Bernhard Schölkopf

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Bernhard Schölkopf

Computer Science

#371

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Machine Learning

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Bernhard Schölkopf

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Bernhard Schölkopf's Degrees

PhD Computer Science Technical University of Berlin

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Why Is Bernhard Schölkopf Influential?

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According to Wikipedia, Bernhard Schölkopf is a German computer scientist known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is also an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin, and chairman of the European Laboratory for Learning and Intelligent Systems .

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Bernhard Schölkopf's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

A tutorial on support vector regression (2004) (10276)
Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998) (8278)
Advances in kernel methods: support vector learning (1999) (5368)
Estimating the Support of a High-Dimensional Distribution (2001) (5079)
Learning with Local and Global Consistency (2003) (4163)
An introduction to kernel-based learning algorithms (2001) (3641)
A Kernel Two-Sample Test (2012) (3479)
Learning with Kernels: support vector machines, regularization, optimization, and beyond (2001) (3425)
Fisher discriminant analysis with kernels (1999) (3017)
New Support Vector Algorithms (2000) (2761)
A gene expression map of Arabidopsis thaliana development (2005) (2507)
Kernel Principal Component Analysis (1997) (2323)
Learning with kernels (2001) (2226)
Support Vector Method for Novelty Detection (1999) (1861)
A Kernel Method for the Two-Sample-Problem (2006) (1785)
A Generalized Representer Theorem (2001) (1648)
Correcting Sample Selection Bias by Unlabeled Data (2006) (1551)
Kernel methods in machine learning (2007) (1434)
Large Scale Multiple Kernel Learning (2006) (1404)
Measuring Statistical Dependence with Hilbert-Schmidt Norms (2005) (1392)
Comparing support vector machines with Gaussian kernels to radial basis function classifiers (1997) (1382)
Input space versus feature space in kernel-based methods (1999) (1265)
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006) (1132)
Learning with Hypergraphs: Clustering, Classification, and Embedding (2006) (1098)
Kernel PCA and De-Noising in Feature Spaces (1998) (1062)
Elements of Causal Inference: Foundations and Learning Algorithms (2017) (1058)
Predicting Time Series with Support Vector Machines (1997) (1023)
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018) (981)
Kernel Methods in Computational Biology (2003) (963)
Greedy Layer-Wise Training of Deep Networks (2007) (908)
Use of the Zero-Norm with Linear Models and Kernel Methods (2003) (859)
A Hilbert Space Embedding for Distributions (2007) (834)
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2016) (830)
Nonlinear causal discovery with additive noise models (2008) (771)
Ranking on Data Manifolds (2003) (760)
Sparse Greedy Matrix Approximation for Machine Learning (2000) (745)
Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana (2007) (744)
Domain Generalization via Invariant Feature Representation (2013) (740)
A Kernel Statistical Test of Independence (2007) (715)
Support Vector Machines and Kernels for Computational Biology (2008) (690)
The connection between regularization operators and support vector kernels (1998) (690)
Extracting Support Data for a Given Task (1995) (667)
Support vector learning (1997) (634)
Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] (2009) (634)
Hilbert Space Embeddings and Metrics on Probability Measures (2009) (623)
The Kernel Trick for Distances (2000) (618)
Training Invariant Support Vector Machines (2002) (615)
A kernel view of the dimensionality reduction of manifolds (2004) (603)
Uncovering the Temporal Dynamics of Diffusion Networks (2011) (569)
Covariate Shift by Kernel Mean Matching (2009) (524)
Kernel Measures of Conditional Dependence (2007) (522)
Cluster Kernels for Semi-Supervised Learning (2002) (521)
Support vector channel selection in BCI (2004) (520)
Domain Adaptation under Target and Conditional Shift (2013) (506)
Toward Causal Representation Learning (2021) (501)
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016) (495)
MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration (2008) (488)
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites (2000) (477)
Introduction to Semi-Supervised Learning (2006) (476)
Avoiding Discrimination through Causal Reasoning (2017) (470)
Kernel-based Conditional Independence Test and Application in Causal Discovery (2011) (455)
Probabilities for SV Machines (2000) (451)
Learning from labeled and unlabeled data on a directed graph (2005) (447)
On causal and anticausal learning (2012) (443)
Learning to Deblur (2014) (441)
Predicting Structured Data (2007) (441)
Improving the Accuracy and Speed of Support Vector Machines (1996) (425)
Causal discovery with continuous additive noise models (2013) (384)
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014) (383)
Inferring causation from time series in Earth system sciences (2019) (371)
Unifying distillation and privileged information (2015) (371)
Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database (2012) (369)
Prior Knowledge in Support Vector Kernels (1997) (360)
Feature selection for support vector machines by means of genetic algorithm (2003) (356)
A Primer on Kernel Methods (2004) (354)
Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques (2009) (349)
Kernel Methods for Measuring Independence (2005) (345)
Map-Reduce for Machine Learning on Multicore (2007) (335)
On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion (1998) (333)
Incorporating Invariances in Support Vector Learning Machines (1996) (331)
TrueSkill™: A Bayesian Skill Rating System (2007) (328)
Fast removal of non-uniform camera shake (2011) (316)
Iterative kernel principal component analysis for image modeling (2005) (309)
Transfer Learning in Brain-Computer Interfaces (2015) (300)
Domain Adaptation with Conditional Transferable Components (2016) (290)
Causality for Machine Learning (2019) (286)
Structure and dynamics of information pathways in online media (2012) (279)
Modeling Human Motion Using Binary Latent Variables (2007) (278)
A Machine Learning Approach for Non-blind Image Deconvolution (2013) (277)
Using support vector machines for time series prediction (1999) (272)
A Local Learning Approach for Clustering (2006) (265)
Support Vector Machine Applications in Computational Biology (2004) (260)
Where did I take that snapshot? Scene-based homing by image matching (1998) (258)
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models (1996) (256)
A Regularization Framework for Learning from Graph Data (2004) (255)
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification (2002) (254)
Information-geometric approach to inferring causal directions (2012) (254)
Fast protein classification with multiple networks (2005) (247)
Semi-Supervised Learning (Adaptive Computation and Machine Learning) (2006) (245)
Invariant Models for Causal Transfer Learning (2015) (242)
Automatic Image Colorization Via Multimodal Predictions (2008) (238)
Efficient filter flow for space-variant multiframe blind deconvolution (2010) (236)
Causal Inference Using the Algorithmic Markov Condition (2008) (234)
Recurrent Independent Mechanisms (2019) (231)
Center-surround patterns emerge as optimal predictors for human saccade targets. (2009) (226)
Dynamic Alignment Kernels (2000) (224)
Improving the accuracy and speed of support vector learning machines (1997) (223)
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces (2003) (221)
A Nonparametric Approach to Bottom-Up Visual Saliency (2006) (219)
The Need for Open Source Software in Machine Learning (2007) (219)
Shrinking the Tube: A New Support Vector Regression Algorithm (1998) (218)
Computationally efficient face detection (2001) (218)
Invariant Feature Extraction and Classification in Kernel Spaces (1999) (218)
Multi-Instance Multi-Label Learning with Application to Scene Classification (2007) (217)
An Introduction to Support Vector Machines (2003) (217)
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines (2002) (214)
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise (2001) (214)
Learning View Graphs for Robot Navigation (1997) (212)
Semi-supervised Learning on Directed Graphs (2004) (209)
Statistical Learning Theory: Models, Concepts, and Results (2008) (206)
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators (2001) (200)
Sampling Techniques for Kernel Methods (2001) (200)
DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification (2016) (199)
From Variational to Deterministic Autoencoders (2019) (196)
Quantifying causal influences (2012) (194)
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions (2009) (194)
AdaGAN: Boosting Generative Models (2017) (193)
View-Based Cognitive Mapping and Path Planning (1995) (191)
Algorithmic Recourse: from Counterfactual Explanations to Interventions (2020) (190)
Kernel Dependency Estimation (2002) (185)
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance (2011) (184)
A few extreme events dominate global interannual variability in gross primary production (2014) (183)
Weakly-Supervised Disentanglement Without Compromises (2020) (182)
Learning from Distributions via Support Measure Machines (2012) (180)
Regularization on Discrete Spaces (2005) (180)
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression (2014) (180)
On integral probability metrics, φ-divergences and binary classification (2009) (178)
The Randomized Dependence Coefficient (2013) (177)
Methods Towards Invasive Human Brain Computer Interfaces (2004) (176)
On the Fairness of Disentangled Representations (2019) (173)
High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression (2007) (172)
Modeling Information Propagation with Survival Theory (2013) (172)
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis (2008) (172)
Learning to Find Pre-Images (2003) (171)
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery (2011) (170)
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation (2017) (169)
Movement templates for learning of hitting and batting (2010) (169)
Randomized Nonlinear Component Analysis (2014) (168)
Discovering Causal Signals in Images (2016) (167)
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014) (160)
Multi-Source Domain Adaptation: A Causal View (2015) (160)
Inferring deterministic causal relations (2010) (159)
Probabilistic movement modeling for intention inference in human–robot interaction (2013) (158)
Injective Hilbert Space Embeddings of Probability Measures (2008) (157)
Statistical Learning and Kernel Methods (2001) (155)
Results of the GREAT08 Challenge?: an image analysis competition for cosmological lensing: Results o (2009) (152)
MR-Based PET attenuation correction for PET/MR imaging. (2013) (151)
Transductive Classification via Local Learning Regularization (2007) (146)
Towards a Learning Theory of Cause-Effect Inference (2015) (144)
Support vector regression with automatic accuracy control. (1998) (144)
RASE: recognition of alternatively spliced exons in C.elegans (2005) (143)
Learning Theory and Kernel Machines (2003) (143)
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017) (142)
Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design (2010) (141)
Face Detection - Efficient and Rank Deficient (2004) (139)
Experimentally optimal v in support vector regression for different noise models and parameter settings (2004) (139)
Causal Inference on Discrete Data Using Additive Noise Models (2009) (137)
Predicting Structured Data (Neural Information Processing) (2007) (137)
Learning Independent Causal Mechanisms (2017) (137)
Learning from Labeled and Unlabeled Data Using Random Walks (2004) (136)
Kernel‐based tests for joint independence (2016) (136)
Regression by dependence minimization and its application to causal inference in additive noise models (2009) (136)
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake (2010) (134)
Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects (2006) (133)
Online Video Deblurring via Dynamic Temporal Blending Network (2017) (132)
Kernel PCA pattern reconstruction via approximate pre-images. (1998) (132)
Influence Maximization in Continuous Time Diffusion Networks (2012) (129)
Identifiability of Causal Graphs using Functional Models (2011) (128)
Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential (2009) (128)
Evaluating Predictive Uncertainty Challenge (2005) (128)
Uncovering the structure and temporal dynamics of information propagation (2014) (126)
A Tutorial Introduction (2001) (125)
Disentangling Factors of Variation Using Few Labels (2019) (125)
Regularized Principal Manifolds (1999) (125)
A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression (2006) (124)
Kernel machine based learning for multi-view face detection and pose estimation (2001) (124)
Asymptotically Optimal Choice of ε-Loss for Support Vector Machines (1998) (124)
Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces (2005) (123)
Towards Total Recall in Industrial Anomaly Detection (2021) (123)
Efficient Structure Learning of Markov Networks using L1-Regularization (2007) (122)
Probabilistic latent variable models for distinguishing between cause and effect (2010) (119)
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018) (116)
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra (2000) (116)
Causal influence of gamma oscillations on the sensorimotor rhythm (2011) (116)
Learning Blind Motion Deblurring (2017) (114)
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style (2021) (112)
At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana (2008) (112)
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (2014) (111)
Learning to Rank with Nonsmooth Cost Functions (2007) (110)
Mask-Specific Inpainting with Deep Neural Networks (2014) (110)
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003) (110)
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020) (109)
A Direct Method for Building Sparse Kernel Learning Algorithms (2006) (108)
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation (2007) (108)
Causal Inference on Time Series using Restricted Structural Equation Models (2013) (107)
An Auditory Paradigm for Brain-Computer Interfaces (2004) (106)
General cost functions for support vector regression. (1998) (105)
Classification on proximity data with LP-machines (1999) (104)
Semiparametric Support Vector and Linear Programming Machines (1998) (104)
A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA (2015) (104)
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020) (104)
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017) (103)
Linear programs for automatic accuracy control in regression. (1999) (102)
From Regularization Operators to Support Vector Kernels (1997) (99)
Spatio-Temporal Transformer Network for Video Restoration (2018) (99)
An improved training algorithm for kernel Fisher discriminants (2001) (98)
Causal Discovery from Heterogeneous/Nonstationary Data (2019) (98)
An Analysis of Inference with the Universum (2007) (97)
SV Estimation of a Distribution's Support (1999) (97)
A Permutation-Based Kernel Conditional Independence Test (2014) (95)
An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli (2012) (95)
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces (1998) (95)
Causal interpretation rules for encoding and decoding models in neuroimaging (2015) (94)
EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units (2011) (94)
Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer (2015) (93)
Learning Inverse Dynamics: a Comparison (2008) (92)
Support vector regression for black-box system identification (2001) (90)
Non-rigid point set registration: Coherent Point Drift (2007) (90)
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019) (88)
Accurate Splice Site Detection for Caenorhabditis elegans (2004) (87)
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning (2006) (87)
Characteristic Kernels on Groups and Semigroups (2008) (87)
Influence Estimation and Maximization in Continuous-Time Diffusion Networks (2016) (87)
Support Vector Machines for 3D Shape Processing (2005) (87)
From Ordinary Differential Equations to Structural Causal Models: the deterministic case (2013) (87)
Seeing the Arrow of Time (2014) (87)
Data scarcity, robustness and extreme multi-label classification (2019) (86)
On Causal Discovery with Cyclic Additive Noise Models (2011) (86)
Enhancing human learning via spaced repetition optimization (2019) (84)
Flexible Spatio-Temporal Networks for Video Prediction (2017) (83)
Learning explanations that are hard to vary (2020) (83)
A Short Introduction to Learning with Kernels (2002) (82)
Remote Sensing Feature Selection by Kernel Dependence Measures (2010) (81)
Non-stationary correction of optical aberrations (2011) (80)
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning (2020) (80)
Counterfactuals uncover the modular structure of deep generative models (2018) (79)
Efficient face detection by a cascaded support–vector machine expansion (2004) (78)
Regularised CSP for Sensor Selection in BCI (2006) (77)
Influence Maximization in Continuous Time Diffusion Networks (2012) (77)
A Kernel Approach for Learning from Almost Orthogonal Patterns (2002) (77)
Learning similarity measure for multi-modal 3D image registration (2009) (77)
High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces (2012) (77)
easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies[OPEN] (2016) (77)
Gaussian Process-Based Predictive Control for Periodic Error Correction (2016) (74)
Generalized Score Functions for Causal Discovery (2018) (74)
Telling cause from effect based on high-dimensional observations (2009) (74)
Causal Consistency of Structural Equation Models (2017) (73)
One-Class Support Measure Machines for Group Anomaly Detection (2013) (73)
Identifying Cause and Effect on Discrete Data using Additive Noise Models (2010) (73)
Discovering Temporal Causal Relations from Subsampled Data (2015) (72)
Contour-propagation algorithms for semi-automated reconstruction of neural processes (2008) (72)
Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis (2010) (72)
Towards brain-robot interfaces in stroke rehabilitation (2011) (71)
Kernel Methods for Implicit Surface Modeling (2004) (71)
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension (2018) (71)
Generalization Bounds and Consistency for Structured Labeling (2007) (70)
The representer theorem for Hilbert spaces: a necessary and sufficient condition (2012) (70)
A brain computer interface with online feedback based on magnetoencephalography (2005) (70)
Sparse Kernel Feature Analysis (2002) (70)
Speakers optimize information density through syntactic reduction (2007) (70)
Does Cognitive Science Need Kernels? (2009) (68)
THE POPULATION OF LONG-PERIOD TRANSITING EXOPLANETS (2016) (68)
Efficient Learning of Sparse Representations with an Energy-Based Model (2007) (68)
Discriminative k-shot learning using probabilistic models (2017) (68)
Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals (2006) (67)
Submodular Inference of Diffusion Networks from Multiple Trees (2012) (67)
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support (2006) (67)
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels (2016) (67)
Robust Ensemble Learning (2000) (67)
Blind retrospective motion correction of MR images (2012) (66)
Convolutional neural networks: a magic bullet for gravitational-wave detection? (2019) (65)
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions (2016) (65)
Inference of Cause and Effect with Unsupervised Inverse Regression (2015) (65)
A kernel-based causal learning algorithm (2007) (65)
Generalization and similarity in exemplar models of categorization: Insights from machine learning (2008) (64)
A tutorial on kernel methods for categorization (2007) (64)
Sparse multiscale gaussian process regression (2008) (64)
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance (2008) (64)
Large scale genomic sequence SVM classifiers (2005) (63)
Identifying confounders using additive noise models (2009) (63)
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (2014) (62)
Causal Inference by Choosing Graphs with Most Plausible Markov Kernels (2006) (62)
Kernel Constrained Covariance for Dependence Measurement (2005) (61)
On Estimation of Functional Causal Models (2015) (61)
Maximal margin classification for metric spaces (2005) (61)
A Compression Approach to Support Vector Model Selection (2004) (60)
Tailoring density estimation via reproducing kernel moment matching (2008) (60)
Deep Energy Estimator Networks (2018) (59)
Cause-Effect Inference by Comparing Regression Errors (2018) (58)
Modeling confounding by half-sibling regression (2016) (58)
MR-Based Attenuation Correction Methods for Improved PET Quantification in Lesions Within Bone and Susceptibility Artifact Regions (2013) (58)
Automatic 3D face reconstruction from single images or video (2008) (57)
Statistical Learning and Kernel Methods in Bioinformatics (2003) (57)
Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook (2010) (56)
Gaussian Processes and SVM: Mean Field and Leave-One-Out (2000) (56)
On Disentangled Representations Learned from Correlated Data (2020) (56)
Learning strategies in table tennis using inverse reinforcement learning (2014) (55)
Fidelity-Weighted Learning (2017) (54)
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM (2010) (53)
How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye Movements (2007) (53)
Probabilistic Modeling of Human Movements for Intention Inference (2012) (52)
Extension of the nu-SVM range for classification (2003) (52)
Causality: Objectives and Assessment (2008) (52)
Introduction to support vector learning (1999) (51)
A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs) (2013) (51)
Feature Selection for Support Vector Machines Using Genetic Algorithms (2004) (51)
Generalization Bounds via Eigenvalues of the Gram matrix (1999) (50)
Learning causality and causality-related learning: some recent progress. (2018) (50)
Learning inverse kinematics with structured prediction (2011) (49)
Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction (2011) (48)
Graph-Based Visual Saliency (2007) (48)
A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment (2020) (48)
Deconfounding Reinforcement Learning in Observational Settings (2018) (48)
Telling cause from effect in deterministic linear dynamical systems (2015) (47)
Adversarial Vulnerability of Neural Networks Increases With Input Dimension (2018) (47)
Beyond pairwise classification and clustering using hypergraphs (2005) (47)
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation (2007) (47)
Analysis of Representations for Domain Adaptation (2007) (46)
SVMs—a practical consequence of learning theory (1998) (46)
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019) (46)
Fair Decisions Despite Imperfect Predictions (2019) (46)
Nonparametric Regression between General Riemannian Manifolds (2010) (46)
Building Sparse Large Margin Classifiers (2005) (45)
Regularization Networks and Support Vector Machines (2000) (45)
Inferring latent structures via information inequalities (2014) (45)
Support Vector Machines as Probabilistic Models (2011) (45)
Adaptation and Robust Learning of Probabilistic Movement Primitives (2018) (45)
Real-time gravitational-wave science with neural posterior estimation (2021) (44)
Blind Correction of Optical Aberrations (2012) (44)
TriFinger: An Open-Source Robot for Learning Dexterity (2020) (44)
Dealing with large diagonals in kernel matrices (2003) (44)
On the Transfer of Disentangled Representations in Realistic Settings (2020) (44)
Consistency of Causal Inference under the Additive Noise Model (2013) (43)
Causal Discovery from Temporally Aggregated Time Series (2017) (43)
Detecting the direction of causal time series (2009) (43)
Voluntary brain regulation and communication with electrocorticogram signals (2008) (43)
Kernel Mean Shrinkage Estimators (2014) (43)
Kernels, regularization and differential equations (2008) (43)
On the Latent Space of Wasserstein Auto-Encoders (2018) (43)
The effect of patient positioning aids on PET quantification in PET/MR imaging (2011) (43)
Causal Discovery via Reproducing Kernel Hilbert Space Embeddings (2014) (42)
GLIDE: GPU-Based Linear Regression for Detection of Epistasis (2012) (42)
Local learning projections (2007) (42)
Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification (2014) (42)
Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach (2016) (42)
Modeling Dyadic Data with Binary Latent Factors (2007) (42)
A Kernel Approach to Comparing Distributions (2007) (42)
Stochastic Relational Models for Discriminative Link Prediction (2007) (41)
Generalization in anti-causal learning (2018) (41)
Group invariance principles for causal generative models (2017) (41)
Non-parametric estimation of integral probability metrics (2010) (41)
Artificial intelligence: Learning to see and act (2015) (40)
Support Vector methods in learning and feature extraction (1998) (39)
From Deterministic ODEs to Dynamic Structural Causal Models (2016) (39)
Real-time prediction of COVID-19 related mortality using electronic health records (2020) (39)
The Inductive Bias of Quantum Kernels (2021) (39)
Switched Latent Force Models for Movement Segmentation (2010) (38)
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020) (38)
Classification of Faces in Man and Machine (2006) (37)
Bounds on Error Expectation for SVM (2000) (37)
Incorporating Invariances in Non-Linear Support Vector Machines (2001) (37)
Similarity, Kernels, and the Triangle Inequality (2008) (37)
Object correspondence as a machine learning problem (2005) (37)
Attention modulation of auditory event-related potentials in a brain-computer interface (2004) (37)
Kernel-dependent support vector error bounds (1999) (37)
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment (2015) (35)
Independent mechanism analysis, a new concept? (2021) (35)
Linear Discriminant and Support Vector Classifiers (2000) (35)
Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference (2004) (35)
Learning to Play Table Tennis From Scratch Using Muscular Robots (2020) (35)
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds (2001) (35)
Generalized Clustering via Kernel Embeddings (2009) (34)
On the Fairness of Causal Algorithmic Recourse (2020) (34)
Causal reasoning by evaluating the complexity of conditional densities with kernel methods (2008) (34)
Entropy Numbers, Operators and Support Vector Kernels (1999) (34)
MLLE: Modified Locally Linear Embedding Using Multiple Weights (2007) (34)
Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions (2009) (34)
Joint Kernel Maps (2005) (34)
Protein functional class prediction with a combined graph (2003) (34)
Discrete Regularization (2006) (33)
Computing functions of random variables via reproducing kernel Hilbert space representations (2015) (33)
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models (2018) (33)
Nonlinear Invariant Risk Minimization: A Causal Approach (2021) (33)
Hierarchical Dirichlet Processes with Random Effects (2007) (33)
Distinguishing Cause from Effect Based on Exogeneity (2015) (32)
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution (2018) (32)
The Arrow of Time in Multivariate Time Series (2016) (32)
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model (2007) (32)
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments (2007) (32)
Towards causal generative scene models via competition of experts (2020) (32)
A brain–computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex (2014) (32)
Distilling Information Reliability and Source Trustworthiness from Digital Traces (2016) (32)
Machine Learning Applied to Perception: Decision Images for Gender Classification (2004) (31)
Sparse online model learning for robot control with support vector regression (2009) (31)
Blind multirigid retrospective motion correction of MR images (2015) (31)
Semi-Supervised Domain Adaptation with Non-Parametric Copulas (2012) (31)
Anticipatory action selection for human-robot table tennis (2017) (31)
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm (2016) (31)
Kernel Mean Estimation and Stein Effect (2013) (31)
Local Group Invariant Representations via Orbit Embeddings (2016) (31)
Incorporating invariances in nonlinear Support Vector Machines (2001) (31)
Empirical Inference (2011) (31)
DiBS: Differentiable Bayesian Structure Learning (2021) (30)
Prediction on Spike Data Using Kernel Algorithms (2003) (30)
View-based cognitive map learning by an autonomous robot (1995) (30)
Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery (2010) (30)
Concepts and Tools (2001) (30)
GACV for Support Vector Machines (2000) (30)
Learning an Interest Operator from Human Eye Movements (2006) (30)
HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization (2013) (30)
Edinburgh Research Explorer Interpolating between types and tokens by estimating power-law generators (2006) (30)
Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis (2003) (29)
Jointly learning trajectory generation and hitting point prediction in robot table tennis (2016) (29)
Efficient sparse coding algorithms (2007) (29)
Handbook of Statistical Bioinformatics (2011) (29)
Optimal Decision Making Under Strategic Behavior (2019) (29)
Implicit surface modelling as an eigenvalue problem (2005) (29)
A Review of Kernel Methods in Machine Learning (2006) (29)
End-to-End Learning for Image Burst Deblurring (2016) (29)
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations (2022) (28)
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences (2008) (28)
Graph Laplacian Regularization for Large-Scale Semidefinite Programming (2007) (28)
Tempered Adversarial Networks (2018) (27)
Towards Principled Disentanglement for Domain Generalization (2021) (27)
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning (2020) (27)
Support Vector Machines: A Practical Consequence of Learning Theory (1998) (27)
A Discussion of Semi-Supervised Learning and Transduction (2006) (27)
A Kernel for Protein Secondary Structure Prediction (2004) (27)
Implicit Wiener Series for Higher-Order Image Analysis (2004) (27)
Learning Dense 3D Correspondence (2006) (27)
Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography (2014) (27)
Kernel Hebbian Algorithm for single-frame super-resolution (1998) (26)
Differentially Private Database Release via Kernel Mean Embeddings (2017) (26)
Justifying Information-Geometric Causal Inference (2014) (26)
Geometrically Enriched Latent Spaces (2020) (26)
Learning Neural Causal Models with Active Interventions (2021) (26)
Causal Markov Condition for Submodular Information Measures (2010) (25)
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems (2019) (25)
Support Vector Machines (2005) (25)
Diffeomorphic Dimensionality Reduction (2008) (25)
Predicting motor learning performance from Electroencephalographic data (2014) (25)
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017) (25)
Kernel Methods and Support Vector Machines (2003) (25)
Disentangled State Space Representations (2019) (25)
On the Adversarial Robustness of Causal Algorithmic Recourse (2021) (24)
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration (2021) (24)
Nonparametric dynamics estimation for time periodic systems (2013) (24)
Robust Ensemble Learning for Data Mining (2000) (24)
Competitive Training of Mixtures of Independent Deep Generative Models (2018) (24)
Photorealistic Video Super Resolution (2018) (24)
Logistic Regression for Single Trial EEG Classification (2007) (23)
A Causal, Data-driven Approach to Modeling the Kepler Data (2015) (23)
Learning optimal striking points for a ping-pong playing robot (2015) (23)
Invariant Causal Representation Learning for Out-of-Distribution Generalization (2022) (23)
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots (2020) (23)
Inexact Matching String Kernels for Protein Classification (2004) (23)
Causal Transfer in Machine Learning (2015) (23)
Bayesian Kernel Methods (2002) (23)
Multiple Instance Learning for Computer Aided Diagnosis (2007) (23)
Fast and Slow Learning of Recurrent Independent Mechanisms (2021) (23)
Kernel method for percentile feature extraction (2000) (23)
Learning Disentangled Representations with Wasserstein Auto-Encoders (2018) (23)
Isotonic Conditional Random Fields and Local Sentiment Flow (2007) (23)
Using Combinatorial Optimization within Max-Product Belief Propagation (2007) (23)
Detecting non-causal artifacts in multivariate linear regression models (2018) (23)
Multidimensional Contrast Limited Adaptive Histogram Equalization (2019) (23)
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020) (22)
Discriminative Transfer Learning for General Image Restoration (2017) (22)
Detecting low-complexity unobserved causes (2011) (22)
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain (2021) (22)
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Informative Features for Model Comparison (2018) (22)
Adversarial Robustness through the Lens of Causality (2022) (22)
Adaptive Margin Support Vector Machines (2000) (22)
Prototype Classification: Insights from Machine Learning (2009) (22)
Necessary and sufficient conditions for causal feature selection in time series with latent common causes (2020) (22)
Semi-supervised Learning in Causal and Anticausal Settings (2013) (22)
Theoretical Aspects of Cyclic Structural Causal Models (2016) (22)
Generalization and Robustness Implications in Object-Centric Learning (2021) (21)
Semi-supervised interpolation in an anticausal learning scenario (2015) (21)
PG-means: learning the number of clusters in data (2007) (21)
Fragmentation of slow wave sleep after onset of complete locked-in state. (2013) (21)
A brain-robot interface for studying motor learning after stroke (2012) (21)
Multi-way set enumeration in weight tensors (2011) (21)
Assaying Out-Of-Distribution Generalization in Transfer Learning (2022) (21)
Interventional Robustness of Deep Latent Variable Models (2018) (21)
Non-monotonic Poisson Likelihood Maximization (2008) (21)
Using probabilistic movement primitives for striking movements (2016) (21)
Two-locus association mapping in subquadratic time (2011) (20)
Entropy Numbers of Linear Function Classes (2000) (20)
On Matching Pursuit and Coordinate Descent (2018) (20)
Screening Rules for Convex Problems (2016) (20)
Computing the Bayes Kernel Classifier (2000) (20)
The View-Graph Approach to Visual Navigation and Spatial Memory (1997) (20)
Causal Influence Detection for Improving Efficiency in Reinforcement Learning (2021) (20)
Learning with Hyperspherical Uniformity (2021) (19)
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020) (19)
Learning anticipation policies for robot table tennis (2011) (19)
A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation (2016) (19)
Reliable Real Time Ball Tracking for Robot Table Tennis (2019) (19)
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm (2000) (19)
Energy-Based Models (2007) (19)
GeNet: Deep Representations for Metagenomics (2019) (18)
Homing by parameterized scene matching (1997) (18)
Maximum Moment Restriction for Instrumental Variable Regression (2020) (18)
Fast Discriminative Visual Codebooks using Randomized Clustering Forests (2007) (18)
Kernel-Based Integration of Genomic Data Using Semidefinite Programming (2004) (18)
Convex Cost Functions for Support Vector Regression (1998) (17)
Semi-supervised learning, causality, and the conditional cluster assumption (2019) (17)
Learning annotated hierarchies from relational data (2007) (17)
Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS (2016) (17)
Combining a Filter Method with SVMs (2006) (17)
DeepMAsED: Evaluating the quality of metagenomic assemblies. (2020) (17)
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data (2020) (17)
Relative gradient optimization of the Jacobian term in unsupervised deep learning (2020) (17)
A 32‐channel multi‐coil setup optimized for human brain shimming at 9.4T (2020) (17)
Adversarial Extreme Multi-label Classification (2018) (17)
BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI (2017) (17)
Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis (2010) (17)
An introduction to kernel learning algorithms (2009) (17)
Causality for Machine Learning (2019) (17)
MRzero ‐ Automated discovery of MRI sequences using supervised learning (2020) (16)
Dynamic Inference with Neural Interpreters (2021) (16)
Causal Inference on Time Series using Structural Equation Models (2012) (16)
Interventions, Where and How? Experimental Design for Causal Models at Scale (2022) (16)
Submodular Inference of Diffusion Networks from Multiple Trees (2012) (16)
Beyond the Margin (2000) (16)
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders (2013) (16)
Kernel Methods for Extracting Local Image Semantics (2001) (16)
1 Making Large-scale Svm Learning Practical (1999) (16)
Analysis of cause-effect inference by comparing regression errors (2018) (16)
Causal analysis of Covid-19 Spread in Germany (2020) (16)
Bayesian Online Prediction of Change Points (2019) (15)
The effect of artifacts on dependence measurement in fMRI. (2006) (15)
v-Arc: Ensemble Learning in the Presence of Outliers (1999) (15)
A Tutorial Review of RKHS Methods in Machine Learning (2005) (15)
Active learning for misspecified generalized linear models (2007) (15)
Real Time Trajectory Prediction Using Deep Conditional Generative Models (2019) (15)
A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions (2022) (15)
Pattern Recognition (2004) (14)
Machine Learning Methods for Automatic Image Colorization (2011) (14)
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data (2007) (14)
Quantum Mean Embedding of Probability Distributions (2019) (14)
A theory of independent mechanisms for extrapolation in generative models (2020) (14)
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints (2007) (14)
Testing Goodness of Fit of Conditional Density Models with Kernels (2020) (14)
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs (2019) (14)
Learning Kernel Tests Without Data Splitting (2020) (14)
Gene Expression Analysis: Joint Feature Selection and Classifier Design (2004) (14)
Causal and anti-causal learning in pattern recognition for neuroimaging (2015) (14)
Case series: Slowing alpha rhythm in late-stage ALS patients (2018) (13)
Convex Repeated Games and Fenchel Duality (2007) (13)
Blind Motion Deblurring Using Image Statistics (2007) (13)
A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis (2015) (13)
Prediction on a Graph with a Perceptron (2007) (13)
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise (2018) (13)
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation (2021) (13)
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators (2013) (13)
Learning Nonparametric Models for Probabilistic Imitation (2007) (13)
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery (2010) (13)
Training Support Vector Machines with Multiple Equality Constraints (2005) (13)
Annealed Generative Adversarial Networks (2017) (13)
Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach (2007) (13)
Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test (2018) (13)
Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA (2007) (13)
KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug Design-Binding to Thrombin (2001) (13)
Score matching enables causal discovery of nonlinear additive noise models (2022) (13)
Quantization Functionals and Regularized Principal Manifolds (1998) (13)
Control of Musculoskeletal Systems Using Learned Dynamics Models (2018) (13)
Markerless 3D Face Tracking (2009) (12)
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression (2021) (12)
Clustering appearance and shape by learning jigsaws (2007) (12)
S2RMs: Spatially Structured Recurrent Modules (2020) (12)
Manifold‐valued Thin‐Plate Splines with Applications in Computer Graphics (2008) (12)
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers (2022) (12)
Consistent Kernel Mean Estimation for Functions of Random Variables (2016) (12)
Kernel Mean Estimation via Spectral Filtering (2014) (12)
A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller (2011) (12)
Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis (2015) (12)
Feature Selection for Troubleshooting in Complex Assembly Lines (2007) (12)
Some kernels for structured data (2001) (12)
Kernel methods in medical imaging (2015) (12)
A Commentary on the Unsupervised Learning of Disentangled Representations (2020) (12)
Large Scale Hidden Semi-Markov SVMs (2007) (12)
Bridging the Gap to Real-World Object-Centric Learning (2022) (11)
Identification of the Default Mode Network with electroencephalography (2015) (11)
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction (2021) (11)
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks (2019) (11)
Structural Autoencoders Improve Representations for Generation and Transfer (2020) (11)
Implicit estimation of wiener series (2004) (11)
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem (2020) (11)
A New Method for Constructing Artificial Neural Networks (1995) (11)
Kernel Fisher Discriminant (2001) (11)
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data (2022) (11)
Predicting Infectiousness for Proactive Contact Tracing (2020) (11)
Removing noise from astronomical images using a pixel-specific noise model (2011) (11)
Efficient Approximations for Support Vector Machines in Object Detection (2004) (11)
Multivariate Regression via Stiefel Manifold Constraints (2004) (11)
Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study (2021) (11)
Blind source separation for over-determined delayed mixtures (2007) (10)
Kernel Distributionally Robust Optimization (2020) (10)
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders (2018) (10)
Towards Machine Learning of Motor Skills (2007) (10)
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection (2016) (10)
Advances in Neural Information Processing Systems 21 (NIPS 2008) (2008) (10)
easyGWAS: An integrated interspecies platform for performing genome-wide association studies (2012) (10)
Fast Kernels for String and Tree Matching (2004) (10)
From Statistical to Causal Learning (2022) (10)
Kernel Stein Tests for Multiple Model Comparison (2019) (10)
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions (2016) (10)
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions (2006) (10)
Experimentally optimal nu in support vector regression for different noise models and parameter settings (2005) (10)
Removing systematic errors for exoplanet search via latent causes (2015) (10)
Data Integration for Classification Problems Employing Gaussian Process Priors (2007) (10)
Estimating Causal Effects by Bounding Confounding (2014) (10)
Learning Optimal EEG Features Across Time, Frequency and Space (2006) (9)
Temporal dynamics of information content carried by neurons in the primary visual cortex (2007) (9)
A General Regression Framework for Learning String-to-String Mappings (2007) (9)
A Blind Deconvolution Approach for Improving the Resolution of Cryo-EM Density Maps (2011) (9)
Popper, Falsification and the VC-dimension (2005) (9)
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games (2007) (9)
On the design of consequential ranking algorithms (2020) (9)
Selecting causal brain features with a single conditional independence test per feature (2019) (9)
Max-margin classification of incomplete data (2007) (9)
Semi-supervised Kernel Regression Using Whitened Function Classes (2004) (9)
Improving alpha matting and motion blurred foreground estimation (2013) (9)
Generalized Support Vector Machines (2000) (9)
Discovery and Visualization of Nonstationary Causal Models (2015) (9)
Natural Regularization from Generative Models (2000) (9)
Resampling Base Distributions of Normalizing Flows (2021) (9)
Diffusion-Based Representation Learning (2021) (9)
On Probabilistic Conditional Independence Structures (2005) (9)
Flow Annealed Importance Sampling Bootstrap (2022) (9)
Support Vector Machines and Kernel Algorithms. (2005) (9)
Probabilistic Active Learning of Functions in Structural Causal Models (2017) (8)
Spatial statistics, image analysis and percolation theory (2011) (8)
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time (2007) (8)
Task-induced frequency modulation features for brain-computer interfacing (2017) (8)
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States (2021) (8)
Group equivariant neural posterior estimation (2021) (8)
Absence of EEG correlates of self-referential processing depth in ALS (2017) (8)
The Role of Pretrained Representations for the OOD Generalization of RL Agents (2022) (8)
Optimizing Human Learning (2017) (8)
CausalAdv: Adversarial Robustness through the Lens of Causality (2021) (8)
Unsupervised Object Learning via Common Fate (2021) (8)
Closing the Sensorimotor Loop: Haptic Feedback Helps Decoding of Motor Imagery (2011) (8)
The unpopular Package: A Data-driven Approach to Detrending TESS Full-frame Image Light Curves (2021) (8)
Entropy Numbers for Convex Combinations and MLPs (2000) (8)
The Moon Tilt Illusion (1998) (8)
Retrospective Motion Correction of Magnitude-Input MR Images (2015) (7)
A machine learning route between band mapping and band structure (2020) (7)
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm (2007) (7)
Causal Inference Through the Structural Causal Marginal Problem (2022) (7)
Semi-Supervised Learning through Principal Directions Estimation (2003) (7)
Kernel Mean Matching for Content Addressability of GANs (2019) (7)
Generalization bounds and learning rates for Regularized principal manifolds (1998) (7)
Towards a Learning Theory of Causation (2015) (7)
Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic 18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach (2017) (7)
Efficient Algorithms for Max-Margin Structured Classification (2007) (7)
Large Margin Bank Boundaries for Ordinal Regression (2000) (7)
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks (2007) (7)
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields (2007) (7)
Backward-Compatible Prediction Updates: A Probabilistic Approach (2021) (7)
Training Conditional Random Fields for Maximum Labelwise Accuracy (2007) (7)
A Witness Two-Sample Test (2021) (7)
Robust Learning via Cause-Effect Models (2011) (7)
Listening to bluetooth beacons for epidemic risk mitigation (2020) (7)
Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis (2004) (7)
Multi-way set enumeration in real-valued tensors (2009) (7)
A Robot Cluster for Reproducible Research in Dexterous Manipulation (2021) (7)
Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions (2007) (7)
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes. (2019) (7)
A recipe for optimizing a time-histogram (2007) (7)
Automatic estimation of modulation transfer functions (2018) (7)
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing (2020) (7)
Decoding index finger position from EEG using random forests (2014) (6)
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier (2007) (6)
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks (2022) (6)
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 (2003) (6)
Semi-Supervised Domain Adaptation with Copulas (2012) (6)
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control (2020) (6)
Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons (2007) (6)
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment (2022) (6)
Single-class Support Vector Machines (1999) (6)
LanczosNet : Multi-Scale Deep Graph Convolutional Networks (2018) (6)
Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina (2004) (6)
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification (2016) (6)
Logical Fallacy Detection (2022) (6)
Competitive Training of Mixtures of Independent Deep Generative Models (2018) (6)
Automated Hierarchy Discovery for Planning in Partially Observable Environments (2007) (6)
Implicit Volterra and Wiener Series for Higher-Order Image Analysis (2006) (6)
Function Classes for Identifiable Nonlinear Independent Component Analysis (2022) (6)
Beta Power May Meditate the Effect of Gamma-TACS on Motor Performance (2019) (6)
Self-Calibration of Optical Lenses (2015) (6)
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis (2022) (6)
Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach (2005) (6)
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming (2019) (6)
Function Contrastive Learning of Transferable Meta-Representations (2020) (6)
MRI zero: Fully automated invention of MRI sequences using supervised learning (2019) (6)
Probabilistic Modeling of Human Dynamics for Intention Inference (2012) (6)
Elements of Statistical Learning Theory (2001) (6)
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments (2007) (6)
Spread‐spectrum magnetic resonance imaging (2019) (6)
Reinforcement Learning for Motor Primitives (2009) (6)
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing (2007) (6)
How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data (2013) (6)
1 A primer on kernel methods (2004) (6)
Generalization Bounds for Convex Combinations of Kernel Functions (1998) (6)
Probable Domain Generalization via Quantile Risk Minimization (2022) (5)
Introduction to kernel methods (2003) (5)
Ja n 20 09 A Note on Integral Probability Metrics and φ-divergences (2009) (5)
Support Vectors and Statistical Mechanics (2000) (5)
A pixel-level model for event discovery in time-domain imaging (2017) (5)
Neural Lyapunov Redesign (2020) (5)
On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example (2013) (5)
Minimum Information Exchange in Distributed Systems (2018) (5)
Experimental and causal view on information integration in autonomous agents (2016) (5)
Structure by Architecture: Disentangled Representations without Regularization (2020) (5)
RKHS Representation of Measures Applied to Homogeneity, Independence, and Fourier Optics (2008) (5)
Towards neurofeedback for improving visual attention (2013) (5)
A note on integral probability metrics and $\phi$-divergences (2009) (5)
On Matching Pursuit and Coordinate Descent (2018) (5)
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models (2020) (5)
Perceptual Video Super Resolution with Enhanced Temporal Consistency (2018) (5)
Implicit Wiener series analysis of epileptic seizure recordings (2009) (5)
Iterative Teaching by Label Synthesis (2021) (5)
Action-Sufficient State Representation Learning for Control with Structural Constraints (2021) (5)
Structural causal models for macro-variables in time-series (2018) (5)
Deep Lyapunov Function: Automatic Stability Analysis for Dynamical Systems (2019) (5)
Causal models for debugging and control in cloud computing (2016) (5)
On the Identifiability and Estimation of Causal Location-Scale Noise Models (2022) (5)
Towards Causal Algorithmic Recourse (2020) (5)
Time-Dependent Demixing of Task-Relevant EEG Signals (2006) (5)
Bayesian Online Detection and Prediction of Change Points (2019) (5)
Depth Estimation Through a Generative Model of Light Field Synthesis (2016) (5)
Personalized brain-computer interface models for motor rehabilitation (2017) (5)
Intrinsic disentanglement: an invariance view for deep generative models (2018) (5)
Clustering Under Prior Knowledge with Application to Image Segmentation (2007) (5)
Boxhead: A Dataset for Learning Hierarchical Representations (2021) (4)
Robotic Grasping of Novel Objects (2007) (4)
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference (2022) (4)
Nonstationary GANs: Analysis as Nonautonomous Dynamical Systems (2018) (4)
Diabetes detection from whole-body magnetic resonance imaging using deep learning. (2021) (4)
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning (2021) (4)
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data (2016) (4)
A Primer on KernelMethods (2004) (4)
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images (2021) (4)
Learning on Graph with Laplacian Regularization (2007) (4)
Analysis of Benchmarks (2006) (4)
Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data (2019) (4)
Machine Learning Methods For Estimating Operator Equations (2006) (4)
Amortized Inference for Causal Structure Learning (2022) (4)
Structural Causal 3D Reconstruction (2022) (4)
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee (2022) (4)
Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm (2004) (4)
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A Causal Perspective on Domain Adaptation (2015) (4)
Consequential Ranking Algorithms and Long-term Welfare (2019) (4)
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware (2020) (4)
Improving Consequential Decision Making under Imperfect Predictions (2019) (4)
B0 matrix shim array design-optimization of the position, geometry and the number of segments of individual coil elements (2016) (4)
Causal Feature Selection via Orthogonal Search (2020) (4)
Learning Structural Equation Models for fMRI (2007) (4)
Discrete Key-Value Bottleneck (2022) (4)
Learning Dynamical Systems using Local Stability Priors (2020) (4)
An Oracle Inequality for Clipped Regularized Risk Minimizers (2007) (4)
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees (2019) (4)
Sparse Representation for Signal Classification (2007) (4)
Kernel Methods for Detecting the Direction of Time Series (2008) (4)
Theory and Dynamics of Perceptual Bistability (2007) (4)
Adversarially Robust Kernel Smoothing (2021) (3)
Crackovid: Optimizing Group Testing (2020) (3)
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2004 IEEE International Workshop on Biomedical Circuits & Systems ATTENTIONAL MODULATION OF AUDITORY EVENT-RELATED POTENTIALS IN A BRAIN-COMPUTER INTERFACE (2004) (0)
Relational Learning with Gaussian Processes (2007) (0)
A General Purpose Neural Architecture for Geospatial Systems (2022) (0)
The Problem of Representative Choice (2005) (0)
Minimal Logical Constraint Covering Sets (2006) (0)
Structured Prediction Using Probabilistic Models (2007) (0)
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces (2019) (0)
Accepted for NIPS ’ 99 SV Estimation of a Distribution ’ s Support (1999) (0)
Learning Random Feature Dynamics for Uncertainty Quantification (2022) (0)
Abstract: BCI-based robot rehabilitation framework for stroke patients (2010) (0)
Multiple timescales and uncertainty in motor adaptation (2007) (0)
An Approach to Bounded Rationality (2007) (0)
Simultaneous Implicit Surface Reconstruction and Meshing (2008) (0)
Instability , Computational Efficiency and Statistical Accuracy Instability , Computational Efficiency and Statistical Accuracy (2022) (0)
On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset (2019) (0)
Explorer Mixture Regression for Covariate Shift (2006) (0)
Predicting spike times from subthreshold dynamics of a neuron (2007) (0)
Large-Scale Sparsified Manifold Regularization (2007) (0)
Inference Principles and Model Selection (Dagstuhl Seminar 01301) (2021) (0)
Chapter 20 Combining a Filter Method with SVMs (0)
Unsupervised identification of neural events in local field potentials (2014) (0)
Iterative Model-Fitting and Local Controller Optimization - Towards a Better Understanding of Convergence Properties (2018) (0)
Face Detection Benchmark Database Data Set Location Description (2007) (0)
Structural Imsets: Fundamentals (2005) (0)
Human Classification Behaviour Revisited by Machine Learning (2004) (0)
Generate Semantically Similar Images with Kernel Mean Matching (2019) (0)
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory (2018) (0)
The Neurodynamics of Belief Propagation on Binary Markov Random Fields (2007) (0)
Measuring Conditional Dependence with Kernels (2007) (0)
Approximate Correspondences in High Dimensions (2007) (0)
Inferring causation from time series in Earth system sciences (2019) (0)
MYND: Neuroscience at Home (2020) (0)
Modeling Structure via Graphical Models (2007) (0)
Bayesian Policy Gradient Algorithms (2007) (0)
Scalable Discriminative Learning for Natural Language Parsing and Translation (2007) (0)
Coordination via predictive assistants: time series algorithms and game-theoretic analysis (2018) (0)
Kernel Conditional Graphical Models (2007) (0)
Uncertainty, phase and oscillatory hippocampal recall (2007) (0)
Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions (2006) (0)
Causal Consistency of Structural Equation Models (2017) (0)
A Generative Model for Quasar Spectra (2022) (0)
Analysis of Cause-Effect Inference via Regression Errors (2018) (0)
Dirichlet-Enhanced Spam Filtering based on Biased Samples (2007) (0)
An Inventory of Common Sequence Polymorphisms for Arabidopsis (2006) (0)
Data Fusion with Kernel Methods (2004) (0)
Temporal Coding using the Response Properties of Spiking Neurons (2007) (0)
Research focus Causal inference from time series and quasi-experimental data, causal and probabilistic modeling of large-scale computer systems (2015) (0)
Spread-spectrum MRI: acceleration of image acquisition using locally modulated magnetic fields (2019) (0)
Preface to the ACM TIST Special Issue on Causal Discovery and Inference (2016) (0)
Detecting Humans via Their Pose (2007) (0)
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective" (2022) (0)
A Guided Task for Cognitive brain-Computer Interfaces (2017) (0)
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets (2007) (0)
CHALLENGE HANDBOOK (2020) (0)

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