B. Schölkopf

Bernhard Schölkopf, Melanie Nicole Zeilinger, Philipp Hennig, 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

THE POPULATION OF LONG-PERIOD TRANSITING EXOPLANETS

B. Schölkopf, B. Scholkopf, D. Hogg, 2016, 1607.08237.

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows

Bernhard Schölkopf, Clark Glymour, Jiji Zhang, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Julius von Kügelgen, B. Schölkopf, I. Valera, 2020, NeurIPS.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Nonparametric Regression between General Riemannian Manifolds

Bernhard Schölkopf, Florian Steinke, Matthias Hein, 2010, SIAM J. Imaging Sci..

Regression by dependence minimization and its application to causal inference in additive noise models

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2009, ICML '09.

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions

Joris M. Mooij, Stephan Bongers, Jonas Peters, 2016, ArXiv.

Causality : Objectives and Assessment

Isabelle Guyon, Bernhard Schölkopf, Dominik Janzing, 2010 .

Enhancing human learning via spaced repetition optimization

Bernhard Schölkopf, Utkarsh Upadhyay, Behzad Tabibian, 2019, Proceedings of the National Academy of Sciences.

Submodular Inference of Diffusion Networks from Multiple Trees

Bernhard Scholkopf, Manuel Gomez Rodriguez, B. Schölkopf, 2012, ICML.

Uncovering the Temporal Dynamics of Diffusion Networks pdf

Bernhard Schölkopf, David Balduzzi, Manuel Gomez-Rodriguez, 2011, ICML.

Machine Learning Applied to Perception: Decision Images for Gender Classification

Bernhard Schölkopf, Heinrich H. Bülthoff, Felix A. Wichmann, 2004, NIPS.

Statistical Learning and Kernel Methods in Bioinformatics

Isabelle Guyon, Bernhard Schölkopf, Jason Weston, 2003 .

Modeling confounding by half-sibling regression

Bernhard Schölkopf, Dominik Janzing, Daniel Foreman-Mackey, 2016, Proceedings of the National Academy of Sciences.

A Causal, Data-driven Approach to Modeling the Kepler Data

Bernhard Scholkopf, David W. Hogg, Dun Wang, 2015, 1508.01853.

Causal Influence Detection for Improving Efficiency in Reinforcement Learning pdf

Bernhard Schölkopf, Georg Martius, Maximilian Seitzer, 2021, NeurIPS.

Inferring latent structures via information inequalities

Bernhard Schölkopf, Dominik Janzing, Lukas Luft, 2014, UAI.

Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Tempered Adversarial Networks

Bernhard Schölkopf, Mehdi S. M. Sajjadi, B. Schölkopf, 2018, ICML.

Annealed Generative Adversarial Networks pdf

Bernhard Schölkopf, Saeed Saremi, Arash Mehrjou, 2017, ArXiv.

Learning to Find Pre-Images

Bernhard Schölkopf, Jason Weston, Gökhan H. Bakir, 2003, NIPS.

Invariant Models for Causal Transfer Learning

Bernhard Schölkopf, Richard E. Turner, Mateo Rojas-Carulla, 2015, J. Mach. Learn. Res..

Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions

Bernhard Schölkopf, David Corfield, Vladimir Vapnik, 2009 .

Towards Causal Representation Learning

Yoshua Bengio, Francesco Locatello, Anirudh Goyal, 2021, ArXiv.

Weakly-Supervised Disentanglement Without Compromises

Ben Poole, B. Schölkopf, M. Tschannen, 2020, ICML.

Causality for Machine Learning

B. Schölkopf, B. Scholkopf, B. Scholkopf, 2019, Probabilistic and Causal Inference.

Group invariance principles for causal generative models pdf

Bernhard Schölkopf, Dominik Janzing, Michel Besserve, 2017, AISTATS.

Geometrically Enriched Latent Spaces pdf

Bernhard Schölkopf, Søren Hauberg, Georgios Arvanitidis, 2020, AISTATS.

Towards Machine Learning of Motor Skills

Bernhard Schölkopf, Stefan Schaal, Jan Peters, 2007, AMS.

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

B. Schölkopf, Chris Russell, Francesco Locatello, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

O. Chapelle, B. Scholkopf, A. Zien, Eds., 2009, IEEE Transactions on Neural Networks.

Probable Domain Generalization via Quantile Risk Minimization

George J. Pappas, Julius von Kügelgen, B. Schölkopf, 2022, NeurIPS.

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Bernhard Schölkopf, Bharath K. Sriperumbudur, Ilya O. Tolstikhin, 2016, NIPS.

Iterative Teaching by Data Hallucination

Tim Z. Xiao, B. Schölkopf, Adrian Weller, 2022, ArXiv.

Spatio-Temporal Transformer Network for Video Restoration

Bernhard Schölkopf, Tae Hyun Kim, Michael Hirsch, 2018, ECCV.

Structural Causal 3D Reconstruction

B. Schölkopf, B. Scholkopf, L. Paull, 2022, ECCV.

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning pdf

Yoshua Bengio, Bernhard Scholkopf, Stefan Bauer, 2020, ICLR.

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Bernhard Schölkopf, David Lopez-Paz, Yann Ollivier, 2018, ICML.

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

B. Schölkopf, Mrinmaya Sachan, Zhijing Jin, 2022, arXiv.org.

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction pdf

Thomas Brox, Osama Makansi, Dominik Janzing, 2021, ICLR.

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Peter V. Gehler, Bernhard Schölkopf, Carsten Rother, 2011, NIPS.

Unsupervised Object Learning via Common Fate

Julius von Kügelgen, M. Bethge, B. Schölkopf, 2021, CLeaR.

A teacher-student framework to distill future trajectories

Bernhard Schölkopf, Giambattista Parascandolo, Alexander Neitz, 2021, ICLR.

Learning Blind Motion Deblurring

Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Can Large Language Models Distinguish Cause from Effect?

B. Schölkopf, Rada Mihalcea, Mrinmaya Sachan, 2022 .

Towards Causal Algorithmic Recourse

Julius von Kügelgen, B. Schölkopf, I. Valera, 2020, xxAI@ICML.

Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2010, NIPS.

Bridging the Gap to Real-World Object-Centric Learning

B. Schölkopf, T. Brox, Tianjun Xiao, 2022, ICLR.

Fast and Slow Learning of Recurrent Independent Mechanisms pdf

Anirudh Goyal, Kanika Madan, Yoshua Bengio, 2021, ICLR.

CausalAdv: Adversarial Robustness through the Lens of Causality

B. Schölkopf, B. Scholkopf, Mingming Gong, 2021, 2106.06196.

Maximum Moment Restriction for Instrumental Variable Regression

B. Schölkopf, B. Scholkopf, Krikamol Muandet, 2020, ArXiv.

Learning Inverse Dynamics: a Comparison

Bernhard Schölkopf, Jan Peters, Duy Nguyen-Tuong, 2008, ESANN.

Multiplane Diffractive Acoustic Networks

B. Schölkopf, P. Fischer, A. Athanassiadis, 2023, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

Directed Acoustic Assembly in 3D

B. Schölkopf, B. Scholkopf, P. Fischer, 2022, 2210.07153.

Assaying Out-Of-Distribution Generalization in Transfer Learning

B. Schiele, B. Schölkopf, T. Brox, 2022, NeurIPS.

Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study

Bernhard Schölkopf, Sergios Gatidis, Darko Stern, 2021, Comput. Medical Imaging Graph..

Flexible Spatio-Temporal Networks for Video Prediction

Bernhard Schölkopf, Michael Hirsch, Chaochao Lu, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Towards causal generative scene models via competition of experts pdf

Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf, 2020, ArXiv.

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

B. Schölkopf, B. Scholkopf, Markus Freitag, 2022, NAACL.

MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration

Bernhard Schölkopf, Florian Steinke, Jason Farquhar, 2008, Journal of Nuclear Medicine.

Personalized brain-computer interface models for motor rehabilitation

Bernhard Schölkopf, Moritz Grosse-Wentrup, Ozan Özdenizci, 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Kernel Methods for Implicit Surface Modeling

Bernhard Schölkopf, Joachim Giesen, Simon Spalinger, 2004, NIPS.

Implicit surface modelling as an eigenvalue problem

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2005, ICML.

Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2006, NIPS.

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects

Julius von Kügelgen, B. Schölkopf, B. Scholkopf, 2020, IEEE Transactions on Artificial Intelligence.

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

B. Schölkopf, M. Besserve, Simon Buchholz, 2020, 2012.01912.

Avoiding Discrimination through Causal Reasoning

Bernhard Schölkopf, Dominik Janzing, Mateo Rojas-Carulla, 2017, NIPS.

The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution pdf

Bernhard Schölkopf, Michael Hirsch, Muhammad Waleed Gondal, 2018, ECCV Workshops.

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

Julius von Kügelgen, B. Schölkopf, G. Martius, 2022, NeurIPS.

Independent mechanism analysis, a new concept?

Julius von Kügelgen, B. Schölkopf, B. Scholkopf, 2021, NeurIPS.

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

Luigi Gresele, Francesco Locatello, Wieland Brendel, 2021, NeurIPS.

On Disentangled Representations Learned from Correlated Data

B. Schölkopf, Anirudh Goyal, Francesco Locatello, 2020, ICML.

Inferring deterministic causal relations pdf

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2010, UAI.

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

B. Schölkopf, Alexandre Alahi, Chris Russell, 2023, ArXiv.

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks

B. Schölkopf, Stefan Bauer, F. Träuble, 2022, ArXiv.

Adversarially Robust Kernel Smoothing

Bernhard Scholkopf, Yassine Nemmour, Jia-Jie Zhu, 2021, AISTATS.

Efficient filter flow for space-variant multiframe blind deconvolution

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Fisher discriminant analysis with kernels

B. Scholkopf, S. Mika, G. Ratsch, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

Flow Annealed Importance Sampling Bootstrap

José Miguel Hernández-Lobato, B. Schölkopf, B. Scholkopf, 2022, ICLR.

TriFinger: An Open-Source Robot for Learning Dexterity

Ludovic Righetti, Vincent Berenz, Julian Viereck, 2020, CoRL.

Blind retrospective motion correction of MR images

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2012, Magnetic resonance in medicine.

Perceiving the arrow of time in autoregressive motion

Bernhard Schölkopf, Felix A. Wichmann, Kristof Meding, 2019, NeurIPS.

Randomized Nonlinear Component Analysis pdf

Bernhard Schölkopf, Alexander J. Smola, Zoubin Ghahramani, 2014, ICML.

Spectral Representation Learning for Conditional Moment Models

B. Schölkopf, Yucen Luo, Jun Zhu, 2022, ArXiv.

Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

Bernhard Schölkopf, Arash Mehrjou, B. Schölkopf, 2018 .

The Randomized Dependence Coefficient

Bernhard Schölkopf, David Lopez-Paz, Philipp Hennig, 2013, NIPS.

Implicit Wiener Series for Higher-Order Image Analysis

Bernhard Schölkopf, Matthias O. Franz, B. Schölkopf, 2004, NIPS.

A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression

Bernhard Schölkopf, Matthias O. Franz, B. Schölkopf, 2006, Neural Computation.

Adversarial Robustness through the Lens of Causality pdf

Bernhard Schölkopf, Mingming Gong, Xinmei Tian, 2022, ICLR.

Quantifying causal influences

Moritz Grosse-Wentrup, Dominik Janzing, David Balduzzi, 2012, 1203.6502.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Bernhard Schölkopf, Bernd J Pichler, Prateek Katiyar, 2016, Molecular Imaging and Biology.

Building Sparse Large Margin Classifiers

Bernhard Schölkopf, Mingrui Wu, Gökhan H. Bakir, 2005, ICML.

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Richard E. Turner, Zoubin Ghahramani, Bernhard Scholkopf, 2017, NIPS.

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing

B. Schölkopf, Pradeep Ravikumar, Bryon Aragam, 2023, ArXiv.

Nonparametric Identifiability of Causal Representations from Unknown Interventions

Julius von Kügelgen, E. Bareinboim, B. Schölkopf, 2023, ArXiv.

Adaptation and Robust Learning of Probabilistic Movement Primitives

Bernhard Schölkopf, Jan Peters, Gerhard Neumann, 2018, IEEE Transactions on Robotics.

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Stefan Bauer, Bernhard Schölkopf, Giambattista Parascandolo, 2018, NeurIPS.

Iterative Teaching by Label Synthesis

B. Schölkopf, L. Paull, Adrian Weller, 2021, Neural Information Processing Systems.

A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA

Daniel Foreman-Mackey, Bernhard Scholkopf, Dun Wang, 2015, 1502.04715.

Group equivariant neural posterior estimation

Stephen R. Green, B. Schölkopf, J. Macke, 2021, ICLR.

Real-time gravitational-wave science with neural posterior estimation pdf

Jakob H. Macke, Bernhard Scholkopf, Maximilian Dax, 2021, Physical Review Letters.

Real-time gravitational-wave science with neural posterior estimation

Stephen R. Green, B. Schölkopf, J. Macke, 2021, Physical review letters.

Out-of-Variable Generalization

B. Schölkopf, Siyuan Guo, Jonas Wildberger, 2023, ArXiv.

Nonlinear Invariant Risk Minimization: A Causal Approach

José Miguel Hernández-Lobato, Yuhuai Wu, B. Schölkopf, 2021, ArXiv.

Predicting Infectiousness for Proactive Contact Tracing pdf

Tristan Deleu, Tegan Maharaj, Irina Rish, 2020, ICLR.

Approximate dual control maintaining the value of information with an application to building control

Bernhard Schölkopf, Melanie Nicole Zeilinger, Philipp Hennig, 2016, 2016 European Control Conference (ECC).

Identifying Cause and Effect on Discrete Data using Additive Noise Models

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, 2010, AISTATS.

A machine learning route between band mapping and band structure

B. Schölkopf, B. Scholkopf, Stefan Bauer, 2020, Nature Computational Science.

Face Detection Benchmark Database Data Set Location Description

B. Schölkopf, D. Kriegman, Ming-Hsuan Yang, 2007 .

Dataflow graphs as complete causal graphs

Neil D. Lawrence, B. Schölkopf, Andrei Paleyes, 2023, 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN).

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

M. Bethge, B. Schölkopf, B. Moghaddam, 2010, 1009.0779.

Results of the GREAT08 Challenge?: an image analysis competition for cosmological lensing: Results o

M. Bethge, B. Schölkopf, B. Moghaddam, 2009, 0908.0945.

Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Bernhard Schölkopf, Moritz Grosse-Wentrup, Atalanti A. Mastakouri, 2019, 1905.00319.

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

Bernhard Schölkopf, Dominik Janzing, Atalanti A. Mastakouri, 2019, NeurIPS.

A Measure-Theoretic Axiomatisation of Causality

B. Schölkopf, Krikamol Muandet, Simon Buchholz, 2023, ArXiv.

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Bernhard Schölkopf, Philipp Geiger, Michel Besserve, 2018, UAI.

Coordination via predictive assistants: time series algorithms and game-theoretic analysis

B. Schölkopf, M. Besserve, Philipp Geiger, 2018 .

Causal Inference on Time Series using Structural Equation Models pdf

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, 2012, ArXiv.

An Auditory Paradigm for Brain-Computer Interfaces

Bernhard Schölkopf, Niels Birbaumer, Thomas Navin Lal, 2004, NIPS.

Learning from Labeled and Unlabeled Data Using Random Walks

Bernhard Schölkopf, Dengyong Zhou, B. Schölkopf, 2004, DAGM-Symposium.

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Bernhard Schölkopf, Alice H. Oh, Alice Oh, 2017, WSDM.

Consistent Kernel Mean Estimation for Functions of Random Variables

Bernhard Schölkopf, Adam Scibior, Ilya O. Tolstikhin, 2016, NIPS.

From Statistical to Causal Learning

Julius von Kügelgen, B. Schölkopf, B. Scholkopf, 2022, ArXiv.

On Estimation of Functional Causal Models

Bernhard Schölkopf, Zhikun Wang, Kun Zhang, 2015, ACM Trans. Intell. Syst. Technol..

A Permutation-Based Kernel Conditional Independence Test

Bernhard Schölkopf, Krikamol Muandet, Gary Doran, 2014, UAI.

Learning Neural Causal Models with Active Interventions

Nan Rosemary Ke, B. Schölkopf, Anirudh Goyal, 2021, ArXiv.

Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra

Bernhard Schölkopf, Lionel Tarassenko, Paul M. Hayton, 2000, NIPS.

Exploring the Latent Space of Autoencoders with Interventional Assays

B. Schölkopf, B. Scholkopf, Felix Leeb, 2021, NeurIPS.

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

Francesco Locatello, Olivier Bachem, Mario Lucic, 2020, J. Mach. Learn. Res..

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

Julia E. Vogt, B. Schölkopf, P. Bühlmann, 2022, ICML.

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation pdf

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Kun Zhang, 2020, ArXiv.

3D Reconstruction of Neural Circuits from Serial EM Images

Bernhard Schölkopf, Alexander Borst, Winfried Denk, 2007 .

An Analysis of Inference with the Universum

Bernhard Schölkopf, Alekh Agarwal, Olivier Chapelle, 2007, NIPS.

Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina

B Schölkopf, B. Schölkopf, S. Thrun, 2004 .

Towards fully covariant machine learning

B. Schölkopf, D. Hogg, Weichi Yao, 2023, 2301.13724.

Kernel Mean Matching for Content Addressability of GANs

Bernhard Schölkopf, Wittawat Jitkrittum, Amit Raj, 2019, ICML.

A 32‐channel multi‐coil setup optimized for human brain shimming at 9.4T

Bernhard Schölkopf, Klaus Scheffler, Feng Jia, 2020, Magnetic resonance in medicine.

The passive symmetries of machine learning

B. Schölkopf, D. Hogg, Weichi Yao, 2023, ArXiv.

Leveraging sparse and shared feature activations for disentangled representation learning

B. Schölkopf, F. Wenzel, B. Scholkopf, 2023, ArXiv.

Score matching enables causal discovery of nonlinear additive noise models

B. Schölkopf, V. Cevher, Francesco Locatello, 2022, ICML.

Prototype Classification: Insights from Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Olivier Bousquet, 2009, Neural Computation.

On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Contour-propagation algorithms for semi-automated reconstruction of neural processes

Alexander Borst, Jakob H. Macke, Winfried Denk, 2008, Journal of Neuroscience Methods.

Likelihood Annealing: Fast Calibrated Uncertainty for Regression

Jae Myung Kim, B. Schölkopf, Zeynep Akata, 2023, 2302.11012.

Posterior Annealing: Fast Calibrated Uncertainty for Regression

Jae Myung Kim, B. Schölkopf, Zeynep Akata, 2023, ArXiv.

Diffeomorphic Dimensionality Reduction

Bernhard Schölkopf, Christian Walder, B. Schölkopf, 2008, NIPS.

Blind Correction of Optical Aberrations

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2012, ECCV.

Non-stationary correction of optical aberrations

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2011, 2011 International Conference on Computer Vision.

Experimentally optimal v in support vector regression for different noise models and parameter settings

Bernhard Schölkopf, Alexander J. Smola, Athanassia Chalimourda, 2004, Neural Networks.

Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

Bernhard Schölkopf, Moritz Grosse-Wentrup, Michael Erb, 2016, Journal of neural engineering.

B. Schölkopf, D. Hogg, T. Pritchard, 2021, The Astronomical Journal.

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes.

Clark Glymour, Jiji Zhang, Bernhard Scholkopf, 2019 .

Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis

Bernhard Schölkopf, Vinay Jayaram, Matthias Hohmann, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Discrete Key-Value Bottleneck

B. Schölkopf, Anirudh Goyal, Y. Bengio, 2022, ICML.

Amortized Inference for Causal Structure Learning

B. Schölkopf, Jonas Rothfuss, B. Scholkopf, 2022, NeurIPS.

Transfer Learning in Brain-Computer Interfaces

Bernhard Schölkopf, Vinay Jayaram, Moritz Grosse-Wentrup, 2015, IEEE Computational Intelligence Magazine.

Flow Matching for Scalable Simulation-Based Inference

Stephen R. Green, B. Schölkopf, J. Macke, 2023, ArXiv.

Diffusion Based Representation Learning

B. Schölkopf, B. Scholkopf, Stefan Bauer, 2021, ICML.

Instability , Computational Efficiency and Statistical Accuracy Instability , Computational Efficiency and Statistical Accuracy

B. Schölkopf, Nhat Ho, 2022 .

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

Stephen R. Green, B. Schölkopf, J. Macke, 2022, Physical Review D.

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Stephen R. Green, B. Schölkopf, J. Macke, 2022, Physical review letters.

On integral probability metrics, φ-divergences and binary classification

Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, 2009, 0901.2698.

Justifying Information-Geometric Causal Inference

B. Schölkopf, B. Scholkopf, Bastian Steudel, 2014, 1402.2499.

Neural Attentive Circuits

Erran L. Li, B. Schölkopf, Nicolas Ballas, 2022, NeurIPS.

Semi-supervised interpolation in an anticausal learning scenario

Bernhard Schölkopf, Dominik Janzing, B. Schölkopf, 2015, J. Mach. Learn. Res..

Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance

Jason Farquhar, Bernhard Schölkopf, Felix Bießmann, 2008, NIPS.

Influence Estimation and Maximization in Continuous-Time Diffusion Networks

Le Song, Bernhard Schölkopf, Hongyuan Zha, 2016, ACM Trans. Inf. Syst..

Support Vector Machines as Probabilistic Models

Bernhard Schölkopf, Alexander Zien, Vojtech Franc, 2011, ICML.

Automatic 3D face reconstruction from single images or video

Bernhard Schölkopf, Kwang In Kim, Wolf Kienzle, 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

Machine Learning Methods for Automatic Image Colorization

Yasemin Altun, Guillaume Charpiat, Bernhard Schölkopf, 2011 .

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

B. Schölkopf, Wittawat Jitkrittum, Krikamol Muandet, 2019, ArXiv.

Kernel-Guided Training of Implicit Generative Models with Stability Guarantees pdf

Krikamol Muandet, Wittawat Jitkrittum, Arash Mehrjou, 2019, ArXiv.

Dynamic Inference with Neural Interpreters pdf

Yoshua Bengio, Bernhard Scholkopf, Muhammad Waleed Gondal, 2021, NeurIPS.

Implicit Wiener series analysis of epileptic seizure recordings

Moritz Grosse-Wentrup, Bernhard Scholkopf, Jose R. Dorronsoro, 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Detection of diabetes from whole-body MRI using deep learning

B. Schölkopf, H. Preissl, J. Machann, 2021, JCI Insight.

A pixel-level model for event discovery in time-domain imaging

Daniel Foreman-Mackey, Bernhard Scholkopf, David W. Hogg, 2017, 1710.02428.

Kernel methods in medical imaging

Bernhard Schölkopf, Guillaume Charpiat, Matthias Hofmann, 2015 .

Learning soft interventions in complex equilibrium systems

B. Schölkopf, M. Besserve, B. Scholkopf, 2021, UAI.

Perceptual Video Super Resolution with Enhanced Temporal Consistency

Bernhard Schölkopf, Michael Hirsch, Eduardo Perez-Pellitero, 2018 .

Diabetes detection from whole-body magnetic resonance imaging using deep learning.

B. Schölkopf, F. Schick, H. Preissl, 2021, JCI insight.

Out-of-Variable Generalization for Discriminative Models

B. Schölkopf, Jonas Wildberger, Siyuan Guo, 2023, 2304.07896.

On the Transfer of Disentangled Representations in Realistic Settings

B. Schölkopf, Francesco Locatello, B. Scholkopf, 2020, ICLR.

Structure by Architecture: Disentangled Representations without Regularization

B. Schölkopf, B. Scholkopf, Felix Leeb, 2020, 2006.07796.

Jacobian-based Causal Discovery with Nonlinear ICA

M. Bethge, B. Schölkopf, Wieland Brendel, 2023, Trans. Mach. Learn. Res..

Optimizing Human Learning pdf

Bernhard Schölkopf, Utkarsh Upadhyay, Abir De, 2017, ArXiv.

A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment pdf

Bernhard Schölkopf, Manuel Gomez-Rodriguez, Lars Lorch, 2020, ArXiv.

Case series: Slowing alpha rhythm in late-stage ALS patients

Bernhard Schölkopf, Vinay Jayaram, Moritz Grosse-Wentrup, 2018, Clinical Neurophysiology.

Testing Goodness of Fit of Conditional Density Models with Kernels pdf

Bernhard Schölkopf, Wittawat Jitkrittum, Heishiro Kanagawa, 2020, UAI.

Task-induced frequency modulation features for brain-computer interfacing

Vinay Jayaram, Matthias Hohmann, Jennifer Just, 2017, Journal of neural engineering.

Fragmentation of slow wave sleep after onset of complete locked-in state.

Bernhard Schölkopf, Michael Bensch, Niels Birbaumer, 2013, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

A General Purpose Neural Architecture for Geospatial Systems

B. Schölkopf, B. Scholkopf, Y. Bengio, 2022, ArXiv.

How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye Movements

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2007, DAGM-Symposium.

On the design of consequential ranking algorithms

Bernhard Schölkopf, Vicenç Gómez, Behzad Tabibian, 2020, UAI.

Function Contrastive Learning of Transferable Meta-Representations

Bernhard Schölkopf, Manuel Wüthrich, Muhammad Waleed Gondal, 2020, ICML.

Function Contrastive Learning of Transferable Representations pdf

Bernhard Schölkopf, Manuel Wüthrich, Muhammad Waleed Gondal, 2020, ArXiv.

Bayesian Quadrature on Riemannian Data Manifolds

B. Schölkopf, Philipp Hennig, A. Gessner, 2021, ICML.

Membership Inference Attacks against Language Models via Neighbourhood Comparison

B. Schölkopf, Taylor Berg-Kirkpatrick, Mrinmaya Sachan, 2023, ACL.

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Mark van der Wilk, Tycho F. A. van der Ouderaa, B. Schölkopf, 2023, ICML.

Disentangling Factors of Variation Using Few Labels

B. Schölkopf, M. Tschannen, G. Rätsch, 2019, ArXiv.

Can Large Language Models Infer Causation from Correlation?

B. Schölkopf, Spencer Poff, Rada Mihalcea, 2023, ArXiv.

Analysis of cause-effect inference by comparing regression errors

Bernhard Schölkopf, Takashi Washio, Dominik Janzing, 2018, PeerJ Comput. Sci..

Quantum Mean Embedding of Probability Distributions pdf

Bernhard Schölkopf, Krikamol Muandet, Jonas M. Kübler, 2019, Physical Review Research.

The Hessian perspective into the Nature of Convolutional Neural Networks

Sidak Pal Singh, B. Schölkopf, T. Hofmann, 2023, ICML.

Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2010, 2010 IEEE International Conference on Image Processing.

Local learning projections

Bernhard Schölkopf, Shipeng Yu, Kai Yu, 2007, ICML '07.

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

Bernhard Scholkopf, Manuel Gomez-Rodriguez, Lars Lorch, 2020, ACM Trans. Spatial Algorithms Syst..

Semi-Supervised Learning by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien (Review)

B. Schölkopf, O. Chapelle, A. Zien, Eds., 2009, IEEE Trans. Neural Networks.

Uncovering the structure and temporal dynamics of information propagation

Bernhard Schölkopf, Jure Leskovec, David Balduzzi, 2014, Network Science.

Real-time prediction of COVID-19 related mortality using electronic health records

Leo Anthony Celi, Arash Mehrjou, Bernhard Scholkopf, 2020, Nature Communications.

Action-Sufficient State Representation Learning for Control with Structural Constraints

José Miguel Hernández-Lobato, C. Glymour, B. Schölkopf, 2021, ICML.

Kernel‐based tests for joint independence

B. Schölkopf, J. Peters, Peter Buhlmann, 2016, 1603.00285.

Photorealistic Video Super Resolution pdf

Bernhard Schölkopf, Michael Hirsch, Eduardo Pérez-Pellitero, 2018, ArXiv.

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Julius von Kügelgen, B. Schölkopf, B. Scholkopf, 2022, NeurIPS.

Phenomenology of Double Descent in Finite-Width Neural Networks

Sidak Pal Singh, B. Schölkopf, T. Hofmann, 2022, ICLR.

Learning strategies in table tennis using inverse reinforcement learning

Jan Peters, B. Schölkopf, Katharina Muelling, 2014, Biological Cybernetics.

Direct Advantage Estimation

B. Schölkopf, Alexander Neitz, Hsiao-Ru Pan, 2021, NeurIPS.

Causal discovery with continuous additive noise models

B. Schölkopf, J. Mooij, J. Peters, 2013, J. Mach. Learn. Res..

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

Gunnar Rätsch, Bernhard Schölkopf, Christian Widmer, 2008, NIPS.

Characteristic Kernels on Groups and Semigroups

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2008, NIPS.

Probabilistic latent variable models for distinguishing between cause and effect

Bernhard Schölkopf, Joris M. Mooij, Oliver Stegle, 2010, NIPS.

Influence Maximization in Continuous Time Diffusion Networks pdf

Bernhard Schölkopf, Manuel Gomez-Rodriguez, B. Schölkopf, 2012, ICML.

Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort

B. Schölkopf, H. Kauczor, M. Schulze, 2023 .

AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation

B. Schölkopf, Dieter Büchler, Simon Guist, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Real Time Trajectory Prediction Using Deep Conditional Generative Models

J. Peters, B. Schölkopf, S. Gómez-González, 2019, IEEE Robotics and Automation Letters.

Fidelity-Weighted Learning pdf

Bernhard Schölkopf, Jaap Kamps, Arash Mehrjou, 2017, ICLR.

Multi armed bandits and quantum channel oracles

Jonas M. Kubler, B. Schölkopf, Simon Buchholz, 2023, 2301.08544.

Regret Bounds for Gaussian-Process Optimization in Large Domains

B. Schölkopf, Andreas Krause, M. Wuthrich, 2021, NeurIPS.

Provably Learning Object-Centric Representations

Roland S. Zimmermann, Julius von Kügelgen, Yash Sharma, 2023, ICML.

Towards Principled Disentanglement for Domain Generalization

E. Xing, B. Schölkopf, Adrian Weller, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Deconfounding Reinforcement Learning in Observational Settings pdf

Bernhard Schölkopf, José Miguel Hernández-Lobato, Chaochao Lu, 2018, ArXiv.

A kernel-based causal learning algorithm

Bernhard Schölkopf, Kenji Fukumizu, Dominik Janzing, 2007, ICML '07.

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

B. Schölkopf, B. Scholkopf, Uri Shalit, 2021, ICML.

S TRUCTURE BY A RCHITECTURE : S TRUCTURED R EP - RESENTATIONS WITHOUT R EGULARIZATION

B. Schölkopf, Felix Leeb, M. Besserve, 2023 .

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Vignesh Ram Somnath, B. Schölkopf, Jonas Rothfuss, 2022, AISTATS.

Automatic estimation of modulation transfer functions

Bernhard Schölkopf, Michael Hirsch, Matthias Bauer, 2018, 2018 IEEE International Conference on Computational Photography (ICCP).

Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2011 .

Improving alpha matting and motion blurred foreground estimation

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2013, 2013 IEEE International Conference on Image Processing.

normflows: A PyTorch Package for Normalizing Flows

José Miguel Hernández-Lobato, B. Schölkopf, Vincent Berenz, 2023, J. Open Source Softw..

Resampling Base Distributions of Normalizing Flows

José Miguel Hernández-Lobato, B. Schölkopf, Vincent Stimper, 2021, AISTATS.

Causal Component Analysis

Julius von Kügelgen, B. Schölkopf, M. Besserve, 2023, ArXiv.

Spatially Structured Recurrent Modules

Bernhard Schölkopf, Anirudh Goyal, Muhammad Waleed Gondal, 2021, ICLR.

A Kernel for Protein Secondary Structure Prediction

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2004 .

Fast protein classification with multiple networks

Bernhard Schölkopf, Hyunjung Shin, Koji Tsuda, 2005, ECCB/JBI.

Remote Sensing Feature Selection by Kernel Dependence Measures

Bernhard Schölkopf, Joris M. Mooij, Gustavo Camps-Valls, 2010, IEEE Geoscience and Remote Sensing Letters.

Learning with Hyperspherical Uniformity

Adrian Weller, Weiyang Liu, Bernhard Scholkopf, 2021, AISTATS.

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

B. Schölkopf, Hugo Yeche, Guy Tennenholtz, 2023, ArXiv.

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

Philippe Wenk, Stefan Bauer, Andreas Krause, 2019, AAAI.

Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems pdf

Bernhard Schölkopf, Arash Mehrjou, Mohammad Ghavamzadeh, 2020, ArXiv.

Control of Musculoskeletal Systems Using Learned Dynamics Models

Bernhard Schölkopf, Jan Peters, Roberto Calandra, 2018, IEEE Robotics and Automation Letters.

A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs)

Bernhard Schölkopf, Moritz Grosse-Wentrup, B. Schölkopf, 2013 .

A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller

Jason Farquhar, Bernhard Schölkopf, Joris M. Mooij, 2011, Neural Computation.

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

Martin A. Riedmiller, Barnabas Gavin Cangan, B. Schölkopf, 2023, ArXiv.

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

B. Schölkopf, Sebastian Blaes, G. Martius, 2023, ICLR.

Identifiability of Causal Graphs using Functional Models

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2011, UAI.

DiBS: Differentiable Bayesian Structure Learning pdf

Bernhard Scholkopf, Andreas Krause, Lars Lorch, 2021, NeurIPS.

Towards a Learning Theory of Cause-Effect Inference

Bernhard Schölkopf, David Lopez-Paz, Krikamol Muandet, 2015, ICML.

Non-parametric estimation of integral probability metrics

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2010, 2010 IEEE International Symposium on Information Theory.

Inference of Cause and Effect with Unsupervised Inverse Regression

Bernhard Schölkopf, Eleni Sgouritsa, Dominik Janzing, 2015, AISTATS.

Causal Inference Through the Structural Causal Marginal Problem

Jonas M. Kubler, Julius von Kügelgen, B. Schölkopf, 2022, ICML.

Probable Domain Generalization via Quantile Risk Minimization

George J. Pappas, Julius von Kügelgen, B. Schölkopf, 2022, NeurIPS.

FINITE-WIDTH NEURAL NETWORKS

Sidak Pal Singh, B. Schölkopf, T. Hofmann, 2022 .

Multi-Armed Bandits and Quantum Channel Oracles

Jonas M. Kubler, B. Schölkopf, Simon Buchholz, 2023, 2301.08544.

Feature selection and transduction for prediction of molecular bioactivity for drug design

Bernhard Schölkopf, Jason Weston, Fernando Pérez-Cruz, 2003, Bioinform..

Estimation Beyond Data Reweighting: Kernel Method of Moments

B. Schölkopf, Yassine Nemmour, Heiner Kremer, 2023, ICML.

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

B. Schölkopf, B. Scholkopf, Michael Muehlebach, 2022, NeurIPS.

Causal models for debugging and control in cloud computing pdf

Bernhard Schölkopf, Lucian Carata, Philipp Geiger, 2016, ArXiv.

Causal inference for cloud computing

Lucian Carata, Philipp Geiger, Bernhard Schoelkopf, 2016 .

Frequency peak features for low-channel classification in motor imagery paradigms

Bernhard Schölkopf, Vinay Jayaram, Moritz Grosse-Wentrup, 2017, 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER).

Sparse Representation for Signal Classification

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

Julius von Kügelgen, B. Schölkopf, Diane Bouchacourt, 2023, ArXiv.

A brain computer interface with online feedback based on magnetoencephalography

Thomas Hofmann, Bernhard Schölkopf, Wolfgang Rosenstiel, 2005, ICML.