Bernhard Schölkopf

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

Minimum Information Exchange in Distributed Systems pdf

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

Modeling Structure via Graphical Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Learning anticipation policies for robot table tennis

Christoph H. Lampert, Bernhard Schölkopf, Jan Peters, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Detecting non-causal artifacts in multivariate linear regression models

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

JMLR Workshop and Conference Proceedings: Volume 6

Isabelle Guyon, Bernhard Schölkopf, Dominik Janzing, 2008, NIPS 2008.

A General Regression Framework for Learning String-to-String Mappings

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Carl E. Rasmussen, Bernhard Schölkopf, Arthur Gretton, 2016, Dagstuhl Reports.

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.

The Need for Open Source Software in Machine Learning

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

From Deterministic ODEs to Dynamic Structural Causal Models

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

Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Bernhard Schölkopf, Lawrence K. Saul, Sebastian Thrun, 2004, NIPS 2004.

Inferring latent structures via information inequalities

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

Bayesian Image Super-resolution, Continued

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

Transfer Learning in Brain-Computer Interfaces

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

Analysis of Cause-Effect Inference via Regression Errors pdf

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

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

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

Comparative Gene Prediction using Conditional Random Fields

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Classification and Memory Behaviour of Man Revisited by Machine

Bernhard Schölkopf, Heinrich H. Bülthoff, Aba Graf, 2004 .

Online Clustering of Moving Hyperplanes

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Combining causal and similarity-based reasoning

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces

Bernhard Schölkopf, Alexander J. Smola, Christopher J. C. Burges, 1998, DAGM-Symposium.

Feature Selection for Support Vector Machines Using Genetic Algorithms

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

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

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

Remote Sensing Feature Selection by Kernel Dependence Measures

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

Extracting Support Data for a Given Task

Bernhard Schölkopf, Christopher J. C. Burges, Vladimir Vapnik, 1995, KDD.

Joint Kernel Maps

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

Multiple Instance Learning for Computer Aided Diagnosis

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

The Kernel Trick for Distances

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2000, NIPS.

Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach

Bernhard Schölkopf, Moritz Grosse-Wentrup, Dominik Janzing, 2016, NeuroImage.

Bayesian Ensemble Learning

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Large Scale Hidden Semi-Markov SVMs

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

Calibrating the pixel-level Kepler imaging data with a causal data-driven model

Bernhard Schölkopf, Daniel Foreman-Mackey, David W. Hogg, 2015 .

Modeling Information Propagation with Survival Theory pdf

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

The human brain as a large margin classifier

Bernhard Schölkopf, Heinrich H. Bülthoff, Aba Graf, 2005 .

Self-Calibration of Optical Lenses

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

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.

Unifying distillation and privileged information pdf

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

Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

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

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

Support Vector Machines

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

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA pdf

Bernhard Schölkopf, Luigi Gresele, Francesco Locatello, 2019, UAI.

Computing the Bayes Kernel Classifier

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

Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Entropy Numbers, Operators and Support Vector Kernels

Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, 1999, EuroCOLT.

Identifiability of Causal Graphs using Functional Models

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

Probabilistic Modeling of Human Dynamics for Intention Inference

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

Machine Learning Applied to Perception: Decision Images for Gender Classification

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

TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification

Bernhard Schölkopf, Krikamol Muandet, Rohit Babbar, 2016, SDM.

Improving the accuracy and speed of support vector learning machines

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

Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings

Bernhard Schölkopf, Manfred K. Warmuth, B. Schölkopf, 2003, Annual Conference Computational Learning Theory.

Advanced Application of Support Vector Machines

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

Analysis of cause-effect inference by comparing regression errors

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

09401 Abstracts Collection - Machine learning approaches to statistical dependences and causality

Bernhard Schölkopf, Steffen L. Lauritzen, Dominik Janzing, 2009, Machine learning approaches to statistical dependences and causality.

Blind multirigid retrospective motion correction of MR images

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2015, Magnetic resonance in medicine.

Context Effects in Category Learning: An Investigation of Four Probabilistic Models

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning the Influence of Spatio-Temporal Variations in Local Image Structure on Visual Saliency

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2007 .

v-Arc: Ensemble Learning in the Presence of Outliers

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

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

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

Regularization on Discrete Spaces

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

Optimal Single-Class Classification Strategies

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A Hilbert Space Embedding for Distributions

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2007, Discovery Science.

Blind source separation for over-determined delayed mixtures

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Kernel Mean Estimation via Spectral Filtering

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

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

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

Transductive Inference with Graphs

Bernhard Schölkopf, Dengyong Zhou, B. Schölkopf, 2004 .

Automatic estimation of modulation transfer functions

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

Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra

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

Regularization Networks and Support Vector Machines

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

A Kernel Approach for Learning from Almost Orthogonal Patterns

Bernhard Schölkopf, Jason Weston, Eleazar Eskin, 2002, European Conference on Principles of Data Mining and Knowledge Discovery.

DeepMAsED: Evaluating the quality of metagenomic assemblies.

Bernhard Schölkopf, Mateo Rojas-Carulla, Olga Mineeva, 2020, Bioinformatics.

Efficient Encoding of Dynamical Systems Through Local Approximations

Bernhard Schölkopf, Sebastian Trimpe, Arash Mehrjou, 2018, 2018 IEEE Conference on Decision and Control (CDC).

Accurate prediction of alternative splicing events

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2005, NIPS 2005.

Generalization in anti-causal learning pdf

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

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..

Cause-Effect Inference by Comparing Regression Errors

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

Differentially Private Database Release via Kernel Mean Embeddings

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

Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

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

Inferring deterministic causal relations pdf

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

Incorporating Invariances in Support Vector Learning Machines

Bernhard Schölkopf, Christopher J. C. Burges, Vladimir Vapnik, 1996, ICANN.

A Hilbert Space Embedding for Distributions

Le Song, Bernhard Schölkopf, Alexander J. Smola, 2007, IFIP Working Conference on Database Semantics.

Real Time Trajectory Prediction Using Deep Conditional Generative Models

Bernhard Schölkopf, Sebastián Gómez-González, Jan Peters, 2020, IEEE Robotics and Automation Letters.

Learning Dense 3D Correspondence

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

A Primer on KernelMethods

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

Discovering Temporal Causal Relations from Subsampled Data

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

Learning from labeled and unlabeled data on a directed graph

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

A Permutation-Based Kernel Conditional Independence Test

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

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.

Kernel Constrained Covariance for Dependence Measurement

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

Spatio-Temporal Transformer Network for Video Restoration

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

Semi-supervised Learning in Causal and Anticausal Settings

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

Learning annotated hierarchies from relational data

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

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

Causal Inference on Time Series using Restricted Structural Equation Models

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, 2013, NIPS.

Learning Theory and Kernel Machines

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

A Guided Task for Cognitive brain-Computer Interfaces

Bernhard Schölkopf, Matthias Hohmann, Moritz Grosse-Wentrup, 2017, GBCIC.

Optimal Coding in Biological and Artificial Neural Networks pdf

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

Switched Latent Force Models for Movement Segmentation

Bernhard Schölkopf, Neil D. Lawrence, Jan Peters, 2010, NIPS.

Invariant Feature Extraction and Classification in Kernel Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

On causal and anticausal learning

Bernhard Schölkopf, Joris M. Mooij, Eleni Sgouritsa, 2012, ICML.

Classification of Faces in Man and Machine

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

Interventional Robustness of Deep Latent Variable Models pdf

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

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders

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

Learning Independent Causal Mechanisms

Bernhard Schölkopf, Mateo Rojas-Carulla, Giambattista Parascandolo, 2017, ICML.

Splice Form Prediction using Machine Learning

Bernhard Schölkopf, Jagan Srinivasan, K-R Müller, 2006, ISMB 2006.

Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments

Stefan Bauer, Joachim M. Buhmann, Bernhard Schölkopf, 2019, DGS@ICLR.

Learning with Hypergraphs: Clustering, Classification, and Embedding

Bernhard Schölkopf, Jiayuan Huang, Dengyong Zhou, 2006, NIPS.

A note on integral probability metrics and $\phi$-divergences pdf

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

Aggregating Classification Accuracy across Time: Application to Single Trial EEG

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola, 2006, ISMB.

Efficient Algorithms for Max-Margin Structured Classification

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Kernel Methods for Implicit Surface Modeling

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

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

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2013, UAI.

MYND: A Platform for Large-scale Neuroscientific Studies

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

A tutorial on support vector regression

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2004, Stat. Comput..

Retrospective Motion Correction of Magnitude-Input MR Images

Bernhard Schölkopf, Klaus Scheffler, Christian J. Schuler, 2015, MLMMI@ICML.

Feature extraction from the Hermitian manifold for Brain-Computer Interfaces

Bernhard Schölkopf, Vinay Jayaram, Moritz Grosse-Wentrup, 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).

Object correspondence as a machine learning problem

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

The connection between regularization operators and support vector kernels

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

Efficient Methods for Privacy Preserving Face Detection

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Data scarcity, robustness and extreme multi-label classification

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

Unsupervised Regression with Applications to Nonlinear System Identification

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Navigation mit Schnappschüssen

Andreas Zell, Bernhard Schölkopf, Heinrich H. Bülthoff, 1998, DAGM-Symposium.

Towards Machine Learning of Motor Skills

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

Causal Discovery from Heterogeneous/Nonstationary Data pdf

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

Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database

Bernhard Schölkopf, Betty J. Mohler, Michael Hirsch, 2012, ECCV.

Differential Entropic Clustering of Multivariate Gaussians

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Optimizing Human Learning pdf

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

Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

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

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

Learning to Deblur

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Causal Inference Using the Algorithmic

Bernhard Schölkopf, Dominik Janzing, B. Schölkopf, 2010 .

Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems

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

HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization

Bernhard Schölkopf, Thomas Schultz, Lara Schlaffke, 2013, Comput. Graph. Forum.

Ranking on Data Manifolds

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

An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2009 .

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML 2018.

Model based reconstruction for GRE EPI

Bernhard Schölkopf, Rolf Pohmann, Matthias Seeger, 2011 .

Large Margin Hidden Markov Models for Automatic Speech Recognition

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

An Application of Reinforcement Learning to Aerobatic Helicopter Flight

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Some kernels for structured data

Bernhard Schölkopf, Peter L. Bartlett, P. Bartlett, 2001 .

Learning to Rank with Nonsmooth Cost Functions

Thomas Hofmann, Bernhard Schölkopf, Quoc V. Le, 2006, NIPS.

Identifying confounders using additive noise models

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

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

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

Support Vector Machines for 3D Shape Processing

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

Stability of K-Means Clustering

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Maximal Margin Perception

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

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

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

Learning an Interest Operator from Human Eye Movements

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A recipe for optimizing a time-histogram

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Kernels, regularization and differential equations

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

An Auditory Paradigm for Brain-Computer Interfaces

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

Training Invariant Support Vector Machines

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

An Analysis of Inference with the Universum

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

A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A selective attention multi-chip system with dynamic synapses and spiking neurons

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2005, IEEE Transactions on Neural Networks.

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).

Influence Maximization in Continuous Time Diffusion Networks pdf

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

Support Vectors and Statistical Mechanics

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

Causal Inference on Time Series using Structural Equation Models pdf

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

Support vector channel selection in BCI

Bernhard Schölkopf, Jason Weston, Thilo Hinterberger, 2004, IEEE Transactions on Biomedical Engineering.

Temporal dynamics of information content carried by neurons in the primary visual cortex

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Robust Ensemble Learning

Gunnar Rätsch, Bernhard Schölkopf, Dale Schuurmans, 2000 .

Adversarial Extreme Multi-label Classification pdf

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

In-Network PCA and Anomaly Detection

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

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

Convex Cost Functions for Support Vector Regression

Bernhard Schölkopf, Alexander J. Smola, Tom Ziemke, 1998 .

Ordinal Regression by Extended Binary Classification

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A Local Learning Approach for Clustering

Bernhard Schölkopf, Mingrui Wu, B. Schölkopf, 2006, NIPS.

Coordination via predictive assistants from a game-theoretic view pdf

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

Kernel methods in medical imaging

Bernhard Schölkopf, Guillaume Charpiat, Matthias Hofmann, 2015 .

Consequential Ranking Algorithms and Long-term Welfare pdf

Bernhard Schölkopf, Vicenç Gómez, Abir De, 2019, ArXiv.

Implicit Volterra and Wiener Series for Higher-Order Image Analysis

Bernhard Schölkopf, Matthias O. Franz, 2006 .

Inferring Network Structure from Co-Occurrences

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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).

Learning View Graphs for Robot Navigation

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

A Direct Method for Building Sparse Kernel Learning Algorithms

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

Proceedings of the 2008th International Conference on Causality: Objectives and Assessment - Volume 6

Isabelle Guyon, Bernhard Schölkopf, Dominik Janzing, 2008 .

Learning with Local and Global Consistency

Bernhard Schölkopf, Jason Weston, Dengyong Zhou, 2003, NIPS.

Regularized Principal Manifolds

Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, 1999, J. Mach. Learn. Res..

Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Correcting Sample Selection Bias by Unlabeled Data

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2006, NIPS.

Kernel Measures of Conditional Dependence

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2007, NIPS.

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.

A Blind Deconvolution Approach for Improving the Resolution of Cryo-EM Density Maps

Bernhard Schölkopf, Michael Hirsch, Michael Habeck, 2011, J. Comput. Biol..

View-Based Cognitive Mapping and Path Planning

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

Discriminative k-shot learning using probabilistic models pdf

Bernhard Schölkopf, Richard E. Turner, Mateo Rojas-Carulla, 2017, ArXiv.

Support Vector Machines as Probabilistic Models

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

On Estimation of Functional Causal Models

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

A tutorial on ν-support vector machines: Research Articles

Bernhard Schölkopf, Pai-Hsuen Chen, Chih-Jen Lin, 2005 .

Boosting Structured Prediction for Imitation Learning

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning causal mechanisms

Bernhard Schölkopf, B. Schölkopf, 2019, GI-Jahrestagung.

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

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

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

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

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

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

Group invariance principles for causal generative models pdf

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

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).

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

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

A Kernel Two-Sample Test

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2012, J. Mach. Learn. Res..

A tutorial on v-support vector machines

Bernhard Schölkopf, P-H Chen, C-J Lin, 2005 .

Game Theoretic Algorithms for Protein-DNA binding

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Statistical Learning Theory: Models, Concepts, and Results

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

Sparse Representation for Signal Classification

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

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.

Implicit Online Learning with Kernels

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Markerless 3D Face Tracking

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

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

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

Pattern recognition : 26th DAGM Symposium, Tübingen, Germany, August 30 - Sepember 1, 2004 : proceedings

Bernhard Schölkopf, Martin A. Giese, Heinrich H. Bülthoff, 2004 .

Modeling confounding by half-sibling regression

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

Denoising and Dimension Reduction in Feature Space

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Choosing /spl nu/ in support vector regression with different noise models-theory and experiments

Bernhard Schölkopf, Alexander J. Smola, Athanassia Chalimourda, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Gaussian Process-Based Predictive Control for Periodic Error Correction

Bernhard Schölkopf, Melanie Nicole Zeilinger, Philipp Hennig, 2016, IEEE Transactions on Control Systems Technology.

Learning-based solution to phase error correction in T2*-weighted GRE scans

Bernhard Schölkopf, Klaus Scheffler, Alexander Loktyushin, 2018 .

Lernen mit Kernen

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1999, Informatik Forschung und Entwicklung.

Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

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

Cluster Kernels for Semi-Supervised Learning

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

Nonparametric Regression between General Riemannian Manifolds

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

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

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

New Support Vector Algorithms

Bernhard Schölkopf, Alex J Smola, Robert C Williamson, 2000, Neural Computation.

A Kernel for Protein Secondary Structure Prediction

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

A brain computer interface with online feedback based on magnetoencephalography

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

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.

Sparse Kernel Orthonormalized PLS for feature extraction in large data sets

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning strategies in table tennis using inverse reinforcement learning

Bernhard Schölkopf, Betty J. Mohler, Jan Peters, 2014, Biological Cybernetics.

BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI

Bernhard Schölkopf, Thomas Schultz, Tobias Schmidt-Wilcke, 2017, Pattern Recognit..

Optimization of k-Space Trajectories by Bayesian Experimental Design

Bernhard Schölkopf, Hannes Nickisch, Rolf Pohmann, 2009 .

Structured Prediction Based on Discriminative Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems pdf

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

AdaBoost is Consistent

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Popper, Falsification and the VC-dimension

Bernhard Schölkopf, D Corfield, Vapnik, 2005 .

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.

Convergence of Laplacian Eigenmaps

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Maximal margin classification for metric spaces

Bernhard Schölkopf, Matthias Hein, Olivier Bousquet, 2005, J. Comput. Syst. Sci..

Inference of Cause and Effect with Unsupervised Inverse Regression

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

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

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

Probabilistic Active Learning of Functions in Structural Causal Models pdf

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

An introduction to kernel learning algorithms

Peter V. Gehler, Bernhard Schölkopf, B. Schölkopf, 2009 .

Causal influence of gamma oscillations on the sensorimotor rhythm

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

Human Classification Behaviour Revisited by Machine Learning

Bernhard Schölkopf, Heinrich H. Bülthoff, Hanspeter A. Mallot, 2004 .

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

Bernhard Schölkopf, Alexander J. Smola, Athanassia Chalimourda, 2005, Neural Networks.

An improved training algorithm for kernel Fisher discriminants

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

Center-surround filters emerge from optimizing predictivity in a free-viewing task

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2007 .

Shrinking the Tube: A New Support Vector Regression Algorithm

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

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

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.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Anticipatory action selection for human-robot table tennis

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

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Large Scale Multiple Kernel Learning

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2006, J. Mach. Learn. Res..

Building Sparse Large Margin Classifiers

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

Support Vector Machines

Bernhard Schölkopf, Alexander J. Smola, 2014 .

Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

The Entropy Regularization Information Criterion

Bernhard Schölkopf, Alexander J. Smola, John Shawe-Taylor, 1999, NIPS.

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Towards Robust and Specific Causal Discovery from fMRI pdf

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

Kernel Methods in Computational Biology

Bernhard Schölkopf, Jean-Philippe Vert, Koji Tsuda, 2005 .

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Domain Adaptation under Target and Conditional Shift

Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, 2013, ICML.

Semi-supervised interpolation in an anticausal learning scenario

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

Tailoring density estimation via reproducing kernel moment matching

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

Quantum Mean Embedding of Probability Distributions pdf

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

Kernel Mean Embedding of Distributions: A Review and Beyonds pdf

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2016, Found. Trends Mach. Learn..

Fast Kernels for String and Tree Matching

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

Closing One's eyes Affects amplitude modulation but not frequency modulation in a Cognitive BCI

Bernhard Schölkopf, Moritz Grosse-Wentrup, Marius Goerner, 2017, GBCIC.

Domain Generalization via Invariant Feature Representation pdf

Bernhard Schölkopf, Krikamol Muandet, David Balduzzi, 2013, ICML.

Detecting Humans via Their Pose

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Support Vector Method for Novelty Detection

Bernhard Schölkopf, Alexander J. Smola, John C. Platt, 1999, NIPS.

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).

Introduction to support vector learning

Bernhard Schölkopf, Alexander J. Smola, Christopher J. C. Burges, 1999 .

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).

Energy-Based Models

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Fast Discriminative Visual Codebooks using Randomized Clustering Forests

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Kernel Methods for Measuring Independence

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

Tempered Adversarial Networks

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

Robust Learning via Cause-Effect Models pdf

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

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

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

Detecting low-complexity unobserved causes

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

A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds

Bernhard Schölkopf, Michael E. Tipping, B. Schölkopf, 2001, AISTATS.

Probabilistic Modeling of Human Movements for Intention Inference

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

Causal Inference Using the Algorithmic Markov Condition

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

Tighter PAC-Bayes Bounds

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Estimating Causal Effects by Bounding Confounding

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

Single Channel Speech Separation Using Factorial Dynamics

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning from Distributions via Support Measure Machines

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2012, NIPS.

The Neurodynamics of Belief Propagation on Binary Markov Random Fields

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

Support Vector Machine Applications in Computational Biology

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

Input space versus feature space in kernel-based methods

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, IEEE Trans. Neural Networks.

Use of the Zero-Norm with Linear Models and Kernel Methods

Bernhard Schölkopf, Jason Weston, Michael E. Tipping, 2003, J. Mach. Learn. Res..

Wasserstein Auto-Encoders pdf

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

Temporal Coding using the Response Properties of Spiking Neurons

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Adversarial Vulnerability of Neural Networks Increases With Input Dimension pdf

Bernhard Schölkopf, David Lopez-Paz, Léon Bottou, 2018, ArXiv.

Kernel-based dependence detection in the Macaque visual cortex

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

Removing systematic errors for exoplanet search via latent causes

Bernhard Schölkopf, Dominik Janzing, Daniel Foreman-Mackey, 2015, ICML.

Generalized Clustering via Kernel Embeddings

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

Active learning for misspecified generalized linear models

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Online Classification for Complex Problems Using Simultaneous Projections

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Disentangling Factors of Variations Using Few Labels pdf

Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, 2020, ICLR.

Stochastic Relational Models for Discriminative Link Prediction

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Identifying Cause and Effect on Discrete Data using Additive Noise Models

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

Statistical Modeling of Images with Fields of Gaussian Scale Mixtures

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Randomized Nonlinear Component Analysis pdf

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

The Randomized Dependence Coefficient

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

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

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

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

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

Computationally efficient face detection

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

Informative Features for Model Comparison

Bernhard Schölkopf, Arthur Gretton, Wittawat Jitkrittum, 2018, NeurIPS.

Support Vector Machines and Kernels for Computational Biology

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

Statistical Learning and Kernel Methods in Bioinformatics

Isabelle Guyon, Bernhard Schölkopf, Jason Weston, 2003 .

Protein functional class prediction with a combined graph

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

Information-geometric approach to inferring causal directions

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

Scalable Discriminative Learning for Natural Language Parsing and Translation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Towards building a Crowd-Sourced Sky Map

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

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

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

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks pdf

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2014, J. Mach. Learn. Res..

iLSTD: Eligibility Traces and Convergence Analysis

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Multivariate Regression with Stiefel Constraints

Gökhan BakIr, Bernhard Schölkopf, Arthur Gretton, 2004 .

On Probabilistic Conditional Independence Structures

Bernhard Schölkopf, Michael I. Jordan, Jon Kleinberg, 2005 .

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

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

Towards a Strategy for Boosting Regressors

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

Multi-Source Domain Adaptation: A Causal View

Bernhard Schölkopf, Mingming Gong, Kun Zhang, 2015, AAAI.

A Kernel Approach for Learning from Almost Orthogonal Patterns

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

A Kernel Statistical Test of Independence

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

An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

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

Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Real-time adaptive information-theoretic optimization of neurophysiology experiments

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Support Vector methods in learning and feature extraction

Bernhard Schölkopf, Marcus Gallagher, Tom Downs, 1998 .

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).

Robust Ensemble Learning for Data Mining

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, PAKDD.

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.

Speakers optimize information density through syntactic reduction

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Kernel Mean Matching for Content Addressability of GANs

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

Multi-way set enumeration in real-valued tensors

Bernhard Schölkopf, Koji Tsuda, Elisabeth Georgii, 2009, DMMT '09.

Semiparametric Support Vector and Linear Programming Machines

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

Prior Knowledge in Support Vector Kernels

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

Implicit Wiener Series for Higher-Order Image Analysis

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

Data Integration for Classification Problems Employing Gaussian Process Priors

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Automated Hierarchy Discovery for Planning in Partially Observable Environments

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Nonlinear physically-based models for decoding motor-cortical population activity

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Pre-Images and Reduced Set Methods

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

Adaptation and Robust Learning of Probabilistic Movement Primitives

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

Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions

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

Learning Blind Motion Deblurring

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

Probabilistic latent variable models for distinguishing between cause and effect

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

Experimental and causal view on information integration in autonomous agents pdf

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

Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Kernel Mean Shrinkage Estimators pdf

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

Kernel Dependency Estimation

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

Statistical Learning and Kernel Methods

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

Dirichlet-Enhanced Spam Filtering based on Biased Samples

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Local Group Invariant Representations via Orbit Embeddings pdf

Bernhard Schölkopf, Youssef Mroueh, Anant Raj, 2016, AISTATS.

Kernel Methods and Support Vector Machines

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

Deep Energy Estimator Networks pdf

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

AdaGAN: Boosting Generative Models

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

Analysis of Benchmarks

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

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

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2016, UAI.

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

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

Artificial intelligence: Learning to see and act

Bernhard Schölkopf, B. Schölkopf, B. Scholkopf, 2015, Nature.

Towards a Learning Theory of Causation

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

Fast removal of non-uniform camera shake

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

Clustering appearance and shape by learning jigsaws

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Analysis of Representations for Domain Adaptation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Philippe Wenk, Bernhard Schölkopf, Michael A. Osborne, 2019, ICML.

Depth Estimation Through a Generative Model of Light Field Synthesis

Bernhard Schölkopf, Michael Hirsch, Mehdi S. M. Sajjadi, 2016, GCPR.

Bayesian Policy Gradient Algorithms

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

Entropy Numbers of Linear Function Classes

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

Uncovering the Temporal Dynamics of Diffusion Networks pdf

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

Mask-Specific Inpainting with Deep Neural Networks

Bernhard Schölkopf, Stefan Harmeling, Christian J. Schuler, 2014, GCPR.

Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Support Vector Machines on a Budget

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Causal Inference on Discrete Data Using Additive Noise Models

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

Efficient sparse coding algorithms

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Bernhard Schölkopf, Tomaso A. Poggio, Federico Girosi, 1997, IEEE Trans. Signal Process..

Image Construction by Linear Programming

Gunnar Rätsch, Bernhard Schölkopf, Lawrence K. Saul, 2004, NIPS 2004.

Semi-supervised Learning on Directed Graphs

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

An Introduction to Support Vector Machines

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

Semi-Supervised Learning through Principal Directions Estimation

Bernhard Schölkopf, Jason Weston, Olivier Chapelle, 2003, ICML 2003.

PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Feature Selection for Troubleshooting in Complex Assembly Lines

Bernhard Schölkopf, Andreas Feustel, Tobias Pfingsten, 2007, IEEE Transactions on Automation Science and Engineering.

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.

One-Class Support Measure Machines for Group Anomaly Detection pdf

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

Face Detection - Efficient and Rank Deficient

Bernhard Schölkopf, Matthias O. Franz, Wolf Kienzle, 2004, NIPS.

Distinguishing Cause from Effect Based on Exogeneity pdf

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

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

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

Kernels for Biological Data

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

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

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

Causal inference in sensorimotor integration

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Generalized Score Functions for Causal Discovery

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

Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Inexact Matching String Kernels for Protein Classification

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

From Regularization Operators to Support Vector Kernels

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 1997, NIPS.

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

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

Introduction to kernel methods

Bernhard Schölkopf, B. Schölkopf, 2003 .

Learning Structural Equation Models for fMRI

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Map-Reduce for Machine Learning on Multicore

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Generalization Bounds and Consistency for Structured Labeling

Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, 2007 .

Fast protein classification with multiple networks

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

Bernhard Schölkopf, Florian Steinke, Nathan D. Cahill, 2009, CVPR 2009.

Bayesian Online Detection and Prediction of Change Points pdf

Stefan Bauer, Bernhard Schölkopf, Jan Peters, 2019, ArXiv.

Concepts and Tools

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

Causal models for debugging and control in cloud computing pdf

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

Kernel-based measures of dependence in the Macaque visual cortex

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

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.

Estimating the Support of a High-Dimensional Distribution

Bernhard Schölkopf, Alexander J. Smola, John Shawe-Taylor, 2001, Neural Computation.

A brain-robot interface for studying motor learning after stroke

Bernhard Schölkopf, Moritz Grosse-Wentrup, Jan Peters, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Consistency of Causal Inference under the Additive Noise Model pdf

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

Advances in Neural Information Processing Systems 21 (NIPS 2008)

Gunnar Rätsch, Bernhard Schölkopf, Christian Widmer, 2008 .

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

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

A Generalized Representer Theorem

Bernhard Schölkopf, Alexander J. Smola, Ralf Herbrich, 2001, COLT/EuroCOLT.

GeNet: Deep Representations for Metagenomics

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

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).

Semi-Supervised Learning (Adaptive Computation and Machine Learning)

Bernhard Schölkopf, Alexander Zien, Olivier Chapelle, 2006 .

Telling cause from effect in deterministic linear dynamical systems pdf

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

Iterative kernel principal component analysis for image modeling

Bernhard Schölkopf, Kwang In Kim, Matthias O. Franz, 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Beyond the Margin

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

Kernel PCA and De-Noising in Feature Spaces

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

Kernel methods and dimensionality reduction

Bernhard Schölkopf, 2003 .

Multidimensional Contrast Limited Adaptive Histogram Equalization

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

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

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

Learning Optimal EEG Features Across Time, Frequency and Space

Bernhard Schölkopf, Jason Farquhar, J Hill, 2006, NIPS 2006.

Nonlinear Receptive Field Analysis: Making Kernel Methods Interpretable

Bernhard Schölkopf, Felix A. Wichmann, Jakob H. Macke, 2007 .

Learning from Labeled and Unlabeled Data Using Random Walks

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

Guest Editorial

Pietro Perona, Bernhard Schölkopf, William T. Freeman, 2008, International Journal of Computer Vision.

Clustering Meets Implicit Generative Models pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018, ICLR.

Hilbert Space Embeddings and Metrics on Probability Measures

Bernhard Schölkopf, Kenji Fukumizu, Arthur Gretton, 2009, J. Mach. Learn. Res..

Automatic Image Colorization Via Multimodal Predictions

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

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

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

Learning to Find Pre-Images

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

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.

Sparse Kernel Feature Analysis

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

RASE: recognition of alternatively spliced exons in C.elegans

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

Kernel machine based learning for multi-view face detection and pose estimation

Bernhard Schölkopf, Stan Z. Li, HongJiang Zhang, 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

Natural Actor-Critic for Road Traffic Optimisation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A Compression Approach to Support Vector Model Selection

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

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

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

Annealed Generative Adversarial Networks pdf

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

Kernel-Based Integration of Genomic Data Using Semidefinite Programming

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

Domain Adaptation with Conditional Transferable Components

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2016, ICML.

Sparse online model learning for robot control with support vector regression

Bernhard Schölkopf, Duy Nguyen-Tuong, Jan Peters, 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Structure and dynamics of information pathways in online media

Bernhard Schölkopf, Jure Leskovec, Manuel Gomez-Rodriguez, 2012, WSDM.

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.

Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm

Bernhard Schölkopf, Rodney J. Douglas, Susanne Still, 2000, NIPS.

Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference (Bradford Books)

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

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

Semi-Supervised Domain Adaptation with Non-Parametric Copulas

Bernhard Schölkopf, David Lopez-Paz, José Miguel Hernández-Lobato, 2012, NIPS.

Attentional Processing on a Spike-Based VLSI Neural Network

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

TrueSkill™: A Bayesian Skill Rating System

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Optimal Change-Detection and Spiking Neurons

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A kernel view of the dimensionality reduction of manifolds

Bernhard Schölkopf, Daniel D. Lee, Sebastian Mika, 2004, ICML.

Sparse Greedy Matrix Approximation for Machine Learning

Bernhard Schölkopf, Alexander J. Smola, B. Schölkopf, 2000, International Conference on Machine Learning.

Generalized Support Vector Machines

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

Efficient Learning of Sparse Representations with an Energy-Based Model

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Learning Inverse Dynamics: a Comparison

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

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

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

Generalized Maximum Margin Clustering and Unsupervised Kernel Learning

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Incorporating Invariances in Non-Linear Support Vector Machines

Bernhard Schölkopf, Olivier Chapelle, B. Schölkopf, 2001, NIPS.

Flexible Spatio-Temporal Networks for Video Prediction

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

3D Reconstruction of Neural Circuits from Serial EM Images

Bernhard Schölkopf, Alexander Borst, Winfried Denk, 2007 .

Semi-supervised learning, causality, and the conditional cluster assumption

Bernhard Schölkopf, Marco Loog, Julius von Kügelgen, 2019, UAI.

Predicting Structured Data (Neural Information Processing)

Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, 2007 .

Learning an Interest Operator from Eye Movements

Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz, 2005 .

Kernel Hebbian Algorithm for single-frame super-resolution

Bernhard Schölkopf, Kwang In Kim, Matthias O. Franz, 1998 .

Blind Correction of Optical Aberrations

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

Training Support Vector Machines with Multiple Equality Constraints

Bernhard Schölkopf, Wolf Kienzle, 2005, ECML.

Large Margin Bank Boundaries for Ordinal Regression

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

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 Latent Space of Wasserstein Auto-Encoders pdf

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

Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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.

Description of Probabilistic Models

Bernhard Schölkopf, Michael I. Jordan, Jon Kleinberg, 2005 .

Distilling Information Reliability and Source Trustworthiness from Digital Traces

Bernhard Schölkopf, Mehrdad Farajtabar, Behzad Tabibian, 2017, WWW.

Methods Towards Invasive Human Brain Computer Interfaces

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

Finding dependencies between frequencies with the kernel cross-spectral density

Bernhard Schölkopf, Nikos K. Logothetis, Dominik Janzing, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning view graphs for robot navigation

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

Parameter Expanded Variational Bayesian Methods

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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.

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

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

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.

Autofocusing‐based phase correction

Bernhard Schölkopf, Klaus Scheffler, Philipp Ehses, 2018, Magnetic resonance in medicine.

Seeing the Arrow of Time

Bernhard Schölkopf, Changshui Zhang, Andrew Zisserman, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

The Arrow of Time in Multivariate Time Series

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

A Bayesian Approach to Diffusion Models of Decision-Making and Response Time

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

Philippe Wenk, Bernhard Schölkopf, Andreas Krause, 2020, ArXiv.

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).

Non-parametric estimation of integral probability metrics

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

A Tutorial Introduction

Bernhard Schölkopf, Alexander J. Smola, P. C. Ölveczky, 2001 .

Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]

Bernhard Schölkopf, Sebastian Thrun, Lawrence K. Saul, 2004, NIPS.

Causal Discovery from Temporally Aggregated Time Series

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

KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug Design-Binding to Thrombin

Bernhard Schölkopf, Jason Weston, André Elisseeff, 2001, Knowledge Discovery and Data Mining.

Sampling Techniques for Kernel Methods

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

End-to-End Learning for Image Burst Deblurring pdf

Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch, 2016, ACCV.

Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions

Bernhard Schölkopf, Dominik Janzing, Xiaohai Sun, 2006, NIPS 2006.

Deconfounding Reinforcement Learning in Observational Settings pdf

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

A Review of Kernel Methods in Machine Learning

Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, 2006 .

Improving alpha matting and motion blurred foreground estimation

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

Learning to parse images of articulated bodies

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Beyond pairwise classification and clustering using hypergraphs

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

GACV for Support Vector Machines

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

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

Bernhard Schölkopf, Vinay Jayaram, Matthias Hohmann, 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

Local learning projections

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

Data Fusion with Kernel Methods

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

Impact of target-to-target interval on classification performance in the P300 speller

Bernhard Schölkopf, Jason Farquhar, J Hill, 2007 .

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

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

Causal Markov Condition for Submodular Information Measures

Bernhard Schölkopf, Dominik Janzing, Bastian Steudel, 2010, COLT.

Relational Learning with Gaussian Processes

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Consistent Kernel Mean Estimation for Functions of Random Variables

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

Predicting Time Series with Support Vector Machines

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1997, ICANN.

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Bernhard Schölkopf, Neil D. Lawrence, Neil D. Lawrence, 2001, ICML.

Preface to the ACM TIST Special Issue on Causal Discovery and Inference

Bernhard Schölkopf, Jiuyong Li, Kun Zhang, 2016, ACM Trans. Intell. Syst. Technol..

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML.

DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification

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

The Problem of Representative Choice

Bernhard Schölkopf, Michael I. Jordan, Jon Kleinberg, 2005 .

Pattern Recognition

Bernhard Schölkopf, Martin A. Giese, Heinrich H. Bülthoff, 2004, Lecture Notes in Computer Science.

Kernel Mean Estimation and Stein Effect

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

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).

Diffeomorphic Dimensionality Reduction

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

groupICA: Independent component analysis for grouped data pdf

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

Causal discovery with continuous additive noise models

Bernhard Schölkopf, Joris M. Mooij, Dominik Janzing, 2013, J. Mach. Learn. Res..

Removing noise from astronomical images using a pixel-specific noise model

Bernhard Schölkopf, Stefan Harmeling, Harold Christopher Burger, 2011, 2011 IEEE International Conference on Computational Photography (ICCP).

Measuring Statistical Dependence with Hilbert-Schmidt Norms

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

The Robustness-Performance Tradeoff in Markov Decision Processes

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Support Vector Machines and Kernel Algorithms.

Bernhard Schölkopf, P. Armitage T. Colton, B. Schölkopf, 2005 .

Characteristic Kernels on Groups and Semigroups

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

A New Method for Constructing Artificial Neural Networks

Bernhard Schölkopf, Cjc Burges, Vapnik, 1995 .

Causal Consistency of Structural Equation Models pdf

Bernhard Schölkopf, Joris M. Mooij, Moritz Grosse-Wentrup, 2017, UAI.

Kernel Methods for Detecting the Direction of Time Series

Bernhard Schölkopf, Arthur Gretton, Dominik Janzing, 2008, GfKl.

Multivariate Regression via Stiefel Manifold Constraints

Bernhard Schölkopf, Arthur Gretton, Matthias O. Franz, 2004, DAGM-Symposium.

Injective Hilbert Space Embeddings of Probability Measures

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

Prototype Classification: Insights from Machine Learning

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

Elements of Causal Inference: Foundations and Learning Algorithms

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, 2017 .

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

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

On Causal Discovery with Cyclic Additive Noise Models

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

Implicit surface modelling as an eigenvalue problem

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

Learning Nonparametric Models for Probabilistic Imitation

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Causal Discovery via Reproducing Kernel Hilbert Space Embeddings

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

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations pdf

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2018, ICML.

Support vector learning

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

Non-stationary correction of optical aberrations

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

Statistical learning theory, capacity, and complexity

Bernhard Schölkopf, 2003, Complex..

Evaluating Predictive Uncertainty Challenge

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

Analysis of Contour Motions

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

A Kernel Method for the Two-Sample-Problem

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2006, NIPS.

Dynamic Alignment Kernels

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

A kernel-based causal learning algorithm

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

A Scalable Machine Learning Approach to Go

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Multi-dynamic Bayesian Networks

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Detecting the direction of causal time series

Bernhard Schölkopf, Arthur Gretton, Dominik Janzing, 2009, ICML '09.

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..

Theoretical Aspects of Cyclic Structural Causal Models

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

Blind retrospective motion correction of MR images

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

Distributed Inference in Dynamical Systems

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Causality : Objectives and Assessment

Isabelle Guyon, Bernhard Schölkopf, Dominik Janzing, 2010 .

A Primer on Kernel Methods

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

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

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

A tutorial on kernel methods for categorization

Bernhard Schölkopf, Frank Jäkel, Felix A. Wichmann, 2007, Journal of Mathematical Psychology.

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Bernhard Schölkopf, Michael Hirsch, Mehdi S. M. Sajjadi, 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

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

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

Uncovering the structure and temporal dynamics of information propagation

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

Computing functions of random variables via reproducing kernel Hilbert space representations

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

Intrinsic disentanglement: an invariance view for deep generative models

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

Counterfactuals uncover the modular structure of deep generative models pdf

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

A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

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

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

A Complexity-Distortion Approach to Joint Pattern Alignment

Thomas Hofmann, Bernhard Schölkopf, John Platt, 2007 .

The Influence of the Image Basis on Modeling and Steganalysis Performance

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

The View-Graph Approach to Visual Navigation and Spatial Memory

Bernhard Schölkopf, Heinrich H. Bülthoff, Hanspeter A. Mallot, 1997, ICANN.

Feature selection for support vector machines by means of genetic algorithm

Bernhard Schölkopf, Holger Fröhlich, Olivier Chapelle, 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

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).

Automatic particle picking using diffusion filtering and random forest classification

Bernhard Schölkopf, Michael Hirsch, Michael Habeck, 2011 .

Large scale genomic sequence SVM classifiers

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

Kernel method for percentile feature extraction

Bernhard Schölkopf, Alexander J. Smola, John Platt, 2000 .

Kernel Principal Component Analysis

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1997, ICANN.

BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease

Bernhard Schölkopf, Thomas Schultz, Tobias Schmidt-Wilcke, 2015, MLMI.

Fidelity-Weighted Learning pdf

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

Learning Disentangled Representations with Wasserstein Auto-Encoders

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

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

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

Support Vector Machines and Kernel Methods: The New Generation of Learning Machines

Nello Cristianini, Bernhard Schölkopf, B. Schölkopf, 2002, AI Mag..

Kernel PCA pattern reconstruction via approximate pre-images.

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

Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation

Bernhard Schölkopf, Moritz Diehl, Wittawat Jitkrittum, 2021, AISTATS.

Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach

Bernhard Schölkopf, Yassine Nemmour, Jia-Jie Zhu, 2021, L4DC.

Generalization and Robustness Implications in Object-Centric Learning pdf

Ole Winther, Bernhard Schölkopf, Andrea Dittadi, 2021, 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.

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..

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

Bernhard Schölkopf, Laurent Itti, Arash Mehrjou, 2021, ICML.

Resampling Base Distributions of Normalizing Flows pdf

Bernhard Schölkopf, José Miguel Hernández-Lobato, Vincent Stimper, 2021, ArXiv.

Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control pdf

Bernhard Schölkopf, Yassine Nemmour, Jia-Jie Zhu, 2021, ArXiv.

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.

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

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

A teacher-student framework to distill future trajectories

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

Recurrent Independent Mechanisms pdf

Bernhard Schölkopf, Sergey Levine, Yoshua Bengio, 2021, ICLR.

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

Bernhard Schölkopf, Cian Eastwood, Ian Mason, 2021, ArXiv.

Causal Influence Detection for Improving Efficiency in Reinforcement Learning pdf

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

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

Bernhard Schölkopf, Laurent Itti, Arash Mehrjou, 2021, AISTATS.

Boxhead: A Dataset for Learning Hierarchical Representations pdf

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

Spatially Structured Recurrent Modules

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

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

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

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain pdf

Peter V. Gehler, Bernhard Schölkopf, Matthias Bethge, 2021, ArXiv.

Towards Total Recall in Industrial Anomaly Detection pdf

Peter V. Gehler, Bernhard Schölkopf, Thomas Brox, 2021, ArXiv.

Iterative Teaching by Label Synthesis pdf

Bernhard Schölkopf, Liam Paull, Adrian Weller, 2021, NeurIPS.

Backward-Compatible Prediction Updates: A Probabilistic Approach pdf

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

Learning soft interventions in complex equilibrium systems pdf

Bernhard Schölkopf, Michel Besserve, B. Schölkopf, 2021, ArXiv.

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

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

Inference Principles and Model Selection (Dagstuhl Seminar 01301)

Joachim M. Buhmann, Bernhard Schölkopf, B. Schölkopf, 2021 .

Adversarial Robustness through the Lens of Causality pdf

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

Function Contrastive Learning of Transferable Meta-Representations

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

A theory of independent mechanisms for extrapolation in generative models

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

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

Bernhard Schölkopf, Arash Mehrjou, Stefan Bauer, 2021, ArXiv.

A prior-based approximate latent Riemannian metric pdf

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

From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing pdf

Bernhard Schölkopf, Yassine Nemmour, Jia-Jie Zhu, 2021, ArXiv.

Algorithmic Recourse: from Counterfactual Explanations to Interventions

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

Physical Derivatives: Computing policy gradients by physical forward-propagation pdf

Bernhard Schölkopf, Arash Mehrjou, Stefan Bauer, 2022, ArXiv.

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

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

Direct Advantage Estimation pdf

Bernhard Schölkopf, Alexander Neitz, Nico Gürtler, 2021, ArXiv.

Generalization bounds and learning rates for Regularized principal manifolds

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

1 Discrete Regularization

Bernhard Schölkopf, Dengyong Zhou, B. Schölkopf, 2006 .

Part-based Probabilistic Point Matching using Equivalence Constraints

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

On Transductive Regression

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Kernel Principal Component Analysis

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1997, International Conference on Artificial Neural Networks.

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

Bernhard Schölkopf, Moritz Diehl, Wittawat Jitkrittum, 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

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

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

Incorporating invariances in nonlinear Support Vector Machines

Bernhard Schölkopf, Olivier Chapelle, B. Schölkopf, 2001, NIPS 2001.

Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography

Bernhard Schölkopf, Wolfgang Rosenstiel, Michael Bensch, 2014, Journal of neural engineering.

Protein Structure and Function Fast protein classification with multiple networks

Bernhard Schölkopf, Koji Tsuda, HyunJung Shin, 2005 .

Kernel methods for dependence testing in LFP-MUA

Bernhard Schölkopf, Arthur Gretton, Nikos K. Logothetis, 2005 .

Implicit Wiener Series

Bernhard Schölkopf, Matthias O. Franz, B. Schölkopf, 2003 .

Towards neurofeedback for improving visual attention

Bernhard Schölkopf, Moritz Grosse-Wentrup, Thorsten O. Zander, 2013 .

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.

Towards a general independent subspace analysis

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Handbook of Statistical Bioinformatics

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

Extension of the nu-SVM range for classification

Bernhard Schölkopf, Jason Weston, Bernhard Schölkopf, 2003 .

Optimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches

Bernhard Schölkopf, Jason Farquhar, Nj Hill, 2006 .

Balanced Graph Matching

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Machine Learning Methods For Estimating Operator Equations

Bernhard Schölkopf, Florian Steinke, B. Schölkopf, 2006 .

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.

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

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2011 .

Margin Distribution and Soft Margin

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

Learning Dense 3 D Correspondence

Bernhard Schölkopf, Volker Blanz, Florian Steinke, 2006 .

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.

Learning Theory and Kernel Machines : 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003. Proceedings

Bernhard Schölkopf, Manfred K. Warmuth, 2003 .

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

Bernhard Schölkopf, Gilles Barthe, Amir-Hossein Karimi, 2020, ArXiv.

Accelerated Variational Dirichlet Process Mixtures

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Kernel Fisher Discriminant

Bernhard Schölkopf, Alexander J. Smola, 2001 .

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.

Bayesian Online Prediction of Change Points

Bernhard Schölkopf, Jan Peters, Sebastián Gómez-González, 2020, UAI.

Kernels and Support Vector Machines

Nello Cristianini, Bernhard Schölkopf, John Shawe-Taylor, 2002 .

Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Absence of EEG correlates of self-referential processing depth in ALS

Bernhard Schölkopf, Moritz Grosse-Wentrup, Sebastian Weichwald, 2017, PloS one.

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.

Generalization Bounds for Convex Combinations of Kernel Functions

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

Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Kernel Methods for Extracting Local Image Semantics

Bernhard Schölkopf, John Platt, B Bradshaw, 2001 .

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.

Theory and Dynamics of Perceptual Bistability

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

Visual saliency re-visited: Center-surround patterns emerge as optimal predictors for human fixation targets

Bernhard Schölkopf, Bernhard Schölkopf, Felix A. Wichmann, 2010 .

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

Bernhard Schölkopf, David Corfield, Vladimir Vapnik, 2009 .

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm

Bernhard Schölkopf, John Platt, Thomas Hofmann, 2007 .

A few extreme events dominate global interannual variability in gross primary production

Bernhard Schölkopf, Markus Reichstein, Anja Rammig, 2014 .

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.