Search Results for author: Bernhard Schölkopf

Found 292 papers, 113 papers with code

Dataflow graphs as complete causal graphs

1 code implementation16 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.

Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap

no code implementations11 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.

Posterior Annealing: Fast Calibrated Uncertainty for Regression

no code implementations21 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.

Denoising Image Super-Resolution +1

On the Interventional Kullback-Leibler Divergence

no code implementations10 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.

Robustness Implies Fairness in Causal Algorithmic Recourse

2 code implementations7 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.

Adversarial Robustness Fairness

The passive symmetries of machine learning

no code implementations31 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)?

Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion

1 code implementation27 Jan 2023 Flavio Schneider, Zhijing Jin, Bernhard Schölkopf

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

Image Generation Music Generation +1

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

1 code implementation12 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.

Representation Learning

Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing

no code implementations20 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.


Evaluating vaccine allocation strategies using simulation-assisted causal modelling

1 code implementation14 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.

On the Relationship Between Explanation and Prediction: A Causal View

no code implementations13 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.

Causal Inference

Adapting to noise distribution shifts in flow-based gravitational-wave inference

no code implementations16 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.

FED-CD: Federated Causal Discovery from Interventional and Observational Data

2 code implementations7 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.

Causal Discovery Privacy Preserving

A General Purpose Neural Architecture for Geospatial Systems

no code implementations4 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.

Disaster Response Humanitarian +1

Iterative Teaching by Data Hallucination

no code implementations31 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.

Spectral Representation Learning for Conditional Moment Models

no code implementations29 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.

Causal Inference Representation Learning

A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models

1 code implementation21 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.

Mathematical Reasoning

Neural Attentive Circuits

no code implementations14 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.

Point Cloud Classification text-classification +1

On the Identifiability and Estimation of Causal Location-Scale Noise Models

1 code implementation13 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., $Y = f(X) + g(X)N$.

Causal Discovery Causal Inference

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

no code implementations11 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.

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

1 code implementation4 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.

Language Modelling Question Answering

Function Classes for Identifiable Nonlinear Independent Component Analysis

no code implementations12 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.

Flow Annealed Importance Sampling Bootstrap

2 code implementations3 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.

Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions

1 code implementation25 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?

Representation Learning Trajectory Prediction

Probable Domain Generalization via Quantile Risk Minimization

2 code implementations20 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 $\alpha$.

Domain Generalization

Structural Causal 3D Reconstruction

no code implementations20 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.

3D Object Reconstruction 3D Reconstruction +2

Assaying Out-Of-Distribution Generalization in Transfer Learning

1 code implementation19 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

Probing the Robustness of Independent Mechanism Analysis for Representation Learning

no code implementations13 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.

Representation Learning

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

1 code implementation11 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.

BIG-bench Machine Learning Causal Inference

Variational Causal Dynamics: Discovering Modular World Models from Interventions

no code implementations22 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.

Causal Discovery Variational Inference

AutoML Two-Sample Test

3 code implementations17 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.

AutoML Two-sample testing

Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

no code implementations7 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.

Amortized Inference for Causal Structure Learning

1 code implementation25 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.

Causal Discovery Inductive Bias +1

Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance

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.

Machine Translation Translation

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

1 code implementation7 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.

Denoising Time Series Analysis

From Statistical to Causal Learning

no code implementations1 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.

BIG-bench Machine Learning

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data

no code implementations29 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.

Causal Inference

Phenomenology of Double Descent in Finite-Width Neural Networks

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.

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

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.


Score matching enables causal discovery of nonlinear additive noise models

no code implementations8 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.

Causal Discovery

Interventions, Where and How? Experimental Design for Causal Models at Scale

1 code implementation3 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.

Causal Discovery Experimental Design

Logical Fallacy Detection

1 code implementation28 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).

Language Modelling Logical Fallacies +2

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks

no code implementations31 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.

reinforcement-learning Reinforcement Learning (RL)

On the Adversarial Robustness of Causal Algorithmic Recourse

1 code implementation21 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.

Adversarial Robustness Decision Making

Learning soft interventions in complex equilibrium systems

1 code implementation10 Dec 2021 Michel Besserve, Bernhard Schölkopf

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

Towards Principled Disentanglement for Domain Generalization

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).

Disentanglement Domain Generalization

Group equivariant neural posterior estimation

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.

GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL

no code implementations29 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)

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations

no code implementations29 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.

Causal Discovery Causal Inference +1

Resampling Base Distributions of Normalizing Flows

1 code implementation29 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)

Density Estimation Image Generation

Iterative Teaching by Label Synthesis

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.

Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control

no code implementations26 Oct 2021 Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

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

Action-Sufficient State Representation Learning for Control with Structural Constraints

no code implementations12 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.

Decision Making Representation Learning

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

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.

Trajectory Prediction

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

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.

Causal Inference Domain Adaptation

Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

no code implementations5 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.

Change Detection

On the interventional consistency of autoencoders

no code implementations29 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.


Direct Advantage Estimation

1 code implementation13 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.

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

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.

Representation Learning

The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents

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.

Reinforcement Learning (RL) Representation Learning

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

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.

Source-Free Domain Adaptation

Generalization and Robustness Implications in Object-Centric Learning

1 code implementation1 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.

Inductive Bias Representation Learning +1

Exploring the Latent Space of Autoencoders with Interventional Assays

1 code implementation30 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.


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

no code implementations24 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.

Real-time gravitational-wave science with neural posterior estimation

no code implementations23 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.

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

no code implementations22 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.

Decision Making

Instrument Space Selection for Kernel Maximum Moment Restriction

1 code implementation7 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.

The Inductive Bias of Quantum Kernels

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.

Inductive Bias Quantum Machine Learning

Diffusion-Based Representation Learning

no code implementations29 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.

Denoising Representation Learning +1

DiBS: Differentiable Bayesian Structure Learning

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.

Causal Discovery Variational Inference

Fast and Slow Learning of Recurrent Independent Mechanisms

no code implementations18 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.


Regret Bounds for Gaussian-Process Optimization in Large Domains

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.

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

1 code implementation24 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.

A prior-based approximate latent Riemannian metric

no code implementations9 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.

Learning with Hyperspherical Uniformity

1 code implementation2 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.

Inductive Bias L2 Regularization

Nonlinear Invariant Risk Minimization: A Causal Approach

no code implementations24 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.

BIG-bench Machine Learning Representation Learning

Finding Stable Matchings in PhD Markets with Consistent Preferences and Cooperative Partners

no code implementations23 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

Adversarially Robust Kernel Smoothing

1 code implementation16 Feb 2021 Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf

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

BIG-bench Machine Learning

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

no code implementations16 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.


Bayesian Quadrature on Riemannian Data Manifolds

1 code implementation12 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.

A Witness Two-Sample Test

1 code implementation10 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.

Two-sample testing

Spatially Structured Recurrent Modules

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.

Starcraft II Video Prediction

Learning to interpret trajectories

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.

Learned residual Gerchberg-Saxton network for computer generated holography

no code implementations1 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.

Dependency Structure Discovery from Interventions

no code implementations1 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.

Invariant Causal Representation Learning

no code implementations1 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).

Out-of-Distribution Generalization Representation Learning

Assaying Large-scale Testing Models to Interpret COVID-19 Case Numbers

no code implementations3 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

Causal analysis of Covid-19 Spread in Germany

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.

Time Series Analysis

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

no code implementations27 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.


On the Transfer of Disentangled Representations in Realistic Settings

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.


Predicting Infectiousness for Proactive Contact Tracing

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).

Instrumental Variable Regression via Kernel Maximum Moment Loss

1 code implementation15 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).


Function Contrastive Learning of Transferable Meta-Representations

no code implementations14 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.

Contrastive Learning Few-Shot Learning

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

no code implementations12 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.

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

no code implementations8 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.

Decision Making Fairness

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

1 code implementation7 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.

Representation Learning Zero-Shot Learning

Function Contrastive Learning of Transferable Representations

no code implementations28 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.

Contrastive Learning Few-Shot Learning

Learning explanations that are hard to vary

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.


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

no code implementations31 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.


Learning Dynamical Systems using Local Stability Priors

no code implementations23 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.

Geometrically Enriched Latent Spaces

no code implementations2 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.

A Commentary on the Unsupervised Learning of Disentangled Representations

no code implementations28 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.

S2RMs: Spatially Structured Recurrent Modules

no code implementations13 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.

Starcraft II Video Prediction

Causal Feature Selection via Orthogonal Search

no code implementations6 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.

Causal Discovery

Metrizing Weak Convergence with Maximum Mean Discrepancies

no code implementations16 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.)

Structure by Architecture: Disentangled Representations without Regularization

no code implementations14 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.


Kernel Distributionally Robust Optimization

2 code implementations12 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.

Stochastic Optimization

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

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.

Learning to Play Table Tennis From Scratch using Muscular Robots

no code implementations10 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.

reinforcement-learning Reinforcement Learning (RL)

Neural Lyapunov Redesign

1 code implementation6 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.

Learning Kernel Tests Without Data Splitting

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.

A machine learning route between band mapping and band structure

1 code implementation20 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

Necessary and sufficient conditions for causal feature selection in time series with latent common causes

no code implementations18 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.

Time Series Analysis

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

1 code implementation14 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

Crackovid: Optimizing Group Testing

no code implementations13 May 2020 Louis Abraham, Gary Bécigneul, Bernhard Schölkopf

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

Disentangling Factors of Variations Using Few Labels

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.

Disentanglement Model Selection

Towards causal generative scene models via competition of experts

no code implementations27 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.

Inductive Bias

Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

2 code implementations15 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.

Point Processes

A theory of independent mechanisms for extrapolation in generative models

no code implementations1 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?

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

no code implementations31 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.

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

1 code implementation5 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.

Gaussian Processes regression

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

1 code implementation26 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

Testing Goodness of Fit of Conditional Density Models with Kernels

1 code implementation24 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 $r_x$.

Two-sample testing

Algorithmic Recourse: from Counterfactual Explanations to Interventions

2 code implementations14 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.

Decision Making

Weakly-Supervised Disentanglement Without Compromises

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.

Disentanglement Fairness

Selecting causal brain features with a single conditional independence test per feature

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.

Perceiving the arrow of time in autoregressive motion

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.

Causal Inference Time Series Analysis

Causality for Machine Learning

1 code implementation24 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.

BIG-bench Machine Learning Causal Inference

Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

no code implementations29 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.

Kernel Stein Tests for Multiple Model Comparison

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).

Learning Neural Causal Models from Unknown Interventions

2 code implementations2 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.


Recurrent Independent Mechanisms

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.

Multidimensional Contrast Limited Adaptive Histogram Equalization

1 code implementation26 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.