B. Scholkopf

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).

Algorithmic Recourse: from Counterfactual Explanations to Interventions

Bernhard Schölkopf, Amir-Hossein Karimi, Isabel Valera, 2020, FAccT.

Membership Inference Attacks against Language Models via Neighbourhood Comparison

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

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2017, IJCAI.

The Arrow of Time in Multivariate Time Series

Stefan Bauer, Bernhard Schölkopf, Jonas Peters, 2016, ICML.

DiBS: Differentiable Bayesian Structure Learning pdf

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

Amortized Inference for Causal Structure Learning

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

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

P. Tigas, Y. Gal, B. Scholkopf, 2022, NeurIPS.

Learning Neural Causal Models with Active Interventions

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

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks pdf

Bernhard Schölkopf, Adrian Weller, Paul K. Rubenstein, 2019, ArXiv.

Cluster Kernels for Semi-Supervised Learning

Bernhard Schölkopf, Jason Weston, Olivier Chapelle, 2002, NIPS.

From Variational to Deterministic Autoencoders pdf

Bernhard Schölkopf, Michael J. Black, Partha Ghosh, 2019, ICLR.

Towards Total Recall in Industrial Anomaly Detection

B. Schölkopf, T. Brox, P. Gehler, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

MR-Based PET attenuation correction for PET/MR imaging.

B. Schölkopf, H. Schmidt, B. Pichler, 2013, Seminars in nuclear medicine.

On integral probability metrics, φ-divergences and binary classification

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

Learning Theory and Kernel Machines

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2003, Lecture Notes in Computer Science.

Multidimensional Contrast Limited Adaptive Histogram Equalization

Stefan Bauer, Bernhard Schölkopf, Vincent Stimper, 2019, IEEE Access.

Efficient Learning of Sparse Representations with an Energy-Based Model

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Spatio-Temporal Transformer Network for Video Restoration

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

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Bernhard Schölkopf, Stefano Panzeri, Nikos K. Logothetis, 2015, PLoS biology.

Support vector learning

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 1997 .

Generalization and similarity in exemplar models of categorization: Insights from machine learning

B. Schölkopf, F. Jäkel, Felix Wichmann, 2008, Psychonomic bulletin & review.

A Kernel Statistical Test of Independence

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2007, NIPS.

Causality : Objectives and Assessment

Isabelle Guyon, Bernhard Schölkopf, Dominik Janzing, 2010 .

An Introduction to Support Vector Machines

Bernhard Schölkopf, Michael G. Akritas, Dimitris N. Politis, 2003 .

Probabilities for SV Machines

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

B. Schölkopf, Rohit Babbar, H. Preissl, 2018, Front. Endocrinol..

Kernel Mean Shrinkage Estimators pdf

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2014, J. Mach. Learn. Res..

Abstract: BCI-based robot rehabilitation framework for stroke patients

J. Peters, A. Gharabaghi, J. Hill, 2010 .

Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery

Bernhard Schölkopf, Moritz Grosse-Wentrup, Jan Peters, 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

Methods Towards Invasive Human Brain Computer Interfaces

Bernhard Schölkopf, Wolfgang Rosenstiel, Thilo Hinterberger, 2004, NIPS.

Geometrically Enriched Latent Spaces pdf

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

Training Invariant Support Vector Machines

Bernhard Schölkopf, Dennis DeCoste, B. Schölkopf, 2002, Machine Learning.

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

A machine learning route between band mapping and band structure

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

Predicting motor learning performance from Electroencephalographic data

Jan Peters, B. Schölkopf, T. Zander, 2014, Journal of NeuroEngineering and Rehabilitation.

Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals

Bernhard Schölkopf, Thilo Hinterberger, Niels Birbaumer, 2006, DAGM-Symposium.

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

Bernhard Schölkopf, Amir-Hossein Karimi, Isabel Valera, 2020, ArXiv.

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Johann S. Hawe, B. Schölkopf, J. Macke, 2014, Nature Biotechnology.

Kernel Methods for Measuring Independence

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005, J. Mach. Learn. Res..

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

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

Advances in neural information processing systems : proceedings of the ... conference

Thomas G. Dietterich, Richard P. Lippmann, Klaus-Robert Müller, 1989 .

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

B. Schölkopf, Dieter Büchler, Michael Muehlebach, 2022, Robotics: Science and Systems XVIII.

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2009, NIPS.

On the Fairness of Disentangled Representations

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

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

B. Schölkopf, Stefan Bauer, A. Mehrjou, 2021, PLoS Comput. Biol..

AdaGAN: Boosting Generative Models

Bernhard Schölkopf, Sylvain Gelly, Olivier Bousquet, 2017, NIPS.

Handling Advertisements of Unknown Quality in Search Advertising

B. Schölkopf, J. Platt, T. Hofmann, 2007 .

Leveraging sparse and shared feature activations for disentangled representation learning

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

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies[OPEN]

Bernhard Schölkopf, Karsten M. Borgwardt, Christoph Lippert, 2016, Plant Cell.

Causality for Machine Learning

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

A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

B. Schölkopf, I. Valera, A. Karimi, 2022, ACM Comput. Surv..

Logical Fallacy Detection

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

Statistical Learning Theory: Models, Concepts, and Results

Bernhard Schölkopf, Ulrike von Luxburg, B. Schölkopf, 2008, Inductive Logic.

Robustness Implies Fairness in Causal Algorithmic Recourse

Amir-Hossein Karimi, S. Maghsudi, A. Ehyaei, 2023, FAccT.

On the Adversarial Robustness of Causal Algorithmic Recourse

B. Scholkopf, Amir-Hossein Karimi, Ricardo Dominguez-Olmedo, 2021, ICML.

Avoiding Discrimination through Causal Reasoning

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

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

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

Deconfounding Reinforcement Learning in Observational Settings pdf

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

Estimation Beyond Data Reweighting: Kernel Method of Moments

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

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

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

Causal Feature Selection via Orthogonal Search

B. Schölkopf, Anant Raj, M. Besserve, 2020, Trans. Mach. Learn. Res..

Causality for Machine Learning pdf

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2019, ArXiv.

39 Causality for Machine Learning

B. Schölkopf, B. Scholkopf, 2021 .

Improving the accuracy and speed of support vector learning machines

Bernhard Schölkopf, Cjc Burges, Mozer, 1997, NIPS 1997.

Improving the Accuracy and Speed of Support Vector Machines

Bernhard Schölkopf, Christopher J. C. Burges, B. Schölkopf, 1996, NIPS.

Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models

Bernhard Schölkopf, Heinrich H. Bülthoff, Thomas Vetter, 1996, ICANN.

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

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

Kernel PCA and De-Noising in Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998, NIPS.

Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

Julius von Kügelgen, Andrei Liviu Nicolicioiu, Shashank Singh, 2023, NeurIPS.

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2006, PLoS Comput. Biol..

Large scale genomic sequence SVM classifiers

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2005, ICML.

Nanotechnology in the Environmental Health Sciences

B. Schölkopf, K. Müller, S. Sonnenburg, 2005, Environmental Health Perspectives.

Sparse Kernel Feature Analysis

Bernhard Schölkopf, Alexander J. Smola, Olvi L. Mangasarian, 2002 .

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing

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

UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models

B. Schölkopf, J. Mooij, J. Peters, 2014 .

Probable Domain Generalization via Quantile Risk Minimization

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

SV Estimation of a Distribution's Support

Bernhard Schölkopf, John Shawe-Taylor, Robert C. Williamson, 1999, NIPS 1999.

Support Vector Machines as Probabilistic Models

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

Support Vector Machines and Kernels for Computational Biology

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2008, PLoS Comput. Biol..

Nonparametric Identifiability of Causal Representations from Unknown Interventions

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

Causal Component Analysis

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

Evaluating Predictive Uncertainty Challenge

Carl E. Rasmussen, Joaquin Quiñonero Candela, Bernhard Schölkopf, 2005, MLCW.

Group equivariant neural posterior estimation

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

Real-time gravitational-wave science with neural posterior estimation

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

Evaluating vaccine allocation strategies using simulation-assisted causal modelling

Julius von Kügelgen, B. Schölkopf, V. Priesemann, 2022, ArXiv.

Theoretical Aspects of Cyclic Structural Causal Models

Bernhard Schölkopf, Joris M. Mooij, Stephan Bongers, 2016 .

Estimating Causal Effects by Bounding Confounding

Bernhard Schölkopf, Dominik Janzing, Philipp Geiger, 2014, UAI.

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

J. Mooij, D. Janzing, B. Scholkopf, 2013, UAI.

Causal Inference Using the Algorithmic Markov Condition

Bernhard Schölkopf, Dominik Janzing, B. Schölkopf, 2008, IEEE Transactions on Information Theory.

Anticipatory action selection for human-robot table tennis

Bernhard Schölkopf, Jan Peters, Katharina Mülling, 2017, Artif. Intell..

Learning soft interventions in complex equilibrium systems

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

Structural causal models for macro-variables in time-series

Dominik Janzing, Bernhard Scholkopf, Paul K. Rubenstein, 2018, 1804.03911.

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

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

From Deterministic ODEs to Dynamic Structural Causal Models

Bernhard Schölkopf, Joris M. Mooij, Stephan Bongers, 2016, UAI.

A ug 2 01 8 Theoretical Aspects of Cyclic Structural Causal Models

B. Schölkopf, J. Mooij, J. Peters, 2018 .

On Causal Discovery with Cyclic Additive Noise Models

Bernhard Schölkopf, Joris M. Mooij, Tom Heskes, 2011, NIPS.

GeNet: Deep Representations for Metagenomics

Bernhard Schölkopf, Mateo Rojas-Carulla, Ilya O. Tolstikhin, 2019, bioRxiv.

Flow Matching for Scalable Simulation-Based Inference

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

Regularization on Discrete Spaces

Bernhard Schölkopf, Dengyong Zhou, B. Schölkopf, 2005, DAGM-Symposium.

Methodolgy for Embedding Cancer Germline Variants

B. Schölkopf, G. Schweikert, G. Visonà, 2021 .

Fast protein classification with multiple networks

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

A Witness Two-Sample Test

Jonas M. Kubler, Wittawat Jitkrittum, Krikamol Muandet, 2021, AISTATS.

Learning Kernel Tests Without Data Splitting

Jonas M. Kubler, Wittawat Jitkrittum, B. Scholkopf, 2020, NeurIPS.

Testing Goodness of Fit of Conditional Density Models with Kernels pdf

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

Kernel Stein Tests for Multiple Model Comparison

Bernhard Schölkopf, Makoto Yamada, Wittawat Jitkrittum, 2019, NeurIPS.

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

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

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Bernhard Schölkopf, Carl-Johann Simon-Gabriel, B. Schölkopf, 2016, J. Mach. Learn. Res..

Causal Inference on Time Series using Structural Equation Models pdf

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

Adversarial Robustness through the Lens of Causality pdf

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

Invariant Causal Representation Learning for Out-of-Distribution Generalization

Yuhuai Wu, B. Schölkopf, Chaochao Lu, 2022, ICLR.

Edinburgh Research Explorer Interpolating between types and tokens by estimating power-law generators

B. Schölkopf, Y. Weiss, J. Platt, 2006 .

AutoML Two-Sample Test

Jonas M. Kubler, Krikamol Muandet, Vincent Stimper, 2022, NeurIPS.

Kernel‐based tests for joint independence

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

Remote Sensing Feature Selection by Kernel Dependence Measures

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

Towards Causal Algorithmic Recourse

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

Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects

T. N. Lal, C. Elger, N. Birbaumer, 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

On the Fairness of Causal Algorithmic Recourse

Julius von Kügelgen, Adrian Weller, I. Valera, 2020, AAAI.

The connection between regularization operators and support vector kernels

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1998, Neural Networks.

Prior Knowledge in Support Vector Kernels

Bernhard Schölkopf, Alexander J. Smola, Patrice Y. Simard, 1997, NIPS.

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

B. Schölkopf, Niki Kilbertus, I. Harry, 2019, Physical Review D.

The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

G. Martius, A. Levina, Nasim Rahaman, 2023, ArXiv.

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

Julius von Kügelgen, Andrei Liviu Nicolicioiu, B. Scholkopf, 2022, ICLR.

Learning with kernels

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2001 .

Predicting Structured Data

Gökhan BakIr, Sundar Vishwanathan, Bernhard Schölkopf, 2008 .

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Bernhard Schölkopf, Peter Bühlmann, Sebastian Weichwald, 2018, J. Mach. Learn. Res..

groupICA: Independent component analysis for grouped data pdf

Bernhard Schölkopf, Peter Bühlmann, Sebastian Weichwald, 2018, ArXiv.

Efficient sparse coding algorithms

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2010, Magnetic resonance in medicine.

Kernel Dependency Estimation

Bernhard Schölkopf, Jason Weston, André Elisseeff, 2002, NIPS.

Semi-Supervised Learning (Adaptive Computation and Machine Learning)

Bernhard Schölkopf, Alexander Zien, Olivier Chapelle, 2006 .

Fast removal of non-uniform camera shake

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

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

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

High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression

B. Schölkopf, J. Platt, T. Hofmann, 2007 .

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

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

Probable Domain Generalization via Quantile Risk Minimization

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

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning

Nicholas D. Youngblut, B. Schölkopf, G. Rätsch, 2022, bioRxiv.

The Role of Pretrained Representations for the OOD Generalization of RL Agents

O. Winther, F. Widmaier, Stefan Bauer, 2021, ICLR.

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

Ole Winther, Peter V. Gehler, Bernhard Schölkopf, 2021, ArXiv.

Dealing with large diagonals in kernel matrices

Jason Weston, William Stafford Noble, Bernhard Schölkopf, 2003 .

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 .

Discriminative Transfer Learning for General Image Restoration

Bernhard Schölkopf, Wolfgang Heidrich, Felix Heide, 2017, IEEE Transactions on Image Processing.

Advanced Application of Support Vector Machines

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

Learning to Find Pre-Images

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

Neural Attentive Circuits

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

Generalized Score Functions for Causal Discovery

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, KDD.

Phenomenology of Double Descent in Finite-Width Neural Networks

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

Automatic Image Colorization Via Multimodal Predictions

Bernhard Schölkopf, Guillaume Charpiat, Matthias Hofmann, 2008, ECCV.

Kernel Mean Estimation and Stein Effect

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2013, ICML.

Using support vector machines for time series prediction

Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, 1999 .

Bloom Origami Assays: Practical Group Testing pdf

Alexander J. Smola, Bernhard Schölkopf, Louis Abraham, 2020, ArXiv.

Sparse Greedy Matrix Approximation for Machine Learning

B. Schölkopf, Alex Smola, B. Scholkopf, 2000, ICML.

Discrete Key-Value Bottleneck

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

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana

Gunnar Rätsch, Bernhard Schölkopf, Timo Sachsenberg, 2008, Genome Biology.

Learning Dense 3D Correspondence

Bernhard Schölkopf, Florian Steinke, Volker Blanz, 2006, NIPS.

A Regularization Framework for Learning from Graph Data

B. Schölkopf, Dengyong Zhou, B. Scholkopf, 2004, ICML 2004.

Membership Inference Attacks against Language Models via Neighbourhood Comparison

Taylor Berg-Kirkpatrick, Mrinmaya Sachan, Zhijing Jin, 2023, ACL.

On the Interventional Kullback-Leibler Divergence

Arnab Bhattacharyya, Siyuan Guo, B. Scholkopf, 2023, CLeaR.

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

Christopher K. I. Williams, B. Schölkopf, Cian Eastwood, 2021, ICLR.

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).

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

B. Schölkopf, K. Borgwardt, A. Altmann, 2012, Human Heredity.

Fast and Slow Learning of Recurrent Independent Mechanisms pdf

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

DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification

Bernhard Schölkopf, Rohit Babbar, B. Schölkopf, 2016, WSDM.

Learning Inverse Dynamics: a Comparison

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

Uncovering the structure and temporal dynamics of information propagation

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

Uncovering the Temporal Dynamics of Diffusion Networks pdf

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

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez, 2014, ICML.

Modeling Information Propagation with Survival Theory pdf

Bernhard Schölkopf, Jure Leskovec, Manuel Gomez-Rodriguez, 2013, ICML.

Data scarcity, robustness and extreme multi-label classification

B. Schölkopf, Rohit Babbar, B. Scholkopf, 2019, Machine Learning.

The representer theorem for Hilbert spaces: a necessary and sufficient condition

Bernhard Schölkopf, Francesco Dinuzzo, B. Schölkopf, 2012, NIPS.

U NSUPERVISED O BJECT L EARNING VIA C OMMON F ATE pdf

Thomas Brox, Matthias Bethge, Bernhard Scholkopf, 2021 .

Causal Discovery from Heterogeneous/Nonstationary Data pdf

Bernhard Schölkopf, Kun Zhang, Clark Glymour, 2019, J. Mach. Learn. Res..

A kernel-based causal learning algorithm

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

Unifying distillation and privileged information pdf

Bernhard Schölkopf, David Lopez-Paz, Vladimir Vapnik, 2015, ICLR.

Empirical Inference

Vladimir Vovk, Zhiyuan Luo, Vladimir N. Vapnik, 2011, Springer Berlin Heidelberg.

Generalization in anti-causal learning pdf

Bernhard Schölkopf, Giambattista Parascandolo, Niki Kilbertus, 2018, ArXiv.

Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images pdf

Bernhard Schölkopf, Xiaoxiang Zhu, Aysim Toker, 2021, IEEE Transactions on Geoscience and Remote Sensing.

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

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

Transfer Learning in Brain-Computer Interfaces

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

Feature Selection for Support Vector Machines Using Genetic Algorithms

Bernhard Schölkopf, Holger Fröhlich, Olivier Chapelle, 2004, Int. J. Artif. Intell. Tools.

Probabilistic Modeling of Human Movements for Intention Inference

Bernhard Schölkopf, Jan Peters, Zhikun Wang, 2012, Robotics: Science and Systems.

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

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

An Analysis of Inference with the Universum

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

Adaptation and Robust Learning of Probabilistic Movement Primitives

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

A Machine Learning Approach for Non-blind Image Deconvolution

Bernhard Schölkopf, Stefan Harmeling, Harold Christopher Burger, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

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

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

Causal interpretation rules for encoding and decoding models in neuroimaging

Tonio Ball, Moritz Grosse-Wentrup, Bernhard Schölkopf, 2015, NeuroImage.

Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies

B. Schölkopf, H. Kauczor, M. Schulze, 2022, Scientific Reports.

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data pdf

Stefan Bauer, Luigi Gresele, Anant Raj, 2019, ArXiv.

Real Time Trajectory Prediction Using Deep Conditional Generative Models

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

Classification of Faces in Man and Machine

Bernhard Schölkopf, Heinrich H. Bülthoff, Felix A. Wichmann, 2006, Neural Computation.

Efficient Algorithms for Max-Margin Structured Classification

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

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

Bernhard Schölkopf, Diego Agudelo-España, Yassine Nemmour, 2021, ArXiv.

Learning Blind Motion Deblurring

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

Online Video Deblurring via Dynamic Temporal Blending Network

Bernhard Schölkopf, Tae Hyun Kim, Michael Hirsch, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Variational Causal Dynamics: Discovering Modular World Models from Interventions

B. Schölkopf, I. Posner, Anson Lei, 2022, ArXiv.

I NVARIANT C AUSAL R EPRESENTATION L EARNING FOR G ENERALIZATION IN I MITATION AND R EINFORCEMENT L EARNING

B. Schölkopf, Chaochao Lu, José Miguel Hernández-Lobato, 2022 .

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data pdf

Francesco Locatello, Bernhard Scholkopf, Niki Kilbertus, 2020, ArXiv.

On Disentangled Representations Learned from Correlated Data

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

MR-Based Attenuation Correction Methods for Improved PET Quantification in Lesions Within Bone and Susceptibility Artifact Regions

Bernhard Schölkopf, Bernd J. Pichler, Ilja Bezrukov, 2013, The Journal of Nuclear Medicine.

Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap

B. Scholkopf, Adrian Weller, Weiyang Liu, 2023, ICLR.

HYPERSPHERICAL UNIFORMITY GAP

Adrian Weller, Weiyang Liu, L. Yu, 2023 .

Evaluating vaccine allocation strategies using simulation-assisted causal modeling

Julius von Kügelgen, V. Priesemann, Luigi Gresele, 2023, Patterns.

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

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

THE POPULATION OF LONG-PERIOD TRANSITING EXOPLANETS

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

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

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

ArmSym: A Virtual Human–Robot Interaction Laboratory for Assistive Robotics

Jan Peters, M. Grosse-Wentrup, V. Jayaram, 2021, IEEE Transactions on Human-Machine Systems.

Causal Inference on Discrete Data Using Additive Noise Models

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bernhard Schölkopf, Florian Steinke, Daewon Lee, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Detection of diabetes from whole-body MRI using deep learning

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

View-Based Cognitive Mapping and Path Planning

Bernhard Schölkopf, Hanspeter A. Mallot, B. Schölkopf, 1995, Adapt. Behav..

Multi-Sided Matching Markets with Consistent Preferences and Cooperative Partners pdf

Bernhard Schölkopf, Nicolò Cesa-Bianchi, Riccardo Della Vecchia, 2021, ArXiv.

Machine Learning Methods for Automatic Image Colorization

Yasemin Altun, Guillaume Charpiat, Bernhard Schölkopf, 2011 .

Telling cause from effect in deterministic linear dynamical systems pdf

Bernhard Schölkopf, Dominik Janzing, Michel Besserve, 2015, ICML.

Fast Discriminative Visual Codebooks using Randomized Clustering Forests

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Backward-Compatible Prediction Updates: A Probabilistic Approach pdf

Peter V. Gehler, Bernhard Schölkopf, Matthäus Kleindessner, 2021, NeurIPS.

Function Contrastive Learning of Transferable Representations

Muhammad Waleed Gondal, Manuel Wüthrich, Nasim Rahaman, 2020, ArXiv.

Support Vector Machine Applications in Computational Biology

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

LanczosNet : Multi-Scale Deep Graph Convolutional Networks

Jessica B. Hamrick, B. Schölkopf, P. Battaglia, 2018 .

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

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

Implicit Wiener Series for Higher-Order Image Analysis

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

Movement templates for learning of hitting and batting

Christoph H. Lampert, Bernhard Schölkopf, Oliver Kroemer, 2010, 2010 IEEE International Conference on Robotics and Automation.

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

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

Direct Advantage Estimation

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

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

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

Reinforcement Learning for Motor Primitives

B. Schölkopf, B. Scholkopf, 2009 .

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).

Modeling confounding by half-sibling regression

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

Out-of-Variable Generalization for Discriminative Models

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

Out-of-Variable Generalization

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

I NVARIANT C AUSAL R EPRESENTATION L EARNING FOR O UT - OF -D ISTRIBUTION G ENERALIZATION

Yuhuai Wu, B. Schölkopf, Chaochao Lu, 2022 .

Learning causality and causality-related learning: some recent progress.

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, National science review.

Face Detection Benchmark Database Data Set Location Description

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

Entropy Numbers of Linear Function Classes

Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, 2000, COLT.

Non-rigid point set registration: Coherent Point Drift

B. Schölkopf, J. Platt, T. Hofmann, 2007 .

Function Contrastive Learning of Transferable Meta-Representations

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

Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning

Dieter Büchler, Vincent Berenz, Simon Guist, 2023, Robotics: Science and Systems.

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..

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

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

Causal analysis of Covid-19 Spread in Germany

Bernhard Schölkopf, Atalanti A. Mastakouri, Atalanti-Anastasia Mastakouri, 2020, NeurIPS.

Information-geometric approach to inferring causal directions

Bernhard Schölkopf, Joris M. Mooij, Jan Lemeire, 2012, Artif. Intell..

Identifying Cause and Effect on Discrete Data using Additive Noise Models

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

B. Schölkopf, F. Jäkel, Felix Wichmann, 2008 .

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

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

Characteristic Kernels on Groups and Semigroups

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

Injective Hilbert Space Embeddings of Probability Measures

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

Tailoring density estimation via reproducing kernel moment matching

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2008, ICML '08.

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

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

Learning Dynamical Systems using Local Stability Priors pdf

Bernhard Schölkopf, Arash Mehrjou, Andrea Iannelli, 2020, Journal of Computational Dynamics.

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

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

S2RMs: Spatially Structured Recurrent Modules pdf

Yoshua Bengio, Bernhard Scholkopf, Muhammad Waleed Gondal, 2020, ICLR.

Computationally efficient face detection

Andrew Blake, Bernhard Schölkopf, Sami Romdhani, 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

Structural Autoencoders Improve Representations for Generation and Transfer pdf

Bernhard Scholkopf, Stefan Bauer, Felix Leeb, 2020, ArXiv.

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.

A theory of independent mechanisms for extrapolation in generative models

Bernhard Schölkopf, Dominik Janzing, Rémy Sun, 2020, AAAI.

Optimal Decision Making Under Strategic Behavior pdf

Bernhard Schölkopf, Adish Singla, Moein Khajehnejad, 2019, ArXiv.

Detecting non-causal artifacts in multivariate linear regression models

Bernhard Schölkopf, Dominik Janzing, D. Janzing, 2018, ICML.

Causal Discovery from Temporally Aggregated Time Series

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2017, UAI.

Discovering Causal Signals in Images

Bernhard Schölkopf, David Lopez-Paz, Soumith Chintala, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Causal and anti-causal learning in pattern recognition for neuroimaging

Tonio Ball, Bernhard Schölkopf, Sebastian Weichwald, 2015, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Towards a Learning Theory of Causation

Bernhard Schölkopf, David Lopez-Paz, Krikamol Muandet, 2015, 1502.02398.

Consistency of Causal Inference under the Additive Noise Model pdf

Bernhard Schölkopf, Eleni Sgouritsa, Dominik Janzing, 2013, ICML.

Inferring deterministic causal relations pdf

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

Causal Markov condition for submodular information measures

Bastian Steudel, D. Janzing, B. Scholkopf, 2010, 1002.4020.

Telling cause from effect based on high-dimensional observations

Bernhard Schölkopf, Patrik O. Hoyer, Dominik Janzing, 2009, ICML.

Identifying confounders using additive noise models

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2009, UAI.

Semi-supervised interpolation in an anticausal learning scenario

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

Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

Bernhard Schölkopf, Dominik Janzing, Xiaohai Sun, 2006, AI&M.

Joint Kernel Maps

Bernhard Schölkopf, Jason Weston, Olivier Bousquet, 2005, IWANN.

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

Experimental and causal view on information integration in autonomous agents pdf

Katja Hofmann, Bernhard Schölkopf, Philipp Geiger, 2016, ArXiv.

Learning View Graphs for Robot Navigation

Bernhard Schölkopf, Heinrich H. Bülthoff, Hanspeter A. Mallot, 1997, AGENTS '97.

Implicit surface modelling as an eigenvalue problem

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

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2006, Comput. Graph. Forum.

Nonparametric Regression between General Riemannian Manifolds

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

Manifold‐valued Thin‐Plate Splines with Applications in Computer Graphics

Bernhard Schölkopf, Florian Steinke, Matthias Hein, 2008, Comput. Graph. Forum.

Markerless 3D Face Tracking

Bernhard Schölkopf, Heinrich H. Bülthoff, Martin Breidt, 2009, DAGM-Symposium.

Support Vector Machines for 3D Shape Processing

Bernhard Schölkopf, Florian Steinke, Volker Blanz, 2005, Comput. Graph. Forum.

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

Karsten M. Borgwardt, B. Schölkopf, K. Borgwardt, 2011, European Journal of Human Genetics.

Resampling Base Distributions of Normalizing Flows

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

Score matching enables causal discovery of nonlinear additive noise models

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

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

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

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).

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

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

Ranking on Data Manifolds

Bernhard Schölkopf, Jason Weston, Arthur Gretton, 2003, NIPS.

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.

Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning

G. Schweikert, Mateo Rojas-Carulla, Alex Hawkins-Hooker, 2023, Nature communications.

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders pdf

Bernhard Schölkopf, Eleni Sgouritsa, Dominik Janzing, 2013, UAI.

Robust Learning via Cause-Effect Models pdf

Bernhard Schölkopf, Dominik Janzing, Kun Zhang, 2011, ArXiv.

Detecting low-complexity unobserved causes

Bernhard Schölkopf, Oliver Stegle, Eleni Sgouritsa, 2011, UAI.

Analysis of Representations for Domain Adaptation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

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

Handbook of Statistical Bioinformatics-ReadingSample

B. Schölkopf, Hongyu Zhao, Liang Chen, 2016 .

Probable Domain Generalization via Quantile Risk Minimization

George J. Pappas, Julius von Kugelgen, Julius von Kügelgen, 2022, Neural Information Processing Systems.

Learning optimal striking points for a ping-pong playing robot

Bernhard Schölkopf, Jan Peters, Yanlong Huang, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning to Play Table Tennis From Scratch Using Muscular Robots

J. Peters, B. Schölkopf, R. Calandra, 2020, IEEE Transactions on Robotics.

Sampling Techniques for Kernel Methods

Bernhard Schölkopf, Dimitris Achlioptas, Frank McSherry, 2001, NIPS.

Linear programs for automatic accuracy control in regression.

B. Schölkopf, Alex Smola, G. Rätsch, 1999 .

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.

Iterative Model-Fitting and Local Controller Optimization - Towards a Better Understanding of Convergence Properties

Bernhard Schölkopf, Manuel Wüthrich, B. Schölkopf, 2018 .

One-Class Support Measure Machines for Group Anomaly Detection pdf

Bernhard Schölkopf, Krikamol Muandet, B. Schölkopf, 2013, UAI.

Learning Disentangled Representations with Wasserstein Auto-Encoders

Bernhard Schölkopf, Ilya O. Tolstikhin, Paul K. Rubenstein, 2018, ICLR.

Speakers optimize information density through syntactic reduction

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Bayesian Kernel Methods

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2002, Machine Learning Summer School.

1 Making Large-scale Svm Learning Practical

T. Joachims, C. Burges, B. Scholkopf, 1999 .

Causal Discovery via Reproducing Kernel Hilbert Space Embeddings

Bernhard Schölkopf, Zhitang Chen, Lai-Wan Chan, 2014, Neural Computation.

On the Latent Space of Wasserstein Auto-Encoders pdf

Bernhard Schölkopf, Ilya O. Tolstikhin, Paul K. Rubenstein, 2018, ArXiv.

Boxhead: A Dataset for Learning Hierarchical Representations pdf

Bernhard Schölkopf, Stefan Bauer, Andrea Dittadi, 2021, ArXiv.

Exploring the Latent Space of Autoencoders with Interventional Assays

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

Structure by Architecture: Disentangled Representations without Regularization

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

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2020, AAAI.

Disentangled State Space Representations pdf

Joachim M. Buhmann, Bernhard Schölkopf, Ðorðe Miladinovic, 2019, ArXiv.

Disentangling Factors of Variation Using Few Labels

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

Interventional Robustness of Deep Latent Variable Models pdf

Stefan Bauer, Bernhard Schölkopf, Ðorðe Miladinovic, 2018, ArXiv.

DCI-ES: AN EXTENDED DISENTANGLEMENT FRAME-

Julius von Kügelgen, Andrei Liviu Nicolicioiu, Andrea Dittadi, 2023 .

Structure by Architecture: Structured Representations without Regularization

Felix Leeb, M. Besserve, Stefan Bauer, 2023, ICLR.

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

Julius von Kügelgen, Andrei Liviu Nicolicioiu, B. Schölkopf, 2022, ICLR.

Interventional Assays for the Latent Space of Autoencoders pdf

Bernhard Scholkopf, Stefan Bauer, Felix Leeb, 2021, ArXiv.

Towards building a Crowd-Sourced Sky Map

Bernhard Schölkopf, David W. Hogg, Dustin Lang, 2014, AISTATS.

Instrumental variable regression via kernel maximum moment loss

B. Scholkopf, Krikamol Muandet, M. Imaizumi, 2020, Journal of Causal Inference.

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

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

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

Bernhard Schölkopf, Nikita Agarwal, Julius von Kügelgen, 2021, ArXiv.

Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, Alex Smola, 2005 .

Structural Causal 3D Reconstruction

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

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

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

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).

Reliable Real Time Ball Tracking for Robot Table Tennis

Jan Peters, B. Schölkopf, S. Gómez-González, 2019, Robotics.

Self-Calibration of Optical Lenses

Bernhard Schölkopf, Michael Hirsch, B. Schölkopf, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

A Kernel Approach to Comparing Distributions

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2007, AAAI.

A Nonparametric Approach to Bottom-Up Visual Saliency

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2006, NIPS.

Energy Functionals for Manifold-valued Mappings and Their Properties

B. Schölkopf, Matthias Hein, F. Steinke, 2008 .

Identification of the Default Mode Network with electroencephalography

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

Machine Learning Applied to Perception: Decision Images for Gender Classification

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

Probabilistic latent variable models for distinguishing between cause and effect

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

Kernels, regularization and differential equations

Bernhard Schölkopf, Florian Steinke, B. Schölkopf, 2008, Pattern Recognit..

An Introduction to Support Vector Machines

D. Politis, M. Akritas, B. Scholkopf, 2003 .

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.

Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach

Bernhard Schölkopf, Luis Herrera, Tobias Jung, 2005, IWANN.

A Compression Approach to Support Vector Model Selection

Bernhard Schölkopf, Ulrike von Luxburg, Olivier Bousquet, 2004, J. Mach. Learn. Res..

Dynamic Alignment Kernels

Bernhard Schölkopf, Alexander J. Smola, Dale Schuurmans, 2000 .

Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators

Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, 1998 .

Distinguishing Cause from Effect Based on Exogeneity pdf

Bernhard Schölkopf, Kun Zhang, Jiji Zhang, 2015, ArXiv.

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery pdf

Bernhard Schölkopf, Dominik Janzing, Kun Zhang, 2010, UAI.

Learning from labeled and unlabeled data on a directed graph

Bernhard Schölkopf, Jiayuan Huang, Dengyong Zhou, 2005, ICML.

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..

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.

Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems pdf

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

Asymptotically Optimal Choice of ε-Loss for Support Vector Machines

B. Schölkopf, K. Müller, Alex Smola, 1998 .

Learning with Kernels: support vector machines, regularization, optimization, and beyond

Alex Smola, B. Scholkopf, 2001, Adaptive computation and machine learning series.

Center-surround patterns emerge as optimal predictors for human saccade targets.

Bernhard Schölkopf, Wolf Kienzle, Felix A Wichmann, 2009, Journal of vision.

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

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

A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming

B. Schölkopf, Jia-Jie Zhu, M. Diehl, 2019, IFAC-PapersOnLine.

The Influence of the Image Basis on Modeling and Steganalysis Performance

Bernhard Schölkopf, Matthias O. Franz, Pham Hai Dang Le, 2010, Information Hiding.

On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension

Bernhard Schölkopf, Yevgeny Seldin, B. Schölkopf, 2013, Empirical Inference.

A Kernel Approach for Learning from Almost Orthogonal Patterns

Bernhard Schölkopf, Jason Weston, Eleazar Eskin, 2002, PKDD.

A Direct Method for Building Sparse Kernel Learning Algorithms

Bernhard Schölkopf, Mingrui Wu, Gökhan H. Bakir, 2006, J. Mach. Learn. Res..

New Support Vector Algorithms

R. C. Williamson, P. Bartlett, Alex Smola, 2000, Neural Computation.

Optimal Coding in Biological and Artificial Neural Networks pdf

Bernhard Schölkopf, Moritz Grosse-Wentrup, Sebastian Weichwald, 2016, ArXiv.

Does Cognitive Science Need Kernels?

B. Schölkopf, F. Jäkel, Felix Wichmann, 2009, Trends in Cognitive Sciences.

Shrinking the Tube: A New Support Vector Regression Algorithm

Bernhard Schölkopf, Alexander J. Smola, Peter L. Bartlett, 1998, NIPS.

Support vector regression with automatic accuracy control.

R. C. Williamson, Alexander J. Smola, P. Bartlett, 1998 .

General cost functions for support vector regression.

B. Schölkopf, K. Müller, Alex Smola, 1998 .

Screening Rules for Convex Problems pdf

Bernhard Schölkopf, Martin Jaggi, Anant Raj, 2015 .

Learning inverse kinematics with structured prediction

Bernhard Schölkopf, Jan Peters, Duy Nguyen-Tuong, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Introduction to Semi-Supervised Learning

Bernhard Schölkopf, Alexander Zien, Olivier Chapelle, 2006, Semi-Supervised Learning.

Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good

Rada Mihalcea, Mrinmaya Sachan, Zhijing Jin, 2023, EMNLP.

1 A primer on kernel methods

Jean-Philippe Vert, Koji Tsuda, B. Scholkopf, 2004 .

Improving Consequential Decision Making under Imperfect Predictions

Niki Kilbertus, Krikamol Muandet, M. Gomez-Rodriguez, 2019, ArXiv.

Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification

Bernhard Schölkopf, Michele Dalponte, Terje Gobakken, 2014, IEEE Transactions on Geoscience and Remote Sensing.

Compact holographic sound fields enable rapid one-step assembly of matter in 3D

B. Schölkopf, P. Fischer, D. Schmitt, 2023, Science advances.

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

Mrinmaya Sachan, Zhijing Jin, K. Shridhar, 2022, ACL.

easyGWAS: An integrated interspecies platform for performing genome-wide association studies pdf

Bernhard Schölkopf, Oliver Stegle, Karsten M. Borgwardt, 2012, ArXiv.

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Bernhard Schölkopf, Stefano Panzeri, Nikos K. Logothetis, 2010, Journal of Computational Neuroscience.

Inferring causation from time series in Earth system sciences

P. Spirtes, C. Glymour, J. Peters, 2019, Nature Communications.

Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators

Julius von Kügelgen, Michael Muehlebach, Klaus-Rudolf Kladny, 2023, Conference on Uncertainty in Artificial Intelligence.

Nonparametric Identifiability of Causal Representations from Unknown Interventions

Julius von Kügelgen, E. Bareinboim, D. Blei, 2023, NeurIPS.

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

B. Schölkopf, Ferenc Husz'ar, V. T'oth, 2022, NeurIPS.

Causal Inference Through the Structural Causal Marginal Problem

Jonas M. Kubler, Julius von Kügelgen, B. Schölkopf, 2022, International Conference on Machine Learning.

Beware of the DAG!

A. Philip Dawid, A. Dawid, D. Janzing, 2008, NIPS Causality: Objectives and Assessment.

Causal Component Analysis

Julius von Kügelgen, M. Besserve, Luigi Gresele, 2023, ArXiv.

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

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

Function Classes for Identifiable Nonlinear Independent Component Analysis

B. Schölkopf, M. Besserve, Simon Buchholz, 2022, NeurIPS.

Relative gradient optimization of the Jacobian term in unsupervised deep learning pdf

Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, 2020, NeurIPS.

xxAI - Beyond Explainable Artificial Intelligence

Julius von Kügelgen, W. Samek, Amir-Hossein Karimi, 2020, xxAI@ICML.

Time-Dependent Demixing of Task-Relevant EEG Signals

Bernhard Schölkopf, Jason Farquhar, Nj Hill, 2006 .

A blind deconvolution approach for pseudo CT prediction from MR image pairs

Bernhard Schölkopf, Bernd J. Pichler, Michael Hirsch, 2012, 2012 19th IEEE International Conference on Image Processing.

The effect of patient positioning aids on PET quantification in PET/MR imaging

Bernhard Schölkopf, Matthias Hofmann, Thomas Beyer, 2011, European Journal of Nuclear Medicine and Molecular Imaging.

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

M. Brady, F. Steinke, J. Farquhar, 2008, Journal of Nuclear Medicine.

Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA

Bernhard Schölkopf, Wolfgang Rosenstiel, Hubert Preissl, 2007, IEEE Transactions on Biomedical Engineering.

Accurate Splice Site Detection for Caenorhabditis elegans

B. Schölkopf, Jean-Philippe Vert, K. Tsuda, 2004 .

Development of Brain-Computer Interface Systems

B. Schölkopf, Jeremy Hill, J. Farquhar, 2009 .

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.

Jacobian-based Causal Discovery with Nonlinear ICA

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

A brain–computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex

B. Schölkopf, M. Grosse-Wentrup, B. Scholkopf, 2014, Journal of neural engineering.

High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces

Bernhard Schölkopf, Moritz Grosse-Wentrup, M. Grosse-Wentrup, 2012, Journal of neural engineering.

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

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

Flow Annealed Importance Sampling Bootstrap

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

Action-Sufficient State Representation Learning for Control with Structural Constraints

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

Control of Musculoskeletal Systems Using Learned Dynamics Models

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

A Local Information Criterion for Dynamical Systems pdf

Bernhard Schölkopf, Sebastian Trimpe, Arash Mehrjou, 2018, ArXiv.

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

B. Schölkopf, L. Itti, A. Mehrjou, 2020, ICML.

Decoding index finger position from EEG using random forests

Tonio Ball, Bernhard Schölkopf, Moritz Grosse-Wentrup, 2014, 2014 4th International Workshop on Cognitive Information Processing (CIP).

normflows: A PyTorch Package for Normalizing Flows

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

Towards a Learning Theory of Cause-Effect Inference

I. Tolstikhin, David Lopez-Paz, Krikamol Muandet, 2015, ICML.

Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration

Bernhard Schölkopf, Nathan D. Cahill, Matthias Hofmann, 2009 .

Flow Matching for Scalable Simulation-Based Inference

Stephen R. Green, J. Macke, Maximilian Dax, 2023, NeurIPS.

A Measure-Theoretic Axiomatisation of Causality

Krikamol Muandet, Simon Buchholz, Junhyung Park, 2023, ArXiv.

Causal Inference

Judea Pearl, J. Pearl, B. Scholkopf, 2010 .

Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators

Julius von Kügelgen, Michael Muehlebach, Klaus-Rudolf Kladny, 2023, UAI.

Discovering Temporal Causal Relations from Subsampled Data

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2015, ICML.

Screening Rules for Convex Problems

Martin Jaggi, Anant Raj, Jakob Olbrich, 2016, ArXiv.

Testing Goodness of Fit of Conditional Density Models with Kernels

Heishiro Kanagawa, Wittawat Jitkrittum, B. Scholkopf, 2020, UAI.

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

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

On the Relationship Between Explanation and Prediction: A Causal View

Simon Kornblith, Krikamol Muandet, Amir-Hossein Karimi, 2022, ICML.

B RIDGING THE G AP TO R EAL -W ORLD O BJECT C ENTRIC L EARNING

T. Brox, Tianjun Xiao, B. Scholkopf, 2022 .

Controlling Text-to-Image Diffusion by Orthogonal Finetuning

Adrian Weller, Zhen Liu, Haiwen Feng, 2023, NeurIPS.

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).

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

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

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

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

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

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

Jointly learning trajectory generation and hitting point prediction in robot table tennis

Bernhard Schölkopf, Jan Peters, Yanlong Huang, 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

Using probabilistic movement primitives for striking movements

Bernhard Schölkopf, Gerhard Neumann, Sebastián Gómez-González, 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).

Randomized Nonlinear Component Analysis pdf

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

Voluntary brain regulation and communication with electrocorticogram signals

T. N. Lal, C. Elger, N. Birbaumer, 2008, Epilepsy & Behavior.

On Data Manifolds Entailed by Structural Causal Models

Amir-Hossein Karimi, Georgios Arvanitidis, Ricardo Dominguez-Olmedo, 2023, ICML.

Natural Actor-Critic for Road Traffic Optimisation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Deep Energy Estimator Networks pdf

Bernhard Schölkopf, Aapo Hyvärinen, Saeed Saremi, 2018, ArXiv.

Multivariable Causal Discovery with General Nonlinear Relationships

M. Bethge, Yash Sharma, B. Schölkopf, 2022 .

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

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

Where did I take that snapshot? Scene-based homing by image matching

Bernhard Schölkopf, Heinrich H. Bülthoff, Hanspeter A. Mallot, 1998, Biological Cybernetics.

Graph Laplacian Regularization for Large-Scale Semidefinite Programming

B. Schölkopf, J. Platt, T. Hofmann, 2007 .

Kernel Methods for Implicit Surface Modeling

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

A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

L. Schöls, M. Grosse-Wentrup, V. Jayaram, 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

Non-parametric estimation of integral probability metrics

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

Adversarial Extreme Multi-label Classification pdf

Bernhard Schölkopf, Rohit Babbar, Rohit Babbar, 2018, ArXiv.

On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 1998, Algorithmica.

Kernel-dependent support vector error bounds

R. C. Williamson, Alexander J. Smola, B. Schölkopf, 1999 .

Differentially Private Database Release via Kernel Mean Embeddings

Bernhard Schölkopf, Matej Balog, Ilya O. Tolstikhin, 2017, ICML.

A General Purpose Neural Architecture for Geospatial Systems

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

Improving alpha matting and motion blurred foreground estimation

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

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Bernhard Schölkopf, Jerry Zeyu Peng, Tejas Vaidhya, 2021, EMNLP.

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data

J. Disselhorst, R. Kontermann, L. Quintanilla‐Martinez, 2023, Nature Biomedical Engineering.

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

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

Probing the Robustness of Independent Mechanism Analysis for Representation Learning

B. Scholkopf, Vincent Stimper, Luigi Gresele, 2022, ArXiv.

On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"

Julius von Kügelgen, B. Scholkopf, M. Besserve, 2022, ArXiv.

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

P. Bühlmann, Niklas Pfister, S. Weichwald, 2018, J. Mach. Learn. Res..

How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data

Bernhard Schölkopf, Thorsten O. Zander, Stefan Harmeling, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

Causal influence of gamma oscillations on the sensorimotor rhythm

Bernhard Schölkopf, Moritz Grosse-Wentrup, N. Jeremy Hill, 2011, NeuroImage.

Fidelity-Weighted Learning pdf

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

Justifying Information-Geometric Causal Inference

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

An Introduction to Support Vector Machines

D. Politis, M. Akritas, B. Scholkopf, 2003 .

Semi-supervised Learning in Causal and Anticausal Settings

Bernhard Schölkopf, Joris M. Mooij, Eleni Sgouritsa, 2013, Empirical Inference.

Semi-supervised Learning on Directed Graphs

Thomas Hofmann, Bernhard Schölkopf, Dengyong Zhou, 2004, NIPS.

Protein functional class prediction with a combined graph

Bernhard Schölkopf, Hyunjung Shin, Koji Tsuda, 2003, Expert Syst. Appl..

Influence Estimation and Maximization in Continuous-Time Diffusion Networks

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

Sparse multiscale gaussian process regression

Bernhard Schölkopf, Kwang In Kim, Christian Walder, 2008, ICML '08.

Photorealistic Video Super Resolution pdf

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

Perceptual Video Super Resolution with Enhanced Temporal Consistency

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

The Hessian perspective into the Nature of Convolutional Neural Networks

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

Statistical Learning and Kernel Methods

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2001, Data Fusion and Perception.

Influence Maximization in Continuous Time Diffusion Networks pdf

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

Influence Maximization in Continuous Time Diffusion Networks

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

Generalized Clustering via Kernel Embeddings

Bernhard Schölkopf, Ulrike von Luxburg, Arthur Gretton, 2009, KI.

Quantifying Information Overload in Social Media and Its Impact on Social Contagions pdf

Krishna P. Gummadi, Bernhard Schölkopf, Manuel Gomez-Rodriguez, 2014, ICWSM.

Multi-way set enumeration in weight tensors

Bernhard Schölkopf, Koji Tsuda, Elisabeth Georgii, 2011, Machine Learning.

Inference of Cause and Effect with Unsupervised Inverse Regression

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

A Kernel for Protein Secondary Structure Prediction

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

Causal Transfer in Machine Learning

Richard E. Turner, B. Schölkopf, J. Peters, 2015, 1507.05333.

Diffeomorphic Dimensionality Reduction

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

Object correspondence as a machine learning problem

Bernhard Schölkopf, Florian Steinke, Volker Blanz, 2005, ICML.

Data scarcity, robustness and extreme multi-label classification

Bernhard Schölkopf, Rohit Babbar, Rohit Babbar, 2019, Machine Learning.

Implicit Wiener series analysis of epileptic seizure recordings

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

Amortized Inference for Causal Structure Learning

Jonas Rothfuss, A. Krause, Lars Lorch, 2022, NeurIPS.

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

Matthias Hohmann, Moritz Grosse-Wentrup, Bernhard Schölkopf, 2020, UIST.

MYND: A Platform for Large-scale Neuroscientific Studies

Bernhard Schölkopf, Matthias Hohmann, Moritz Grosse-Wentrup, 2019, CHI Extended Abstracts.

Training Invariant Support Vector Machines

D. DeCoste, B. Scholkopf, 2002, Machine Learning.

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm

Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez, 2016, J. Mach. Learn. Res..

Adaptive Margin Support Vector Machines

Alexander J. Smola, Dale Schuurmans, Bernhard Schölkopf, 2000 .

Gaussian Processes and SVM: Mean Field and Leave-One-Out

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

On the design of consequential ranking algorithms

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

Can Large Language Models Infer Causation from Correlation?

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

Real Robot Challenge: A Robotics Competition in the Cloud

Charles B. Schaff, Harshit S. Sikchi, Matthew R. Walter, 2021, Neural Information Processing Systems.

A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Philippe Wenk, Bernhard Schölkopf, Ludovic Righetti, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Perceiving the arrow of time in autoregressive motion

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

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

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

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).

Towards fully covariant machine learning

George A. Kevrekidis, B. Schölkopf, D. Hogg, 2023, 2301.13724.

The passive symmetries of machine learning

George A. Kevrekidis, B. Schölkopf, D. Hogg, 2023, ArXiv.

Concepts and Tools

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2001 .

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

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

Semiparametric Support Vector and Linear Programming Machines

Bernhard Schölkopf, Alexander J. Smola, Thilo-Thomas Frieß, 1998, NIPS.

Structure by Architecture: Structured Representations without Regularization

Felix Leeb, M. Besserve, Stefan Bauer, 2020, ICLR.

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

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

Classification on proximity data with LP-machines

R. C. Williamson, B. Schölkopf, K. Müller, 1999 .

Discrete Regularization

B. Schölkopf, Dengyong Zhou, B. Scholkopf, 2006, Semi-Supervised Learning.

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

Christopher K. I. Williams, Cian Eastwood, I. Mason, 2021, ICLR.

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

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

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

Dominik Janzing, Bernhard Scholkopf, Michel Besserve, 2021, CLeaR.

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

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

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

C. Glymour, B. Schölkopf, Kun Zhang, 2019 .

Analysis of cause-effect inference by comparing regression errors

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

Analysis of Cause-Effect Inference via Regression Errors pdf

Bernhard Schölkopf, Takashi Washio, Dominik Janzing, 2018, ArXiv.

Probabilistic Active Learning of Functions in Structural Causal Models pdf

Bernhard Schölkopf, Philipp Hennig, Ilya O. Tolstikhin, 2017, ArXiv.

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

C. Glymour, Kun Zhang, Jiji Zhang, 2016, UAI.

Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Bernhard Schölkopf, Moritz Grosse-Wentrup, Arthur Gretton, 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

Discovery and Visualization of Nonstationary Causal Models

Kun Zhang, Clark Glymour, Jiji Zhang, 2015, 1509.08056.

Towards Robust and Specific Causal Discovery from fMRI pdf

Bernhard Schölkopf, Dajiang Zhu, Kun Zhang, 2015, ArXiv.

Identification of Time-Dependent Causal Model: A Gaussian Process Treatment

Bernhard Schölkopf, Kun Zhang, Biwei Huang, 2015, IJCAI.

On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example

Bernhard Schölkopf, Zhikun Wang, Kun Zhang, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators

Bernhard Schölkopf, Nikos K. Logothetis, Michel Besserve, 2013, NIPS.

Causal reasoning by evaluating the complexity of conditional densities with kernel methods

Bernhard Schölkopf, Dominik Janzing, Xiaohai Sun, 2008, Neurocomputing.

Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions

Bernhard Schölkopf, Dominik Janzing, Xiaohai Sun, 2007, ESANN.

Can Large Language Models Distinguish Cause from Effect?

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

Intrinsic disentanglement: an invariance view for deep generative models

Bernhard Schölkopf, Rémy Sun, Michel Besserve, 2018, ICML 2018.

Kernel methods in medical imaging

Bernhard Schölkopf, Guillaume Charpiat, Matthias Hofmann, 2015 .

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Carl-Johann Simon-Gabriel, B. Scholkopf, 2016, J. Mach. Learn. Res..

Maximum Moment Restriction for Instrumental Variable Regression

B. Schölkopf, B. Scholkopf, Krikamol Muandet, 2020, arXiv.org.

Consistent Kernel Mean Estimation for Functions of Random Variables

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

Computing functions of random variables via reproducing kernel Hilbert space representations

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2015, Statistics and Computing.

A brain computer interface with online feedback based on magnetoencephalography

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

Homomorphism Autoencoder - Learning Group Structured Representations from Observed Transitions

M. Besserve, Hsiao-Ru Pan, Hamza Keurti, 2022, ICML.

Linear Discriminant and Support Vector Classifiers

P. Bartlett, Dale Schuurmans, B. Schölkopf, 2000 .

Phenomenology of Double Descent in Finite-Width Neural Networks

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

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

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

Directed Acoustic Assembly in 3D

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

Spectral Representation Learning for Conditional Moment Models

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

Instrument Space Selection for Kernel Maximum Moment Restriction pdf

Krikamol Muandet, Bernhard Scholkopf, Masaaki Imaizumi, 2021, ArXiv.

Bayesian Quadrature on Riemannian Data Manifolds

B. Schölkopf, Philipp Hennig, A. Gessner, 2021, International Conference on Machine Learning.

Quantization Functionals and Regularized Principal Manifolds

Alexander J. Smola, B. Schölkopf, Alex Smola, 1998 .

Kernel Mean Estimation via Spectral Filtering

Bernhard Schölkopf, Krikamol Muandet, Bharath K. Sriperumbudur, 2014, NIPS.

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

B. Schölkopf, Nhat Ho, B. Scholkopf, 2022 .

Deep Backtracking Counterfactuals for Causally Compliant Explanations

Julius von Kügelgen, Michael Muehlebach, Klaus-Rudolf Kladny, 2023, Trans. Mach. Learn. Res..

Kernel Constrained Covariance for Dependence Measurement

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005, AISTATS.

Multi-Instance Multi-Label Learning with Application to Scene Classification

J. Platt, T. Hofmann, B. Scholkopf, 2007 .

Extension of the nu-SVM range for classification

Bernhard Schölkopf, Jason Weston, Bernhard Schölkopf, 2003 .

Handbook of Statistical Bioinformatics

Bernhard Schölkopf, Hongyu Zhao, Henry Horng-Shing Lu, 2011 .

Energy-Based Models

S. Vishwanathan, T. Hofmann, Alex Smola, 2021, Deep Generative Modeling.

Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

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

Prototype Classification: Insights from Machine Learning

Arnulf B. A. Graf, O. Bousquet, G. Rätsch, 2009, Neural Computation.

An Optimal Witness Function for Two-Sample Testing pdf

Krikamol Muandet, Wittawat Jitkrittum, Bernhard Scholkopf, 2021, ArXiv.

A prior-based approximate latent Riemannian metric pdf

Bernhard Schölkopf, Bogdan Georgiev, Georgios Arvanitidis, 2021, AISTATS.

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).

Two-locus association mapping in subquadratic time

Bernhard Schölkopf, Karsten M. Borgwardt, Panagiotis Achlioptas, 2011, KDD.

Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

R. C. Williamson, Alex Smola, B. Scholkopf, 2001, IEEE Trans. Inf. Theory.

An improved training algorithm for kernel Fisher discriminants

Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, 2001, AISTATS.

Kernel Methods and Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2003 .