← Authors

Klaus-Robert Müller

57 papers · 2002–2025 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (37) 🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (10) 🌈 Renaissance Researcher (8) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (23) 🏠 Conference Loyalist (25) 🌟 Keyword Trendsetter Combo (8) 👑 Domain Dominant (10) 🤝 Dynamic Duo (13) 🌱 Topic Pioneer 🏆 Keyword Champion 🏆 Grand Slam 🔬 Deep Specialist (13) 🗃️ Keyword Collector (150) 📈 Trend Setter 🔥 Unstoppable (10) 🚀 Conference Pioneer 💎 Century Club (57) ⚡ Prolific Year (6)

Conferences

NIPS (25) JMLR (17) ICML (4) AISTATS (3) CVPR (2) ICLR (2) AAAI (1) ACL (1) IJCAI (1) MICCAI (1)

Top co-authors

Grégoire Montavon (13) Wojciech Samek (7) Motoaki Kawanabe (6) Shinichi Nakajima (5) Mikio L. Braun (5) Andreas Ziehe (4) Sebastian Lapuschkin (4) Benjamin Blankertz (4) Alexander Binder (4) Pieter-jan Kindermans (4)

Research topics

Statistics (2)

Keywords

brain-computer interface (6) representation learning (5) layer-wise relevance propagation (5) signal processing (4) dimensionality reduction (4) neural network (4) model selection (4) feature extraction (4) kernel methods (4) transfer learning (3) common spatial patterns (3) feature attribution (3) support vector machine (3) electroencephalography (3) motor imagery (3) quantum chemistry (3) adversarial robustness (2) feature learning (2) time series (2) explainable ai (2)

Papers

MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification MICCAI 2025
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology NIPS 2024
MambaLRP: Explaining Selective State Space Sequence Models NIPS 2024
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations CVPR 2023
Physics-Informed Bayesian Optimization of Variational Quantum Circuits NIPS 2023
XAI for Transformers: Better Explanations through Conservative Propagation ICML 2022
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems NIPS 2022
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing ICML 2022
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities NIPS 2021
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging NIPS 2021
Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance AAAI 2020
Fairwashing explanations with off-manifold detergent ICML 2020
Deep Semi-Supervised Anomaly Detection ICLR 2020
iNNvestigate Neural Networks! JMLR 2019
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs AISTATS 2019
Explanations can be manipulated and geometry is to blame NIPS 2019
Evaluating Recurrent Neural Network Explanations ACL 2019
Learning how to explain neural networks: PatternNet and PatternAttribution ICLR 2018
Curly: An AI-based Curling Robot Successfully Competing in the Olympic Discipline of Curling IJCAI 2018
Minimizing Trust Leaks for Robust Sybil Detection ICML 2017
An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels JMLR 2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions NIPS 2017
An Empirical Study on The Properties of Random Bases for Kernel Methods NIPS 2017
Wasserstein Training of Restricted Boltzmann Machines NIPS 2016
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks CVPR 2016
The LRP Toolbox for Artificial Neural Networks JMLR 2016
Covariance shrinkage for autocorrelated data NIPS 2014
Robust Spatial Filtering with Beta Divergence NIPS 2013
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures NIPS 2013
Learning Invariant Representations of Molecules for Atomization Energy Prediction NIPS 2012
Algebraic Geometric Comparison of Probability Distributions JMLR 2012
Regression for sets of polynomial equations AISTATS 2012
Deep Boltzmann Machines as Feed-Forward Hierarchies AISTATS 2012
Kernel Analysis of Deep Networks JMLR 2011
The Stationary Subspace Analysis Toolbox JMLR 2011
How to Explain Individual Classification Decisions JMLR 2010
Layer-wise analysis of deep networks with Gaussian kernels NIPS 2010
Approximate Tree Kernels JMLR 2010
Subject independent EEG-based BCI decoding NIPS 2009
Efficient and Accurate Lp-Norm Multiple Kernel Learning NIPS 2009
Playing Pinball with non-invasive BCI NIPS 2008
On Relevant Dimensions in Kernel Feature Spaces JMLR 2008
Estimating vector fields using sparse basis field expansions NIPS 2008
Covariate Shift Adaptation by Importance Weighted Cross Validation JMLR 2007
The Need for Open Source Software in Machine Learning JMLR 2007
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing NIPS 2007
Heterogeneous Component Analysis NIPS 2007
Denoising and Dimension Reduction in Feature Space NIPS 2006
In Search of Non-Gaussian Components of a High-Dimensional Distribution JMLR 2006
Incremental Support Vector Learning: Analysis, Implementation and Applications JMLR 2006
Logistic Regression for Single Trial EEG Classification NIPS 2006
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach NIPS 2006
Inducing Metric Violations in Human Similarity Judgements NIPS 2006
A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation JMLR 2004
Feature Discovery in Non-Metric Pairwise Data JMLR 2004
Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation JMLR 2003
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces JMLR 2002