Bernhard Schölkopf

377 publications

38 venues

H Index 92

Affiliation

Max Planck Institute for Intelligent Systems, T bingen, Germany
Gottfried Wilhelm Leibniz Prize

Links

Name Venue Year citations
Provable Privacy with Non-Private Pre-Processing. ICML 2024 0
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? ICML 2024 0
Detecting and Identifying Selection Structure in Sequential Data. ICML 2024 0
Geometry-Aware Instrumental Variable Regression. ICML 2024 0
Robustness of Nonlinear Representation Learning. ICML 2024 0
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. ICRA 2024 0
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals. ACL 2024 0
Moûsai: Efficient Text-to-Music Diffusion Models. ACL 2024 0
Causal Modeling with Stationary Diffusions. AISTATS 2024 0
GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs. CVPR 2024 0
Skill or Luck? Return Decomposition via Advantage Functions. ICLR 2024 0
Can Large Language Models Infer Causation from Correlation? ICLR 2024 0
Identifying Policy Gradient Subspaces. ICLR 2024 0
Out-of-Variable Generalisation for Discriminative Models. ICLR 2024 0
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks. ICLR 2024 0
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. ICLR 2024 0
Ghost on the Shell: An Expressive Representation of General 3D Shapes. ICLR 2024 0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR 2024 0
The Hessian perspective into the Nature of Convolutional Neural Networks. ICML 2023 0
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023 0
Diffusion Based Representation Learning. ICML 2023 0
On the Relationship Between Explanation and Prediction: A Causal View. ICML 2023 0
Provably Learning Object-Centric Representations. ICML 2023 0
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML 2023 0
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. ICML 2023 0
Discrete Key-Value Bottleneck. ICML 2023 0
On Data Manifolds Entailed by Structural Causal Models. ICML 2023 0
Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML 2023 0
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. ICRA 2023 0
Causal effect estimation from observational and interventional data through matrix weighted linear estimators. UAI 2023 0
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models. ACL 2023 0
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. AISTATS 2023 0
Iterative Teaching by Data Hallucination. AISTATS 2023 0
Controlling Text-to-Image Diffusion by Orthogonal Finetuning. NIPS/NeurIPS 2023 0
Leveraging sparse and shared feature activations for disentangled representation learning. NIPS/NeurIPS 2023 0
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NIPS/NeurIPS 2023 0
Flow Matching for Scalable Simulation-Based Inference. NIPS/NeurIPS 2023 0
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NIPS/NeurIPS 2023 0
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. NIPS/NeurIPS 2023 0
CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models. NIPS/NeurIPS 2023 0
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. NIPS/NeurIPS 2023 0
Causal Component Analysis. NIPS/NeurIPS 2023 0
A Measure-Theoretic Axiomatisation of Causality. NIPS/NeurIPS 2023 0
SE(3) Equivariant Augmented Coupling Flows. NIPS/NeurIPS 2023 0
Flow Annealed Importance Sampling Bootstrap. ICLR 2023 0
Bridging the Gap to Real-World Object-Centric Learning. ICLR 2023 0
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. ICLR 2023 0
Structure by Architecture: Structured Representations without Regularization. ICLR 2023 0
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. ICLR 2023 0
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. ICLR 2023 0
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis. IROS 2023 0
Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning. RSS 2023 0
Pairwise Similarity Learning is SimPLE. ICCV 2023 0
Reinforcement learning with model-based feedforward inputs for robotic table tennis. Autonomous Robots 2023 0
Metrizing Weak Convergence with Maximum Mean Discrepancies. JMLR 2023 0
The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR 2022 7
Differentially Private Language Models for Secure Data Sharing. EMNLP 2022 3
Adversarial Robustness Through the Lens of Causality. ICLR 2022 20
Causal Inference Through the Structural Causal Marginal Problem. ICML 2022 5
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR 2022 8
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML 2022 0
A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles. RSS 2022 0
Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR 2022 18
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML 2022 10
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR 2022 2
Structural Causal 3D Reconstruction. ECCV 2022 2
On the Fairness of Causal Algorithmic Recourse. AAAI 2022 0
Generalization and Robustness Implications in Object-Centric Learning. ICML 2022 0
On the Adversarial Robustness of Causal Algorithmic Recourse. ICML 2022 0
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022 0
Learning soft interventions in complex equilibrium systems. UAI 2022 0
Adversarially Robust Kernel Smoothing. AISTATS 2022 0
Resampling Base Distributions of Normalizing Flows. AISTATS 2022 0
A Witness Two-Sample Test. AISTATS 2022 0
A prior-based approximate latent Riemannian metric. AISTATS 2022 0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. AISTATS 2022 0
Towards Total Recall in Industrial Anomaly Detection. CVPR 2022 0
Towards Principled Disentanglement for Domain Generalization. CVPR 2022 0
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NIPS/NeurIPS 2022 0
Probable Domain Generalization via Quantile Risk Minimization. NIPS/NeurIPS 2022 0
Amortized Inference for Causal Structure Learning. NIPS/NeurIPS 2022 0
AutoML Two-Sample Test. NIPS/NeurIPS 2022 0
Function Classes for Identifiable Nonlinear Independent Component Analysis. NIPS/NeurIPS 2022 0
Assaying Out-Of-Distribution Generalization in Transfer Learning. NIPS/NeurIPS 2022 0
Exploring the Latent Space of Autoencoders with Interventional Assays. NIPS/NeurIPS 2022 0
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization. NIPS/NeurIPS 2022 0
Neural Attentive Circuits. NIPS/NeurIPS 2022 0
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. NIPS/NeurIPS 2022 0
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. NIPS/NeurIPS 2022 0
Direct Advantage Estimation. NIPS/NeurIPS 2022 0
Interventions, Where and How? Experimental Design for Causal Models at Scale. NIPS/NeurIPS 2022 0
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 0
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. ICLR 2022 0
Group equivariant neural posterior estimation. ICLR 2022 0
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR 2022 0
The Inductive Bias of Quantum Kernels. NIPS/NeurIPS 2021 33
DiBS: Differentiable Bayesian Structure Learning. NIPS/NeurIPS 2021 30
Iterative Teaching by Label Synthesis. NIPS/NeurIPS 2021 4
Bayesian Quadrature on Riemannian Data Manifolds. ICML 2021 2
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS 2021 11
Spatially Structured Recurrent Modules. ICLR 2021 2
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML 2021 11
Dynamic Inference with Neural Interpreters. NIPS/NeurIPS 2021 15
Causal Influence Detection for Improving Efficiency in Reinforcement Learning. NIPS/NeurIPS 2021 14
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NIPS/NeurIPS 2021 98
Regret Bounds for Gaussian-Process Optimization in Large Domains. NIPS/NeurIPS 2021 3
Learning with Hyperspherical Uniformity. AISTATS 2021 17
Backward-Compatible Prediction Updates: A Probabilistic Approach. NIPS/NeurIPS 2021 6
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. EMNLP 2021 14
A teacher-student framework to distill future trajectories. ICLR 2021 1
Independent mechanism analysis, a new concept? NIPS/NeurIPS 2021 30
Fast And Slow Learning Of Recurrent Independent Mechanisms. ICLR 2021 21
A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI 2021 0
Necessary and sufficient conditions for causal feature selection in time series with latent common causes. ICML 2021 0
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning. ICML 2021 0
Function Contrastive Learning of Transferable Meta-Representations. ICML 2021 0
On Disentangled Representations Learned from Correlated Data. ICML 2021 0
Geometrically Enriched Latent Spaces. AISTATS 2021 0
Predicting Infectiousness for Proactive Contact Tracing. ICLR 2021 0
Learning explanations that are hard to vary. ICLR 2021 0
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. ICLR 2021 0
On the Transfer of Disentangled Representations in Realistic Settings. ICLR 2021 0
Recurrent Independent Mechanisms. ICLR 2021 0
On the design of consequential ranking algorithms. UAI 2020 9
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware. UIST 2020 4
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models. ICRA 2020 5
Testing Goodness of Fit of Conditional Density Models with Kernels. UAI 2020 13
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. JMLR 2020 32
TriFinger: An Open-Source Robot for Learning Dexterity. CoRL 2020 40
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020 10
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. NIPS/NeurIPS 2020 96
Causal analysis of Covid-19 Spread in Germany. NIPS/NeurIPS 2020 14
Weakly-Supervised Disentanglement Without Compromises. ICML 2020 166
Learning Kernel Tests Without Data Splitting. NIPS/NeurIPS 2020 13
Relative gradient optimization of the Jacobian term in unsupervised deep learning. NIPS/NeurIPS 2020 16
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. AAAI 2020 0
Bayesian Online Prediction of Change Points. UAI 2020 0
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020 0
Fair Decisions Despite Imperfect Predictions. AISTATS 2020 0
From Variational to Deterministic Autoencoders. ICLR 2020 0
Disentangling Factors of Variations Using Few Labels. ICLR 2020 0
Counterfactuals uncover the modular structure of deep generative models. ICLR 2020 0
Real Time Trajectory Prediction Using Deep Conditional Generative Models. IEEE Robotics and Automation Letters 2020 0
Causal Discovery from Heterogeneous/Nonstationary Data. JMLR 2020 0
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NIPS/NeurIPS 2019 85
On the Fairness of Disentangled Representations. NIPS/NeurIPS 2019 162
Kernel Mean Matching for Content Addressability of GANs. ICML 2019 6
Perceiving the arrow of time in autoregressive motion. NIPS/NeurIPS 2019 2
Data scarcity, robustness and extreme multi-label classification. MLJ 2019 84
Selecting causal brain features with a single conditional independence test per feature. NIPS/NeurIPS 2019 8
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. UAI 2019 42
Kernel Stein Tests for Multiple Model Comparison. NIPS/NeurIPS 2019 10
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. ICML 2019 2
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML 2019 0
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. ICML 2019 0
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019 0
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. UAI 2019 0
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. JMLR 2019 0
Detecting non-causal artifacts in multivariate linear regression models. ICML 2018 22
Invariant Models for Causal Transfer Learning. JMLR 2018 3
Control of Musculoskeletal Systems Using Learned Dynamics Models. IEEE Robotics and Automation Letters 2018 13
Cause-Effect Inference by Comparing Regression Errors. AISTATS 2018 55
Informative Features for Model Comparison. NIPS/NeurIPS 2018 21
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. NIPS/NeurIPS 2018 32
Spatio-Temporal Transformer Network for Video Restoration. ECCV 2018 92
On Matching Pursuit and Coordinate Descent. ICML 2018 20
Tempered Adversarial Networks. ICML 2018 27
Generalized Score Functions for Causal Discovery. KDD 2018 66
Learning Independent Causal Mechanisms. ICML 2018 0
Differentially Private Database Release via Kernel Mean Embeddings. ICML 2018 0
From Deterministic ODEs to Dynamic Structural Causal Models. UAI 2018 0
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. WSDM 2018 0
Group invariance principles for causal generative models. AISTATS 2018 0
Wasserstein Auto-Encoders. ICLR 2018 0
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions. JMLR 2018 0
Anticipatory action selection for human-robot table tennis. Artificial Intelligence 2017 31
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS/NeurIPS 2017 141
Causal Consistency of Structural Equation Models. UAI 2017 70
Flexible Spatio-Temporal Networks for Video Prediction. CVPR 2017 82
Avoiding Discrimination through Causal Reasoning. NIPS/NeurIPS 2017 457
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 40
Distilling Information Reliability and Source Trustworthiness from Digital Traces. WWW 2017 0
AdaGAN: Boosting Generative Models. NIPS/NeurIPS 2017 190
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017 99
Online Video Deblurring via Dynamic Temporal Blending Network. ICCV 2017 127
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017 25
Learning Blind Motion Deblurring. ICCV 2017 111
DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. WSDM 2017 0
Local Group Invariant Representations via Orbit Embeddings. AISTATS 2017 0
Discovering Causal Signals in Images. CVPR 2017 0
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis. ICCV 2017 0
End-to-End Learning for Image Burst Deblurring. ACCV 2016 29
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. NIPS/NeurIPS 2016 64
Jointly learning trajectory generation and hitting point prediction in robot table tennis. Humanoids 2016 29
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. SDM 2016 6
Using probabilistic movement primitives for striking movements. Humanoids 2016 19
The Arrow of Time in Multivariate Time Series. ICML 2016 30
Domain Adaptation with Conditional Transferable Components. ICML 2016 283
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm. JMLR 2016 30
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 10
Consistent Kernel Mean Estimation for Functions of Random Variables. NIPS/NeurIPS 2016 12
Learning to Deblur. TPAMI 2016 0
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. JMLR 2016 0
Kernel Mean Shrinkage Estimators. JMLR 2016 0
Learning optimal striking points for a ping-pong playing robot. IROS 2015 22
Self-Calibration of Optical Lenses. ICCV 2015 6
Semi-supervised interpolation in an anticausal learning scenario. JMLR 2015 21
Towards a Learning Theory of Cause-Effect Inference. ICML 2015 143
Multi-Source Domain Adaptation: A Causal View. AAAI 2015 157
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015 33
Removing systematic errors for exoplanet search via latent causes. ICML 2015 10
Inference of Cause and Effect with Unsupervised Inverse Regression. AISTATS 2015 65
Telling cause from effect in deterministic linear dynamical systems. ICML 2015 45
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015 68
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015 0
A Permutation-Based Kernel Conditional Independence Test. UAI 2014 89
Kernel Mean Estimation via Spectral Filtering. NIPS/NeurIPS 2014 12
Causal discovery with continuous additive noise models. JMLR 2014 0
Seeing the Arrow of Time. CVPR 2014 84
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem. COLT 2014 3
Randomized Nonlinear Component Analysis. ICML 2014 165
Towards building a Crowd-Sourced Sky Map. AISTATS 2014 1
Inferring latent structures via information inequalities. UAI 2014 44
Estimating Causal Effects by Bounding Confounding. UAI 2014 10
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. ICML 2014 110
Kernel Mean Estimation and Stein Effect. ICML 2014 0
Consistency of Causal Inference under the Additive Noise Model. ICML 2014 0
Domain Adaptation under Target and Conditional Shift. ICML 2013 487
Modeling Information Propagation with Survival Theory. ICML 2013 167
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. NIPS/NeurIPS 2013 13
Causal Inference on Time Series using Restricted Structural Equation Models. NIPS/NeurIPS 2013 102
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit. CVPR 2013 3
One-Class Support Measure Machines for Group Anomaly Detection. UAI 2013 71
The Randomized Dependence Coefficient. NIPS/NeurIPS 2013 171
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI 2013 85
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI 2013 16
Domain Generalization via Invariant Feature Representation. ICML 2013 693
A Machine Learning Approach for Non-blind Image Deconvolution. CVPR 2013 271
Structure and dynamics of information pathways in online media. WSDM 2013 0
Probabilistic movement modeling for intention inference in human-robot interaction. IJRR 2013 0
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. NIPS/NeurIPS 2012 31
A Kernel Two-Sample Test. JMLR 2012 3343
Influence Maximization in Continuous Time Diffusion Networks. ICML 2012 77
Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. ECCV 2012 365
Probabilistic Modeling of Human Movements for Intention Inference. RSS 2012 52
Submodular Inference of Diffusion Networks from Multiple Trees. ICML 2012 16
Learning from Distributions via Support Measure Machines. NIPS/NeurIPS 2012 178
On causal and anticausal learning. ICML 2012 424
The representer theorem for Hilbert spaces: a necessary and sufficient condition. NIPS/NeurIPS 2012 68
Blind Correction of Optical Aberrations. ECCV 2012 42
Information-geometric approach to inferring causal directions. Artificial Intelligence 2012 250
A brain-robot interface for studying motor learning after stroke. IROS 2012 20
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. NIPS/NeurIPS 2011 179
Two-locus association mapping in subquadratic time. KDD 2011 20
Uncovering the Temporal Dynamics of Diffusion Networks. ICML 2011 562
Non-stationary correction of optical aberrations. ICCV 2011 78
On Causal Discovery with Cyclic Additive Noise Models. NIPS/NeurIPS 2011 85
Support Vector Machines as Probabilistic Models. ICML 2011 45
Identifiability of Causal Graphs using Functional Models. UAI 2011 125
Detecting low-complexity unobserved causes. UAI 2011 22
Fast removal of non-uniform camera shake. ICCV 2011 312
Learning inverse kinematics with structured prediction. IROS 2011 49
Learning anticipation policies for robot table tennis. IROS 2011 19
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011 441
Multi-way set enumeration in weight tensors. MLJ 2011 21
Causal Inference on Discrete Data Using Additive Noise Models. TPAMI 2011 0
Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS 2010 72
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010 13
Inferring deterministic causal relations. UAI 2010 156
Causal Markov Condition for Submodular Information Measures. COLT 2010 25
Switched Latent Force Models for Movement Segmentation. NIPS/NeurIPS 2010 37
Efficient filter flow for space-variant multiframe blind deconvolution. CVPR 2010 232
Movement templates for learning of hitting and batting. ICRA 2010 169
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake. NIPS/NeurIPS 2010 132
Probabilistic latent variable models for distinguishing between cause and effect. NIPS/NeurIPS 2010 119
Telling cause from effect based on high-dimensional observations. ICML 2010 0
Hilbert Space Embeddings and Metrics on Probability Measures. JMLR 2010 0
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. NIPS/NeurIPS 2009 190
Identifying confounders using additive noise models. UAI 2009 63
Detecting the direction of causal time series. ICML 2009 43
Learning similarity measure for multi-modal 3D image registration. CVPR 2009 76
Regression by dependence minimization and its application to causal inference in additive noise models. ICML 2009 133
Sparse online model learning for robot control with support vector regression. IROS 2009 31
Diffeomorphic Dimensionality Reduction. NIPS/NeurIPS 2008 25
Tailoring density estimation via reproducing kernel moment matching. ICML 2008 60
Characteristic Kernels on Groups and Semigroups. NIPS/NeurIPS 2008 86
Sparse multiscale gaussian process regression. ICML 2008 64
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. NIPS/NeurIPS 2008 172
Injective Hilbert Space Embeddings of Probability Measures. COLT 2008 157
Nonlinear causal discovery with additive noise models. NIPS/NeurIPS 2008 744
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS/NeurIPS 2008 27
Automatic Image Colorization Via Multimodal Predictions. ECCV 2008 232
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance. NIPS/NeurIPS 2008 64
Learning Inverse Dynamics: a Comparison. ESANN 2008 89
A kernel-based causal learning algorithm. ICML 2007 64
Kernel Measures of Conditional Dependence. NIPS/NeurIPS 2007 513
A Kernel Statistical Test of Independence. NIPS/NeurIPS 2007 694
The Need for Open Source Software in Machine Learning. JMLR 2007 214
Transductive Classification via Local Learning Regularization. AISTATS 2007 146
Local learning projections. ICML 2007 42
A Kernel Approach to Comparing Distributions. AAAI 2007 42
An Analysis of Inference with the Universum. NIPS/NeurIPS 2007 96
Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions. ESANN 2007 7
A Direct Method for Building Sparse Kernel Learning Algorithms. JMLR 2006 0
Learning Dense 3D Correspondence. NIPS/NeurIPS 2006 27
Correcting Sample Selection Bias by Unlabeled Data. NIPS/NeurIPS 2006 1526
A Kernel Method for the Two-Sample-Problem. NIPS/NeurIPS 2006 1738
A Nonparametric Approach to Bottom-Up Visual Saliency. NIPS/NeurIPS 2006 219
Learning with Hypergraphs: Clustering, Classification, and Embedding. NIPS/NeurIPS 2006 1074
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions. NIPS/NeurIPS 2006 10
Large Scale Multiple Kernel Learning. JMLR 2006 5
A Local Learning Approach for Clustering. NIPS/NeurIPS 2006 258
Training Support Vector Machines with Multiple Equality Constraints. ECML/PKDD 2005 13
Large scale genomic sequence SVM classifiers. ICML 2005 63
Learning from labeled and unlabeled data on a directed graph. ICML 2005 445
Iterative Kernel Principal Component Analysis for Image Modeling. TPAMI 2005 305
Implicit surface modelling as an eigenvalue problem. ICML 2005 29
Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005 59
Object correspondence as a machine learning problem. ICML 2005 37
Building Sparse Large Margin Classifiers. ICML 2005 45
Kernel Methods for Measuring Independence. JMLR 2005 343
A brain computer interface with online feedback based on magnetoencephalography. ICML 2005 71
Methods Towards Invasive Human Brain Computer Interfaces. NIPS/NeurIPS 2004 174
A Compression Approach to Support Vector Model Selection. JMLR 2004 60
Machine Learning Applied to Perception: Decision Images for Gender Classification. NIPS/NeurIPS 2004 31
Implicit Wiener Series for Higher-Order Image Analysis. NIPS/NeurIPS 2004 27
An Auditory Paradigm for Brain-Computer Interfaces. NIPS/NeurIPS 2004 106
A kernel view of the dimensionality reduction of manifolds. ICML 2004 601
Kernel Methods for Implicit Surface Modeling. NIPS/NeurIPS 2004 71
Face Detection - Efficient and Rank Deficient. NIPS/NeurIPS 2004 139
Semi-supervised Learning on Directed Graphs. NIPS/NeurIPS 2004 207
Learning to Find Pre-Images. NIPS/NeurIPS 2003 168
Learning with Local and Global Consistency. NIPS/NeurIPS 2003 4117
Use of the Zero-Norm with Linear Models and Kernel Methods. JMLR 2003 853
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. TPAMI 2003 221
Prediction on Spike Data Using Kernel Algorithms. NIPS/NeurIPS 2003 30
Ranking on Data Manifolds. NIPS/NeurIPS 2003 758
Kernel Dependency Estimation. NIPS/NeurIPS 2002 184
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. TPAMI 2002 253
Training Invariant Support Vector Machines. MLJ 2002 615
A Kernel Approach for Learning from Almost Orthogonal Patterns. ECML/PKDD 2002 77
Cluster Kernels for Semi-Supervised Learning. NIPS/NeurIPS 2002 517
A Kernel Approach for Learning from almost Orthogonal Patterns. ECML/PKDD 2002 0
Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation. ICCV 2001 124
Computationally Efficient Face Detection. ICCV 2001 218
Sampling Techniques for Kernel Methods. NIPS/NeurIPS 2001 199
An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 2001 3631
Incorporating Invariances in Non-Linear Support Vector Machines. NIPS/NeurIPS 2001 37
An improved training algorithm for kernel Fisher discriminants. AISTATS 2001 99
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds. AISTATS 2001 34
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. ICML 2001 213
Regularized Principal Manifolds. JMLR 2001 0
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra. NIPS/NeurIPS 2000 115
Sparse Greedy Matrix Approximation for Machine Learning. ICML 2000 742
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm. NIPS/NeurIPS 2000 19
The Kernel Trick for Distances. NIPS/NeurIPS 2000 607
Entropy Numbers of Linear Function Classes. COLT 2000 20
Robust Ensemble Learning for Data Mining. PAKDD 2000 24
Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. IJCNN 2000 0
Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 1999 1261
The Entropy Regularization Information Criterion. NIPS/NeurIPS 1999 3
Support Vector Method for Novelty Detection. NIPS/NeurIPS 1999 1801
Invariant Feature Extraction and Classification in Kernel Spaces. NIPS/NeurIPS 1999 218
v-Arc: Ensemble Learning in the Presence of Outliers. NIPS/NeurIPS 1999 15
Semiparametric Support Vector and Linear Programming Machines. NIPS/NeurIPS 1998 104
Kernel PCA and De-Noising in Feature Spaces. NIPS/NeurIPS 1998 1055
Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS/NeurIPS 1998 217
Learning View Graphs for Robot Navigation. Autonomous Robots 1998 0
Kernel Principal Component Analysis. ICANN 1997 2288
Predicting Time Series with Support Vector Machines. ICANN 1997 1018
The View-Graph Approach to Visual Navigation and Spatial Memory. ICANN 1997 20
From Regularization Operators to Support Vector Kernels. NIPS/NeurIPS 1997 99
Prior Knowledge in Support Vector Kernels. NIPS/NeurIPS 1997 358
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. ICANN 1996 255
Improving the Accuracy and Speed of Support Vector Machines. NIPS/NeurIPS 1996 425
Incorporating Invariances in Support Vector Learning Machines. ICANN 1996 327
Extracting Support Data for a Given Task. KDD 1995 665
Copyright ©2019 Universität Würzburg

Impressum | Privacy | FAQ