopf

Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments.

1 code implementation • 1 Oct 2022 • Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation.

no code implementations • 29 Sep 2022 • Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.

no code implementations • 12 Aug 2022 • Simon Buchholz, Michel Besserve, Bernhard Schölkopf

Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings.

2 code implementations • 3 Aug 2022 • Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato

Normalizing flows are tractable density models that can approximate complicated target distributions, e. g. Boltzmann distributions of physical systems.

1 code implementation • 25 Jul 2022 • Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf

How can we acquire world models that veridically represent the outside world both in terms of what is there and in terms of how our actions affect it?

no code implementations • 22 Jul 2022 • Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf

Deep neural networks perform well on classification tasks where data streams are i. i. d.

2 code implementations • 20 Jul 2022 • Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.

no code implementations • 20 Jul 2022 • Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller, Bernhard Schölkopf

This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images.

1 code implementation • 19 Jul 2022 • Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e. g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations.

Adversarial Robustness
Out-of-Distribution Generalization
**+1**

no code implementations • 13 Jul 2022 • Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper, Luigi Gresele, Bernhard Schölkopf

One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases.

1 code implementation • 11 Jul 2022 • Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf

Important problems in causal inference, economics, and, more generally, robust machine learning can be expressed as conditional moment restrictions, but estimation becomes challenging as it requires solving a continuum of unconditional moment restrictions.

no code implementations • 22 Jun 2022 • Anson Lei, Bernhard Schölkopf, Ingmar Posner

In doing so, VCD significantly extends the capabilities of the current state-of-the-art in latent world models while also comparing favourably in terms of prediction accuracy.

3 code implementations • 17 Jun 2022 • Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf

Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.

no code implementations • 7 Jun 2022 • Aniket Das, Bernhard Schölkopf, Michael Muehlebach

We obtain tight convergence rates for RR and SO and demonstrate that these strategies lead to faster convergence than uniform sampling.

1 code implementation • 6 Jun 2022 • Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve

Leveraging self-consistency, we show that the ELBO converges to a regularized log-likelihood.

1 code implementation • 3 Jun 2022 • Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause

Inferring causal structures from experimentation is a central task in many domains.

1 code implementation • 25 May 2022 • Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf

Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data.

1 code implementation • NAACL 2022 • Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, Bernhard Schölkopf

We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.

1 code implementation • 7 Apr 2022 • Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf

Our HSR-based method provides an alternative, flexible and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.

no code implementations • 1 Apr 2022 • Bernhard Schölkopf, Julius von Kügelgen

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality.

no code implementations • 29 Mar 2022 • Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, Ferenc Huszár

It is known that under i.\, i.\, d assumption, even with infinite data, there is a limit to how fine-grained a causal structure we can identify.

no code implementations • ICLR 2022 • Sidak Pal Singh, Aurelien Lucchi, Thomas Hofmann, Bernhard Schölkopf

`Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized.

no code implementations • CVPR 2022 • Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.

no code implementations • 8 Mar 2022 • Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models.

1 code implementation • 3 Mar 2022 • Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer

Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target.

1 code implementation • 28 Feb 2022 • Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf

In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).

no code implementations • 14 Feb 2022 • Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

Model identifiability is a desirable property in the context of unsupervised representation learning.

1 code implementation • 2 Feb 2022 • Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing

We introduce an approach to counterfactual inference based on merging information from multiple datasets.

no code implementations • 31 Jan 2022 • Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello

Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge.

no code implementations • 15 Jan 2022 • Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf

Model-free and model-based reinforcement learning are two ends of a spectrum.

1 code implementation • 21 Dec 2021 • Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf

Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems.

1 code implementation • 10 Dec 2021 • Michel Besserve, Bernhard Schölkopf

Complex systems often contain feedback loops that can be described as cyclic causal models.

1 code implementation • CVPR 2022 • HANLIN ZHANG, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG).

no code implementations • ICLR 2022 • Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke

We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data.

no code implementations • 29 Oct 2021 • Sumedh A Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf

Extending the successes in supervised learning methods to the reinforcement learning (RL) setting, however, is difficult due to the data generating process - RL agents actively query their environment for data, and the data are a function of the policy followed by the agent.

Out of Distribution (OOD) Detection Reinforcement Learning (RL)

no code implementations • 29 Oct 2021 • Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf

A new perspective has been provided based on the principle of Independence of Causal Mechanisms (ICM), leading to the Spectral Independence Criterion (SIC), postulating that the power spectral density (PSD) of the cause time series is uncorrelated with the squared modulus of the frequency response of the filter generating the effect.

1 code implementation • 29 Oct 2021 • Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato

Normalizing flows are a popular class of models for approximating probability distributions.

Ranked #42 on Image Generation on CIFAR-10 (bits/dimension metric)

no code implementations • NeurIPS 2021 • Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller

In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.

no code implementations • 26 Oct 2021 • Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

We provide a functional view of distributional robustness motivated by robust statistics and functional analysis.

no code implementations • 13 Oct 2021 • Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf

Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.

no code implementations • 12 Oct 2021 • Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.

no code implementations • NeurIPS 2021 • Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution.

no code implementations • ICLR 2022 • Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.

no code implementations • NeurIPS Workshop SVRHM 2021 • Yukun Chen, Andrea Dittadi, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf

Disentanglement is hypothesized to be beneficial towards a number of downstream tasks.

1 code implementation • EMNLP 2021 • Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf

The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.

no code implementations • 5 Oct 2021 • Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Schölkopf, Laura Leal-Taixé, Xiao Xiang Zhu

It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection.

no code implementations • ICLR 2022 • Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf

Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods.

no code implementations • 29 Sep 2021 • Giulia Lanzillotta, Felix Leeb, Stefan Bauer, Bernhard Schölkopf

Autoencoders have played a crucial role in the field of representation learning since its inception, proving to be a flexible learning scheme able to accommodate various notions of optimality of the representation.

1 code implementation • 13 Sep 2021 • Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf

The predominant approach in reinforcement learning is to assign credit to actions based on the expected return.

1 code implementation • 6 Sep 2021 • Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke

Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.

1 code implementation • ICLR 2022 • Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.

no code implementations • ICLR 2022 • Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

By training 240 representations and over 10, 000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents.

1 code implementation • ICLR 2022 • Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf

Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain.

no code implementations • NeurIPS 2021 • Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter Gehler

; and (ii) if the new predictions differ from the current ones, should we update?

1 code implementation • 1 Jul 2021 • Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello

The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations.

1 code implementation • 30 Jun 2021 • Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf

Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods.

no code implementations • 24 Jun 2021 • Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks.

no code implementations • 23 Jun 2021 • Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning.

no code implementations • 22 Jun 2021 • Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf

Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.

15 code implementations • CVPR 2022 • Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler

Being able to spot defective parts is a critical component in large-scale industrial manufacturing.

Ranked #2 on Anomaly Detection on MVTec AD

no code implementations • ICML Workshop URL 2021 • Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence.

Out-of-Distribution Generalization
reinforcement-learning
**+2**

1 code implementation • ICLR 2022 • Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning.

1 code implementation • NeurIPS 2021 • Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve

Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process.

1 code implementation • NeurIPS 2021 • Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant.

Ranked #1 on Image Classification on Causal3DIdent

2 code implementations • NeurIPS 2021 • Maximilian Seitzer, Bernhard Schölkopf, Georg Martius

Many reinforcement learning (RL) environments consist of independent entities that interact sparsely.

1 code implementation • 7 Jun 2021 • Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi

Kernel maximum moment restriction (KMMR) recently emerges as a popular framework for instrumental variable (IV) based conditional moment restriction (CMR) models with important applications in conditional moment (CM) testing and parameter estimation for IV regression and proximal causal learning.

1 code implementation • NeurIPS 2021 • Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf

Quantum computers offer the possibility to efficiently compute inner products of exponentially large density operators that are classically hard to compute.

no code implementations • 29 May 2021 • Korbinian Abstreiter, Sarthak Mittal, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising.

2 code implementations • NeurIPS 2021 • Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause

In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.

no code implementations • 18 May 2021 • Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks.

1 code implementation • NeurIPS 2021 • Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause

These regret bounds illuminate the relationship between the number of evaluations, the domain size (i. e. cardinality of finite domains / Lipschitz constant of the covariance function in continuous domains), and the optimality of the retrieved function value.

1 code implementation • 24 Mar 2021 • Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer

Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak.

no code implementations • 9 Mar 2021 • Georgios Arvanitidis, Bogdan Georgiev, Bernhard Schölkopf

In this work we propose a surrogate conformal Riemannian metric in the latent space of a generative model that is simple, efficient and robust.

1 code implementation • 2 Mar 2021 • Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller

Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.

no code implementations • 24 Feb 2021 • Chaochao Lu, Yuhuai Wu, Jośe Miguel Hernández-Lobato, Bernhard Schölkopf

Finally, in the discussion, we further explore the aforementioned assumption and propose a more general hypothesis, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes.

no code implementations • 23 Feb 2021 • Maximilian Mordig, Riccardo Della Vecchia, Nicolò Cesa-Bianchi, Bernhard Schölkopf

Our setting is motivated by a PhD market of students, advisors, and co-advisors, and can be generalized to supply chain networks viewed as $n$-sided markets.

Computer Science and Game Theory Theoretical Economics Combinatorics

no code implementations • 22 Feb 2021 • Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

The two fields of machine learning and graphical causality arose and developed separately.

1 code implementation • 16 Feb 2021 • Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf

We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization.

no code implementations • 16 Feb 2021 • Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean.

1 code implementation • 12 Feb 2021 • Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis

Riemannian manifolds provide a principled way to model nonlinear geometric structure inherent in data.

1 code implementation • 10 Feb 2021 • Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

That is, the test set is used to simultaneously estimate the expectations and define the basis points, while the training set only serves to select the kernel and is discarded.

no code implementations • ICLR 2021 • Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.

no code implementations • ICLR 2021 • Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf

By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience.

no code implementations • 1 Jan 2021 • Lennart Schlieder, Heiner Kremer, Valentin Volchkov, Kai Melde, Peer Fischer, Bernhard Schölkopf

Instead of an iterative optimization algorithm that converges to a (sub-)optimal solution, the inverse problem can be solved by training a neural network to directly estimate the inverse operator.

no code implementations • ICLR 2021 • Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution.

no code implementations • 1 Jan 2021 • Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

no code implementations • 1 Jan 2021 • Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf

As an alternative, we propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution generalization in the nonlinear setting (i. e., nonlinear representations and nonlinear classifiers).

no code implementations • 16 Dec 2020 • Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf

We propose counterfactual RL algorithms to learn both population-level and individual-level policies.

no code implementations • 3 Dec 2020 • Michel Besserve, Simon Buchholz, Bernhard Schölkopf

Large-scale testing is considered key to assess the state of the current COVID-19 pandemic.

Applications Populations and Evolution

no code implementations • NeurIPS 2020 • Atalanti Mastakouri, Bernhard Schölkopf

In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states.

no code implementations • 30 Oct 2020 • Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution.

no code implementations • 27 Oct 2020 • Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.

no code implementations • ICLR 2021 • Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning.

1 code implementation • ICLR 2021 • Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

1 code implementation • 15 Oct 2020 • Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet

We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction (CMR) known as a maximum moment restriction (MMR).

no code implementations • 14 Oct 2020 • Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wüthrich, Bernhard Schölkopf

This \emph{meta-representation}, which is computed from a few observed examples of the underlying function, is learned jointly with the predictive model.

1 code implementation • 13 Oct 2020 • Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf

Algorithmic fairness is typically studied from the perspective of predictions.

no code implementations • 12 Oct 2020 • Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf

The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star.

no code implementations • 8 Oct 2020 • Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives.

1 code implementation • ICLR 2021 • Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Yoshua Bengio, Bernhard Schölkopf, Manuel Wüthrich, Stefan Bauer

To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment.

1 code implementation • 7 Oct 2020 • Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf

Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.

no code implementations • 28 Sep 2020 • Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf

Few-shot-learning seeks to find models that are capable of fast-adaptation to novel tasks which are not encountered during training.

3 code implementations • ICLR 2021 • Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf

In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning.

no code implementations • 31 Aug 2020 • Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients.

no code implementations • 23 Aug 2020 • Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf

A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.

2 code implementations • 8 Aug 2020 • Manuel Wüthrich, Felix Widmaier, Felix Grimminger, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer

Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.

no code implementations • 2 Aug 2020 • Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf

A common assumption in generative models is that the generator immerses the latent space into a Euclidean ambient space.

no code implementations • 28 Jul 2020 • Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision.

no code implementations • 13 Jul 2020 • Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.

no code implementations • 6 Jul 2020 • Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.

1 code implementation • NeurIPS 2020 • Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen

Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.

no code implementations • 16 Jun 2020 • Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey

More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose reproducing kernel Hilbert space (RKHS) functions vanish at infinity, metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (i. s. p. d.)

2 code implementations • 14 Jun 2020 • Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer

The focus of disentanglement approaches has been on identifying independent factors of variation in data.

no code implementations • 14 Jun 2020 • Felix Leeb, Guilia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

We study the problem of self-supervised structured representation learning using autoencoders for generative modeling.

2 code implementations • 12 Jun 2020 • Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf

We prove a theorem that generalizes the classical duality in the mathematical problem of moments.

1 code implementation • NeurIPS 2020 • Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.

no code implementations • 10 Jun 2020 • Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters

This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system despite the control challenges and (c) train robots to play table tennis without real balls.

1 code implementation • 6 Jun 2020 • Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf

We provide theoretical results on the class of systems that can be treated with the proposed algorithm and empirically evaluate the effectiveness of our method using an exemplary dynamical system.

1 code implementation • NeurIPS 2020 • Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

Modern large-scale kernel-based tests such as maximum mean discrepancy (MMD) and kernelized Stein discrepancy (KSD) optimize kernel hyperparameters on a held-out sample via data splitting to obtain the most powerful test statistics.

1 code implementation • 20 May 2020 • Rui Patrick Xian, Vincent Stimper, Marios Zacharias, Shuo Dong, Maciej Dendzik, Samuel Beaulieu, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, Ralph Ernstorfer

Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.

Data Analysis, Statistics and Probability Materials Science Computational Physics

no code implementations • 18 May 2020 • Atalanti A. Mastakouri, Bernhard Schölkopf, Dominik Janzing

We study the identification of direct and indirect causes on time series and provide conditions in the presence of latent variables, which we prove to be necessary and sufficient under some graph constraints.

1 code implementation • 14 May 2020 • Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf

We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.

Applications Methodology

no code implementations • 13 May 2020 • Louis Abraham, Gary Bécigneul, Bernhard Schölkopf

We study the problem usually referred to as group testing in the context of COVID-19.

no code implementations • ICLR Workshop LLD 2019 • Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.

no code implementations • 27 Apr 2020 • Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf

Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world.

2 code implementations • 15 Apr 2020 • Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez

Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.

no code implementations • 1 Apr 2020 • Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments?

no code implementations • 31 Mar 2020 • Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf

In order to anticipate rare and impactful events, we propose to quantify the worst-case risk under distributional ambiguity using a recent development in kernel methods -- the kernel mean embedding.

1 code implementation • 5 Mar 2020 • Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations.

1 code implementation • 26 Feb 2020 • Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf

We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies.

Human-Computer Interaction Neurons and Cognition 68U35 H.5.2

1 code implementation • 24 Feb 2020 • Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf

We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function $p(y|x)$ and a joint sample, decide whether the sample is drawn from $p(y|x)r_x(x)$ for some density $r_x$.

2 code implementations • 14 Feb 2020 • Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera

As machine learning is increasingly used to inform consequential decision-making (e. g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision.

3 code implementations • ICML 2020 • Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets.

1 code implementation • L4DC 2020 • Jia-Jie Zhu, Moritz Diehl, Bernhard Schölkopf

We apply kernel mean embedding methods to sample-based stochastic optimization and control.

no code implementations • NeurIPS 2019 • Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing

We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target.

no code implementations • NeurIPS 2019 • Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann

We employ a so-called frozen noise paradigm enabling us to compare human performance with four different algorithms on a trial-by-trial basis: A causal inference algorithm exploiting the dependence structure of additive noise terms, a neurally inspired network, a Bayesian ideal observer model as well as a simple heuristic.

1 code implementation • 25 Nov 2019 • Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf

This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems.

1 code implementation • 24 Nov 2019 • Bernhard Schölkopf

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning.

no code implementations • 29 Oct 2019 • Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf

Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance.

3 code implementations • NeurIPS 2019 • Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum

The first test, building on the post selection inference framework, provably controls the number of best models that are wrongly declared worse (false positive rate).

no code implementations • 9 Oct 2019 • Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller

We study the problem of causal discovery through targeted interventions.

2 code implementations • 2 Oct 2019 • Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael C. Mozer, Chris Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

no code implementations • 25 Sep 2019 • Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf

Model-free and model-based reinforcement learning are two ends of a spectrum.

Model-based Reinforcement Learning Reinforcement Learning (RL)

3 code implementations • ICLR 2021 • Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf

Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes.

1 code implementation • 26 Jun 2019 • Vincent Stimper, Stefan Bauer, Ralph Ernstorfer, Bernhard Schölkopf, R. Patrick Xian

Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision.

3 code implementations • NeurIPS 2019 • Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.