Bernhard Schölkopf
405 publications
38 venues
H Index 108
Affiliation
Max Planck Institute for Intelligent Systems, T bingen, GermanyGottfried Wilhelm Leibniz Prize
Links
DBLPGoogle Scholar
ORCID- https://www.is.mpg.de/~bs
- https://www.acm.org/articles/people-of-acm/2018/bernhard-scholkopf
- https://dl.acm.org/profile/81100216460
- https://zbmath.org/authors/?q=ai:scholkopf.bernhard
- https://mathscinet.ams.org/mathscinet/MRAuthorID/636968
- https://mathgenealogy.org/id.php?id=99072
- https://en.wikipedia.org/wiki/Bernhard_Sch%C3%B6lkopf
- https://www.wikidata.org/entity/Q4894234
- https://id.loc.gov/authorities/names/n98071318
- https://d-nb.info/gnd/14184633X
| Name | Venue | Year | citations |
|---|---|---|---|
Orthogonal Finetuning Made Scalable.
|
EMNLP | 2025 | 3 |
| Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models.
|
ICML | 2025 | 0 |
| Generalized Interpolating Discrete Diffusion.
|
ICML | 2025 | 33 |
| Are Language Models Consequentialist or Deontological Moral Reasoners?
|
EMNLP | 2025 | 4 |
| DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal.
|
ACL | 2025 | 15 |
| The Directionality of Optimization Trajectories in Neural Networks.
|
ICLR | 2025 | 3 |
| Improving Large Language Model Safety with Contrastive Representation Learning.
|
EMNLP | 2025 | 4 |
| Generative Intervention Models for Causal Perturbation Modeling.
|
ICML | 2025 | 0 |
| Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.
|
AISTATS | 2025 | 0 |
| Can Large Language Models Understand Symbolic Graphics Programs?
|
ICLR | 2025 | 0 |
| Preference Elicitation for Offline Reinforcement Learning.
|
ICLR | 2025 | 0 |
| Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning.
|
ICLR | 2025 | 0 |
| Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering.
|
ICLR | 2025 | 0 |
| MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs.
|
ICLR | 2025 | 0 |
| Standardizing Structural Causal Models.
|
ICLR | 2025 | 0 |
| Language Model Alignment in Multilingual Trolley Problems.
|
ICLR | 2025 | 0 |
| Detecting and Identifying Selection Structure in Sequential Data.
|
ICML | 2024 | 7 |
| Identifying Policy Gradient Subspaces.
|
ICLR | 2024 | 7 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.
|
ICRA | 2024 | 281 |
| Moûsai: Efficient Text-to-Music Diffusion Models.
|
ACL | 2024 | 42 |
| Products, Abstractions and Inclusions of Causal Spaces.
|
UAI | 2024 | 1 |
| Out-of-Variable Generalisation for Discriminative Models.
|
ICLR | 2024 | 6 |
| Skill or Luck? Return Decomposition via Advantage Functions.
|
ICLR | 2024 | 7 |
| Geometry-Aware Instrumental Variable Regression.
|
ICML | 2024 | 0 |
| RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands.
|
CoRL | 2024 | 6 |
| Limits of Transformer Language Models on Learning to Compose Algorithms.
|
NIPS/NeurIPS | 2024 | 12 |
| Do Finetti: On Causal Effects for Exchangeable Data.
|
NIPS/NeurIPS | 2024 | 9 |
| Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents.
|
NIPS/NeurIPS | 2024 | 65 |
| On Affine Homotopy between Language Encoders.
|
NIPS/NeurIPS | 2024 | 6 |
| Provable Privacy with Non-Private Pre-Processing.
|
ICML | 2024 | 4 |
| The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning.
|
EMNLP | 2024 | 8 |
| Diffusion-based learning of contact plans for agile locomotion.
|
Humanoids | 2024 | 8 |
| Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals.
|
ACL | 2024 | 35 |
| Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
|
ICML | 2024 | 24 |
| From Causal to Concept-Based Representation Learning.
|
NIPS/NeurIPS | 2024 | 18 |
| Robustness of Nonlinear Representation Learning.
|
ICML | 2024 | 0 |
| Targeted Reduction of Causal Models.
|
UAI | 2024 | 0 |
| Causal Modeling with Stationary Diffusions.
|
AISTATS | 2024 | 0 |
| GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs.
|
CVPR | 2024 | 0 |
| Causal vs. Anticausal merging of predictors.
|
NIPS/NeurIPS | 2024 | 0 |
| Can Large Language Models Infer Causation from Correlation?
|
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 |
| Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion.
|
RSS | 2024 | 0 |
| Flow Matching for Scalable Simulation-Based Inference.
|
NIPS/NeurIPS | 2023 | 104 |
| Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels.
|
ICML | 2023 | 16 |
| Controlling Text-to-Image Diffusion by Orthogonal Finetuning.
|
NIPS/NeurIPS | 2023 | 227 |
| Nonparametric Identifiability of Causal Representations from Unknown Interventions.
|
NIPS/NeurIPS | 2023 | 87 |
| Pairwise Similarity Learning is SimPLE.
|
ICCV | 2023 | 9 |
| The Hessian perspective into the Nature of Convolutional Neural Networks.
|
ICML | 2023 | 12 |
| Provably Learning Object-Centric Representations.
|
ICML | 2023 | 53 |
| Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features.
|
NIPS/NeurIPS | 2023 | 26 |
| Leveraging sparse and shared feature activations for disentangled representation learning.
|
NIPS/NeurIPS | 2023 | 35 |
| On Data Manifolds Entailed by Structural Causal Models.
|
ICML | 2023 | 4 |
| SE(3) Equivariant Augmented Coupling Flows.
|
NIPS/NeurIPS | 2023 | 41 |
| Learning Linear Causal Representations from Interventions under General Nonlinear Mixing.
|
NIPS/NeurIPS | 2023 | 85 |
| Reinforcement learning with model-based feedforward inputs for robotic table tennis.
|
Autonomous Robots | 2023 | 17 |
| Causal effect estimation from observational and interventional data through matrix weighted linear estimators.
|
UAI | 2023 | 1 |
| Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap.
|
ICLR | 2023 | 23 |
| Benchmarking Offline Reinforcement Learning on Real-Robot Hardware.
|
ICLR | 2023 | 38 |
| Estimation Beyond Data Reweighting: Kernel Method of Moments.
|
ICML | 2023 | 7 |
| Causal Component Analysis.
|
NIPS/NeurIPS | 2023 | 51 |
| CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models.
|
NIPS/NeurIPS | 2023 | 57 |
| Data-Efficient Online Learning of Ball Placement in Robot Table Tennis.
|
IROS | 2023 | 2 |
| A Measure-Theoretic Axiomatisation of Causality.
|
NIPS/NeurIPS | 2023 | 8 |
| 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 |
| Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions.
|
ICML | 2023 | 0 |
| Discrete Key-Value Bottleneck.
|
ICML | 2023 | 0 |
| AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation.
|
ICRA | 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 |
| Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data.
|
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 |
| Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning.
|
RSS | 2023 | 0 |
| Metrizing Weak Convergence with Maximum Mean Discrepancies.
|
JMLR | 2023 | 0 |
| Structural Causal 3D Reconstruction.
|
ECCV | 2022 | 17 |
| Probable Domain Generalization via Quantile Risk Minimization.
|
NIPS/NeurIPS | 2022 | 87 |
| Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization.
|
NIPS/NeurIPS | 2022 | 9 |
| Assaying Out-Of-Distribution Generalization in Transfer Learning.
|
NIPS/NeurIPS | 2022 | 88 |
| Embrace the Gap: VAEs Perform Independent Mechanism Analysis.
|
NIPS/NeurIPS | 2022 | 27 |
| When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment.
|
NIPS/NeurIPS | 2022 | 119 |
| Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.
|
CVPR | 2022 | 87 |
| Adversarial Robustness Through the Lens of Causality.
|
ICLR | 2022 | 63 |
| Causal Inference Through the Structural Causal Marginal Problem.
|
ICML | 2022 | 27 |
| Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis.
|
NIPS/NeurIPS | 2022 | 64 |
| Interventions, Where and How? Experimental Design for Causal Models at Scale.
|
NIPS/NeurIPS | 2022 | 58 |
| Function Classes for Identifiable Nonlinear Independent Component Analysis.
|
NIPS/NeurIPS | 2022 | 49 |
| Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.
|
ICML | 2022 | 118 |
| Differentially Private Language Models for Secure Data Sharing.
|
EMNLP | 2022 | 74 |
| Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions.
|
ICML | 2022 | 8 |
| Neural Attentive Circuits.
|
NIPS/NeurIPS | 2022 | 8 |
| Amortized Inference for Causal Structure Learning.
|
NIPS/NeurIPS | 2022 | 88 |
| A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles.
|
RSS | 2022 | 12 |
| Invariant Causal Representation Learning for Out-of-Distribution Generalization.
|
ICLR | 2022 | 114 |
| Phenomenology of Double Descent in Finite-Width Neural Networks.
|
ICLR | 2022 | 12 |
| AutoML Two-Sample Test.
|
NIPS/NeurIPS | 2022 | 23 |
| 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 |
| Exploring the Latent Space of Autoencoders with Interventional Assays.
|
NIPS/NeurIPS | 2022 | 0 |
| Direct Advantage Estimation.
|
NIPS/NeurIPS | 2022 | 0 |
| The Role of Pretrained Representations for the OOD Generalization of RL Agents.
|
ICLR | 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 |
| Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression.
|
ICML | 2021 | 42 |
| A teacher-student framework to distill future trajectories.
|
ICLR | 2021 | 3 |
| Bayesian Quadrature on Riemannian Data Manifolds.
|
ICML | 2021 | 4 |
| Independent mechanism analysis, a new concept?
|
NIPS/NeurIPS | 2021 | 115 |
| Learning with Hyperspherical Uniformity.
|
AISTATS | 2021 | 47 |
| Fast And Slow Learning Of Recurrent Independent Mechanisms.
|
ICLR | 2021 | 40 |
| Causal Influence Detection for Improving Efficiency in Reinforcement Learning.
|
NIPS/NeurIPS | 2021 | 102 |
| On Disentangled Representations Learned from Correlated Data.
|
ICML | 2021 | 0 |
| Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP.
|
EMNLP | 2021 | 38 |
| Regret Bounds for Gaussian-Process Optimization in Large Domains.
|
NIPS/NeurIPS | 2021 | 8 |
| The Inductive Bias of Quantum Kernels.
|
NIPS/NeurIPS | 2021 | 146 |
| Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation.
|
AISTATS | 2021 | 37 |
| Backward-Compatible Prediction Updates: A Probabilistic Approach.
|
NIPS/NeurIPS | 2021 | 17 |
| Iterative Teaching by Label Synthesis.
|
NIPS/NeurIPS | 2021 | 18 |
| Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.
|
NIPS/NeurIPS | 2021 | 369 |
| DiBS: Differentiable Bayesian Structure Learning.
|
NIPS/NeurIPS | 2021 | 115 |
| Function Contrastive Learning of Transferable Meta-Representations.
|
ICML | 2021 | 0 |
| Dynamic Inference with Neural Interpreters.
|
NIPS/NeurIPS | 2021 | 32 |
| 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 |
| Geometrically Enriched Latent Spaces.
|
AISTATS | 2021 | 0 |
| Spatially Structured Recurrent Modules.
|
ICLR | 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 | 12 |
| Algorithmic recourse under imperfect causal knowledge: a probabilistic approach.
|
NIPS/NeurIPS | 2020 | 196 |
| Weakly-Supervised Disentanglement Without Compromises.
|
ICML | 2020 | 355 |
| Testing Goodness of Fit of Conditional Density Models with Kernels.
|
UAI | 2020 | 29 |
| A Commentary on the Unsupervised Learning of Disentangled Representations.
|
AAAI | 2020 | 26 |
| Disentangling Factors of Variations Using Few Labels.
|
ICLR | 2020 | 75 |
| TriFinger: An Open-Source Robot for Learning Dexterity.
|
CoRL | 2020 | 78 |
| A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation.
|
JMLR | 2020 | 77 |
| A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models.
|
ICRA | 2020 | 6 |
| Relative gradient optimization of the Jacobian term in unsupervised deep learning.
|
NIPS/NeurIPS | 2020 | 25 |
| Learning Kernel Tests Without Data Splitting.
|
NIPS/NeurIPS | 2020 | 27 |
| Causal analysis of Covid-19 Spread in Germany.
|
NIPS/NeurIPS | 2020 | 26 |
| MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware.
|
UIST | 2020 | 3 |
| 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 |
| 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 |
| Perceiving the arrow of time in autoregressive motion.
|
NIPS/NeurIPS | 2019 | 6 |
| Data scarcity, robustness and extreme multi-label classification.
|
MLJ | 2019 | 156 |
| Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness.
|
ICML | 2019 | 1 |
| Kernel Stein Tests for Multiple Model Comparison.
|
NIPS/NeurIPS | 2019 | 15 |
| Selecting causal brain features with a single conditional independence test per feature.
|
NIPS/NeurIPS | 2019 | 15 |
| On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset.
|
NIPS/NeurIPS | 2019 | 157 |
| Kernel Mean Matching for Content Addressability of GANs.
|
ICML | 2019 | 10 |
| The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA.
|
UAI | 2019 | 113 |
| On the Fairness of Disentangled Representations.
|
NIPS/NeurIPS | 2019 | 244 |
| 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 |
| Cause-Effect Inference by Comparing Regression Errors.
|
AISTATS | 2018 | 89 |
| Invariant Models for Causal Transfer Learning.
|
JMLR | 2018 | 1 |
| Spatio-Temporal Transformer Network for Video Restoration.
|
ECCV | 2018 | 175 |
| Control of Musculoskeletal Systems Using Learned Dynamics Models.
|
IEEE Robotics and Automation Letters | 2018 | 23 |
| On Matching Pursuit and Coordinate Descent.
|
ICML | 2018 | 25 |
| Informative Features for Model Comparison.
|
NIPS/NeurIPS | 2018 | 28 |
| Tempered Adversarial Networks.
|
ICML | 2018 | 28 |
| Detecting non-causal artifacts in multivariate linear regression models.
|
ICML | 2018 | 36 |
| Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models.
|
NIPS/NeurIPS | 2018 | 38 |
| Generalized Score Functions for Causal Discovery.
|
KDD | 2018 | 180 |
| 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 |
| Online Video Deblurring via Dynamic Temporal Blending Network.
|
ICCV | 2017 | 171 |
| AdaGAN: Boosting Generative Models.
|
NIPS/NeurIPS | 2017 | 230 |
| Distilling Information Reliability and Source Trustworthiness from Digital Traces.
|
WWW | 2017 | 0 |
| Learning Blind Motion Deblurring.
|
ICCV | 2017 | 154 |
| Avoiding Discrimination through Causal Reasoning.
|
NIPS/NeurIPS | 2017 | 626 |
| Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.
|
IJCAI | 2017 | 155 |
| Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.
|
ICDM | 2017 | 36 |
| Causal Consistency of Structural Equation Models.
|
UAI | 2017 | 155 |
| Flexible Spatio-Temporal Networks for Video Prediction.
|
CVPR | 2017 | 102 |
| Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
|
NIPS/NeurIPS | 2017 | 174 |
| Causal Discovery from Temporally Aggregated Time Series.
|
UAI | 2017 | 64 |
| Anticipatory action selection for human-robot table tennis.
|
Artificial Intelligence | 2017 | 42 |
| 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 |
| Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels.
|
NIPS/NeurIPS | 2016 | 138 |
| Jointly learning trajectory generation and hitting point prediction in robot table tennis.
|
Humanoids | 2016 | 43 |
| The Arrow of Time in Multivariate Time Series.
|
ICML | 2016 | 33 |
| End-to-End Learning for Image Burst Deblurring.
|
ACCV | 2016 | 36 |
| Consistent Kernel Mean Estimation for Functions of Random Variables.
|
NIPS/NeurIPS | 2016 | 15 |
| On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection.
|
UAI | 2016 | 21 |
| Domain Adaptation with Conditional Transferable Components.
|
ICML | 2016 | 362 |
| Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm.
|
JMLR | 2016 | 2 |
| TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification.
|
SDM | 2016 | 6 |
| Using probabilistic movement primitives for striking movements.
|
Humanoids | 2016 | 28 |
| 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 |
| Telling cause from effect in deterministic linear dynamical systems.
|
ICML | 2015 | 0 |
| Self-Calibration of Optical Lenses.
|
ICCV | 2015 | 10 |
| Learning optimal striking points for a ping-pong playing robot.
|
IROS | 2015 | 35 |
| Discovering Temporal Causal Relations from Subsampled Data.
|
ICML | 2015 | 95 |
| Identification of Time-Dependent Causal Model: A Gaussian Process Treatment.
|
IJCAI | 2015 | 46 |
| Multi-Source Domain Adaptation: A Causal View.
|
AAAI | 2015 | 203 |
| Removing systematic errors for exoplanet search via latent causes.
|
ICML | 2015 | 11 |
| Towards a Learning Theory of Cause-Effect Inference.
|
ICML | 2015 | 180 |
| Inference of Cause and Effect with Unsupervised Inverse Regression.
|
AISTATS | 2015 | 79 |
| Semi-supervised interpolation in an anticausal learning scenario.
|
JMLR | 2015 | 26 |
| Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.
|
ICML | 2015 | 0 |
| A Permutation-Based Kernel Conditional Independence Test.
|
UAI | 2014 | 133 |
| Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm.
|
ICML | 2014 | 125 |
| Kernel Mean Estimation via Spectral Filtering.
|
NIPS/NeurIPS | 2014 | 14 |
| Seeing the Arrow of Time.
|
CVPR | 2014 | 104 |
| Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem.
|
COLT | 2014 | 3 |
| Towards building a Crowd-Sourced Sky Map.
|
AISTATS | 2014 | 1 |
| Estimating Causal Effects by Bounding Confounding.
|
UAI | 2014 | 9 |
| Inferring latent structures via information inequalities.
|
UAI | 2014 | 44 |
| Randomized Nonlinear Component Analysis.
|
ICML | 2014 | 183 |
| Kernel Mean Estimation and Stein Effect.
|
ICML | 2014 | 0 |
| Consistency of Causal Inference under the Additive Noise Model.
|
ICML | 2014 | 0 |
| Causal discovery with continuous additive noise models.
|
JMLR | 2014 | 0 |
| On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit.
|
CVPR | 2013 | 2 |
| Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders.
|
UAI | 2013 | 16 |
| Causal Inference on Time Series using Restricted Structural Equation Models.
|
NIPS/NeurIPS | 2013 | 187 |
| From Ordinary Differential Equations to Structural Causal Models: the deterministic case.
|
UAI | 2013 | 114 |
| Domain Adaptation under Target and Conditional Shift.
|
ICML | 2013 | 685 |
| A Machine Learning Approach for Non-blind Image Deconvolution.
|
CVPR | 2013 | 318 |
| Domain Generalization via Invariant Feature Representation.
|
ICML | 2013 | 1312 |
| Modeling Information Propagation with Survival Theory.
|
ICML | 2013 | 183 |
| Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.
|
NIPS/NeurIPS | 2013 | 14 |
| One-Class Support Measure Machines for Group Anomaly Detection.
|
UAI | 2013 | 85 |
| The Randomized Dependence Coefficient.
|
NIPS/NeurIPS | 2013 | 205 |
| 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 |
| A Kernel Two-Sample Test.
|
JMLR | 2012 | 6015 |
| Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database.
|
ECCV | 2012 | 259 |
| Influence Maximization in Continuous Time Diffusion Networks.
|
ICML | 2012 | 132 |
| Probabilistic Modeling of Human Movements for Intention Inference.
|
RSS | 2012 | 53 |
| Semi-Supervised Domain Adaptation with Non-Parametric Copulas.
|
NIPS/NeurIPS | 2012 | 34 |
| On causal and anticausal learning.
|
ICML | 2012 | 660 |
| Submodular Inference of Diffusion Networks from Multiple Trees.
|
ICML | 2012 | 14 |
| Information-geometric approach to inferring causal directions.
|
Artificial Intelligence | 2012 | 308 |
| Learning from Distributions via Support Measure Machines.
|
NIPS/NeurIPS | 2012 | 202 |
| A brain-robot interface for studying motor learning after stroke.
|
IROS | 2012 | 22 |
| Blind Correction of Optical Aberrations.
|
ECCV | 2012 | 63 |
| The representer theorem for Hilbert spaces: a necessary and sufficient condition.
|
NIPS/NeurIPS | 2012 | 93 |
| On Causal Discovery with Cyclic Additive Noise Models.
|
NIPS/NeurIPS | 2011 | 99 |
| Multi-way set enumeration in weight tensors.
|
MLJ | 2011 | 23 |
| Two-locus association mapping in subquadratic time.
|
KDD | 2011 | 21 |
| Kernel-based Conditional Independence Test and Application in Causal Discovery.
|
UAI | 2011 | 693 |
| Learning anticipation policies for robot table tennis.
|
IROS | 2011 | 21 |
| Uncovering the Temporal Dynamics of Diffusion Networks.
|
ICML | 2011 | 611 |
| Identifiability of Causal Graphs using Functional Models.
|
UAI | 2011 | 166 |
| Detecting low-complexity unobserved causes.
|
UAI | 2011 | 24 |
| Fast removal of non-uniform camera shake.
|
ICCV | 2011 | 354 |
| Learning inverse kinematics with structured prediction.
|
IROS | 2011 | 59 |
| Non-stationary correction of optical aberrations.
|
ICCV | 2011 | 101 |
| Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance.
|
NIPS/NeurIPS | 2011 | 203 |
| Support Vector Machines as Probabilistic Models.
|
ICML | 2011 | 51 |
| Causal Inference on Discrete Data Using Additive Noise Models.
|
TPAMI | 2011 | 0 |
| Switched Latent Force Models for Movement Segmentation.
|
NIPS/NeurIPS | 2010 | 43 |
| Causal Markov Condition for Submodular Information Measures.
|
COLT | 2010 | 36 |
| Identifying Cause and Effect on Discrete Data using Additive Noise Models.
|
AISTATS | 2010 | 88 |
| Probabilistic latent variable models for distinguishing between cause and effect.
|
NIPS/NeurIPS | 2010 | 140 |
| Inferring deterministic causal relations.
|
UAI | 2010 | 200 |
| Efficient filter flow for space-variant multiframe blind deconvolution.
|
CVPR | 2010 | 265 |
| Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake.
|
NIPS/NeurIPS | 2010 | 143 |
| Movement templates for learning of hitting and batting.
|
ICRA | 2010 | 179 |
| Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery.
|
UAI | 2010 | 14 |
| Telling cause from effect based on high-dimensional observations.
|
ICML | 2010 | 0 |
| Hilbert Space Embeddings and Metrics on Probability Measures.
|
JMLR | 2010 | 0 |
| Identifying confounders using additive noise models.
|
UAI | 2009 | 70 |
| Regression by dependence minimization and its application to causal inference in additive noise models.
|
ICML | 2009 | 163 |
| Sparse online model learning for robot control with support vector regression.
|
IROS | 2009 | 34 |
| Learning similarity measure for multi-modal 3D image registration.
|
CVPR | 2009 | 83 |
| Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions.
|
NIPS/NeurIPS | 2009 | 241 |
| Detecting the direction of causal time series.
|
ICML | 2009 | 43 |
| Diffeomorphic Dimensionality Reduction.
|
NIPS/NeurIPS | 2008 | 28 |
| Characteristic Kernels on Groups and Semigroups.
|
NIPS/NeurIPS | 2008 | 110 |
| Sparse multiscale gaussian process regression.
|
ICML | 2008 | 65 |
| Automatic Image Colorization Via Multimodal Predictions.
|
ECCV | 2008 | 260 |
| An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis.
|
NIPS/NeurIPS | 2008 | 185 |
| Injective Hilbert Space Embeddings of Probability Measures.
|
COLT | 2008 | 177 |
| Learning Inverse Dynamics: a Comparison.
|
ESANN | 2008 | 103 |
| Bayesian Experimental Design of Magnetic Resonance Imaging Sequences.
|
NIPS/NeurIPS | 2008 | 27 |
| Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance.
|
NIPS/NeurIPS | 2008 | 65 |
| Tailoring density estimation via reproducing kernel moment matching.
|
ICML | 2008 | 64 |
| Nonlinear causal discovery with additive noise models.
|
NIPS/NeurIPS | 2008 | 1178 |
| Transductive Classification via Local Learning Regularization.
|
AISTATS | 2007 | 147 |
| An Analysis of Inference with the Universum.
|
NIPS/NeurIPS | 2007 | 109 |
| A kernel-based causal learning algorithm.
|
ICML | 2007 | 80 |
| A Kernel Approach to Comparing Distributions.
|
AAAI | 2007 | 57 |
| Local learning projections.
|
ICML | 2007 | 41 |
| Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions.
|
ESANN | 2007 | 7 |
| Kernel Measures of Conditional Dependence.
|
NIPS/NeurIPS | 2007 | 665 |
| A Kernel Statistical Test of Independence.
|
NIPS/NeurIPS | 2007 | 1009 |
| The Need for Open Source Software in Machine Learning.
|
JMLR | 2007 | 242 |
| Large Scale Multiple Kernel Learning.
|
JMLR | 2006 | 1419 |
| Learning with Hypergraphs: Clustering, Classification, and Embedding.
|
NIPS/NeurIPS | 2006 | 1517 |
| A Direct Method for Building Sparse Kernel Learning Algorithms.
|
JMLR | 2006 | 111 |
| A Nonparametric Approach to Bottom-Up Visual Saliency.
|
NIPS/NeurIPS | 2006 | 222 |
| Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions.
|
NIPS/NeurIPS | 2006 | 16 |
| Learning Dense 3D Correspondence.
|
NIPS/NeurIPS | 2006 | 26 |
| A Local Learning Approach for Clustering.
|
NIPS/NeurIPS | 2006 | 296 |
| Correcting Sample Selection Bias by Unlabeled Data.
|
NIPS/NeurIPS | 2006 | 1886 |
| A Kernel Method for the Two-Sample-Problem.
|
NIPS/NeurIPS | 2006 | 2571 |
| Kernel Methods for Measuring Independence.
|
JMLR | 2005 | 437 |
| A brain computer interface with online feedback based on magnetoencephalography.
|
ICML | 2005 | 74 |
| Object correspondence as a machine learning problem.
|
ICML | 2005 | 39 |
| Learning from labeled and unlabeled data on a directed graph.
|
ICML | 2005 | 472 |
| Iterative Kernel Principal Component Analysis for Image Modeling.
|
TPAMI | 2005 | 332 |
| Kernel Constrained Covariance for Dependence Measurement.
|
AISTATS | 2005 | 0 |
| Implicit surface modelling as an eigenvalue problem.
|
ICML | 2005 | 36 |
| Training Support Vector Machines with Multiple Equality Constraints.
|
ECML/PKDD | 2005 | 12 |
| Large scale genomic sequence SVM classifiers.
|
ICML | 2005 | 63 |
| Building Sparse Large Margin Classifiers.
|
ICML | 2005 | 47 |
| A kernel view of the dimensionality reduction of manifolds.
|
ICML | 2004 | 628 |
| Semi-supervised Learning on Directed Graphs.
|
NIPS/NeurIPS | 2004 | 210 |
| Face Detection - Efficient and Rank Deficient.
|
NIPS/NeurIPS | 2004 | 140 |
| Methods Towards Invasive Human Brain Computer Interfaces.
|
NIPS/NeurIPS | 2004 | 192 |
| An Auditory Paradigm for Brain-Computer Interfaces.
|
NIPS/NeurIPS | 2004 | 111 |
| Kernel Methods for Implicit Surface Modeling.
|
NIPS/NeurIPS | 2004 | 78 |
| Machine Learning Applied to Perception: Decision Images for Gender Classification.
|
NIPS/NeurIPS | 2004 | 29 |
| A Compression Approach to Support Vector Model Selection.
|
JMLR | 2004 | 60 |
| Implicit Wiener Series for Higher-Order Image Analysis.
|
NIPS/NeurIPS | 2004 | 30 |
| Ranking on Data Manifolds.
|
NIPS/NeurIPS | 2003 | 794 |
| Use of the Zero-Norm with Linear Models and Kernel Methods.
|
JMLR | 2003 | 875 |
| Learning to Find Pre-Images.
|
NIPS/NeurIPS | 2003 | 196 |
| Learning with Local and Global Consistency.
|
NIPS/NeurIPS | 2003 | 4603 |
| Prediction on Spike Data Using Kernel Algorithms.
|
NIPS/NeurIPS | 2003 | 31 |
| Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces.
|
TPAMI | 2003 | 218 |
| Cluster Kernels for Semi-Supervised Learning.
|
NIPS/NeurIPS | 2002 | 557 |
| A Kernel Approach for Learning from Almost Orthogonal Patterns.
|
ECML/PKDD | 2002 | 78 |
| Kernel Dependency Estimation.
|
NIPS/NeurIPS | 2002 | 193 |
| Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification.
|
TPAMI | 2002 | 262 |
| A Kernel Approach for Learning from almost Orthogonal Patterns.
|
ECML/PKDD | 2002 | 0 |
| Training Invariant Support Vector Machines.
|
MLJ | 2002 | 0 |
| A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds.
|
AISTATS | 2001 | 35 |
| Estimating a Kernel Fisher Discriminant in the Presence of Label Noise.
|
ICML | 2001 | 227 |
| Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation.
|
ICCV | 2001 | 123 |
| An improved training algorithm for kernel Fisher discriminants.
|
AISTATS | 2001 | 95 |
| Incorporating Invariances in Non-Linear Support Vector Machines.
|
NIPS/NeurIPS | 2001 | 36 |
| Sampling Techniques for Kernel Methods.
|
NIPS/NeurIPS | 2001 | 205 |
| Computationally Efficient Face Detection.
|
ICCV | 2001 | 220 |
| Regularized Principal Manifolds.
|
JMLR | 2001 | 1 |
| An introduction to kernel-based learning algorithms.
|
IEEE Trans. Neural Networks | 2001 | 3766 |
| Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra.
|
NIPS/NeurIPS | 2000 | 117 |
| Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm.
|
NIPS/NeurIPS | 2000 | 19 |
| Robust Ensemble Learning for Data Mining.
|
PAKDD | 2000 | 24 |
| The Kernel Trick for Distances.
|
NIPS/NeurIPS | 2000 | 700 |
| Entropy Numbers of Linear Function Classes.
|
COLT | 2000 | 24 |
| Sparse Greedy Matrix Approximation for Machine Learning.
|
ICML | 2000 | 47 |
| Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments.
|
IJCNN | 2000 | 0 |
| v-Arc: Ensemble Learning in the Presence of Outliers.
|
NIPS/NeurIPS | 1999 | 18 |
| The Entropy Regularization Information Criterion.
|
NIPS/NeurIPS | 1999 | 2 |
| Input space versus feature space in kernel-based methods.
|
IEEE Trans. Neural Networks | 1999 | 1313 |
| Invariant Feature Extraction and Classification in Kernel Spaces.
|
NIPS/NeurIPS | 1999 | 211 |
| Support Vector Method for Novelty Detection.
|
NIPS/NeurIPS | 1999 | 2582 |
| Kernel PCA and De-Noising in Feature Spaces.
|
NIPS/NeurIPS | 1998 | 1150 |
| Shrinking the Tube: A New Support Vector Regression Algorithm.
|
NIPS/NeurIPS | 1998 | 215 |
| Semiparametric Support Vector and Linear Programming Machines.
|
NIPS/NeurIPS | 1998 | 105 |
| Learning View Graphs for Robot Navigation.
|
Autonomous Robots | 1998 | 0 |
| Kernel Principal Component Analysis.
|
ICANN | 1997 | 2701 |
| From Regularization Operators to Support Vector Kernels.
|
NIPS/NeurIPS | 1997 | 97 |
| The View-Graph Approach to Visual Navigation and Spatial Memory.
|
ICANN | 1997 | 21 |
| Prior Knowledge in Support Vector Kernels.
|
NIPS/NeurIPS | 1997 | 358 |
| Predicting Time Series with Support Vector Machines.
|
ICANN | 1997 | 1067 |
| Improving the Accuracy and Speed of Support Vector Machines.
|
NIPS/NeurIPS | 1996 | 412 |
| Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models.
|
ICANN | 1996 | 251 |
| Incorporating Invariances in Support Vector Learning Machines.
|
ICANN | 1996 | 326 |
| Extracting Support Data for a Given Task.
|
KDD | 1995 | 675 |