← Authors

Bernhard Schölkopf

279 papers · 2001–2026 · 21 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (19) 🗺️ Taxonomy Completionist (61) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (24) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (12) 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (84) 🌟 Keyword Trendsetter Combo (4) 🧬 Topic Evolution 👑 Triple Crown 🌱 Topic Pioneer 🔬 Deep Specialist (17) 🏆 Keyword Champion (12) 🏆 Grand Slam 👥 Mega-Team (23) 🤝 Dynamic Duo (26) 📈 Trend Setter 🚀 Conference Pioneer 🔥 Unstoppable (20) ❓ The Questioner (9) 💎 Century Club (276) 🗃️ Keyword Collector (296) ⚡ Prolific Year (43)

Conferences

NIPS (84) ICML (49) ICLR (42) AISTATS (17) JMLR (17) EMNLP (11) UAI (10) CVPR (9) ACL (7) ICCV (5) CLEAR (5) RSS (4) L4DC (3) NAACL (3) AAAI (3) ECCV (2) EACL (2) CORL (2) IJCAI (2) IJCNLP (1) AACL (1)

Top co-authors

Zhijing Jin (29) Francesco Locatello (25) Dominik Janzing (23) Stefan Bauer (23) Mrinmaya Sachan (22) Julius von Kügelgen (19) Michel Besserve (17) Krikamol Muandet (14) Weiyang Liu (13) Rada Mihalcea (13)

Research topics

Statistics (5) Probability (4) Computer Vision (1) Differential Privacy (1) Privacy (1)

Keywords

causal inference (46) causal discovery (24) kernel methods (23) reproducing kernel hilbert space (17) representation learning (12) maximum mean discrepancy (11) unsupervised learning (9) large language model (9) disentangled representation (9) domain adaptation (8) additive noise model (8) distribution shift (8) transfer learning (8) domain generalization (7) inductive bia (7) reinforcement learning (7) generative model (7) time series (6) feature learning (6) independent component analysis (6)

Papers

When Do Language Models Endorse Limitations on Human Rights Principles? EACL 2026
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities EACL 2026
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation AISTATS 2025
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning ICLR 2025
Preference Elicitation for Offline Reinforcement Learning ICLR 2025
Language Model Alignment in Multilingual Trolley Problems ICLR 2025
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs ICLR 2025
The Directionality of Optimization Trajectories in Neural Networks ICLR 2025
Voices of Her: Analyzing Gender Differences in the AI Publication World ACL 2025
DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal ACL 2025
Can Large Language Models Understand Symbolic Graphics Programs? ICLR 2025
Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries IJCNLP 2025
Generalized Interpolating Discrete Diffusion ICML 2025
Generative Intervention Models for Causal Perturbation Modeling ICML 2025
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models ICML 2025
Are Language Models Consequentialist or Deontological Moral Reasoners? EMNLP 2025
Improving Large Language Model Safety with Contrastive Representation Learning EMNLP 2025
Orthogonal Finetuning Made Scalable EMNLP 2025
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering ICLR 2025
Standardizing Structural Causal Models ICLR 2025
Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries AACL 2025
Algorithmic causal structure emerging through compression CLEAR 2025
Identifying Policy Gradient Subspaces ICLR 2024
Detecting and Identifying Selection Structure in Sequential Data ICML 2024
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? ICML 2024
Geometry-Aware Instrumental Variable Regression ICML 2024
Provable Privacy with Non-Private Pre-Processing ICML 2024
Robustness of Nonlinear Representation Learning ICML 2024
Targeted Reduction of Causal Models UAI 2024
Products, Abstractions and Inclusions of Causal Spaces UAI 2024
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion RSS 2024
Causal vs. Anticausal merging of predictors NIPS 2024
Limits of Transformer Language Models on Learning to Compose Algorithms NIPS 2024
On Affine Homotopy between Language Encoders NIPS 2024
From Causal to Concept-Based Representation Learning NIPS 2024
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents NIPS 2024
Do Finetti: On Causal Effects for Exchangeable Data NIPS 2024
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands CORL 2024
Moûsai: Efficient Text-to-Music Diffusion Models ACL 2024
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals ACL 2024
CausalCite: A Causal Formulation of Paper Citations ACL 2024
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization ICLR 2024
Skill or Luck? Return Decomposition via Advantage Functions ICLR 2024
Ghost on the Shell: An Expressive Representation of General 3D Shapes ICLR 2024
A diverse Multilingual News Headlines Dataset from around the World NAACL 2024
Analyzing the Role of Semantic Representations in the Era of Large Language Models NAACL 2024
Causal Modeling with Stationary Diffusions AISTATS 2024
Out-of-Variable Generalisation for Discriminative Models ICLR 2024
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding ICLR 2024
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks. ICLR 2024
Can Large Language Models Infer Causation from Correlation? ICLR 2024
GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs CVPR 2024
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning EMNLP 2024
Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis EMNLP 2024
Implicit Personalization in Language Models: A Systematic Study EMNLP 2024
Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis EMNLP 2024
Controlling Text-to-Image Diffusion by Orthogonal Finetuning NIPS 2023
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware ICLR 2023
Flow Annealed Importance Sampling Bootstrap ICLR 2023
Unsupervised Object Learning via Common Fate CLEAR 2023
On the Interventional Kullback-Leibler Divergence CLEAR 2023
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning CLEAR 2023
Structure by Architecture: Structured Representations without Regularization ICLR 2023
Flow Matching for Scalable Simulation-Based Inference NIPS 2023
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features NIPS 2023
Leveraging sparse and shared feature activations for disentangled representation learning NIPS 2023
BaCaDI: Bayesian Causal Discovery with Unknown Interventions AISTATS 2023
Iterative Teaching by Data Hallucination AISTATS 2023
Estimation Beyond Data Reweighting: Kernel Method of Moments ICML 2023
Homomorphism AutoEncoder -- Learning Group Structured Representations from Observed Transitions ICML 2023
On the Relationship Between Explanation and Prediction: A Causal View ICML 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models ICML 2023
On Data Manifolds Entailed by Structural Causal Models ICML 2023
Provably Learning Object-Centric Representations ICML 2023
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good EMNLP 2023
Metrizing Weak Convergence with Maximum Mean Discrepancies JMLR 2023
A Measure-Theoretic Axiomatisation of Causality NIPS 2023
CLadder: Assessing Causal Reasoning in Language Models NIPS 2023
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models ACL 2023
Membership Inference Attacks against Language Models via Neighbourhood Comparison ACL 2023
Diffusion Based Representation Learning ICML 2023
The Hessian perspective into the Nature of Convolutional Neural Networks ICML 2023
Discrete Key-Value Bottleneck ICML 2023
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability ICLR 2023
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap ICLR 2023
Causal Component Analysis NIPS 2023
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data NIPS 2023
Learning Locomotion Skills from MPC in Sensor Space L4DC 2023
Pairwise Similarity Learning is SimPLE ICCV 2023
Causal effect estimation from observational and interventional data through matrix weighted linear estimators UAI 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing NIPS 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions NIPS 2023
Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning RSS 2023
Bridging the Gap to Real-World Object-Centric Learning ICLR 2023
SE(3) Equivariant Augmented Coupling Flows NIPS 2023
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations CLEAR 2022
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization NIPS 2022
Neural Attentive Circuits NIPS 2022
Assaying Out-Of-Distribution Generalization in Transfer Learning NIPS 2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis NIPS 2022
Direct Advantage Estimation NIPS 2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis NIPS 2022
Amortized Inference for Causal Structure Learning NIPS 2022
AutoML Two-Sample Test NIPS 2022
Function Classes for Identifiable Nonlinear Independent Component Analysis NIPS 2022
Probable Domain Generalization via Quantile Risk Minimization NIPS 2022
Exploring the Latent Space of Autoencoders with Interventional Assays NIPS 2022
Interventions, Where and How? Experimental Design for Causal Models at Scale NIPS 2022
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment NIPS 2022
Differentially Private Language Models for Secure Data Sharing EMNLP 2022
Logical Fallacy Detection EMNLP 2022
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain ICLR 2022
A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles RSS 2022
On the Fairness of Causal Algorithmic Recourse AAAI 2022
Towards Principled Disentanglement for Domain Generalization CVPR 2022
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction ICLR 2022
Group equivariant neural posterior estimation ICLR 2022
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration ICLR 2022
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers CVPR 2022
Towards Total Recall in Industrial Anomaly Detection CVPR 2022
Structural Causal 3D Reconstruction ECCV 2022
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance NAACL 2022
A Witness Two-Sample Test AISTATS 2022
A prior-based approximate latent Riemannian metric AISTATS 2022
Resampling Base Distributions of Normalizing Flows AISTATS 2022
Adversarially Robust Kernel Smoothing AISTATS 2022
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL AISTATS 2022
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models ICML 2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions ICML 2022
Action-Sufficient State Representation Learning for Control with Structural Constraints ICML 2022
Causal Inference Through the Structural Causal Marginal Problem ICML 2022
On the Adversarial Robustness of Causal Algorithmic Recourse ICML 2022
Generalization and Robustness Implications in Object-Centric Learning ICML 2022
Adversarial Robustness Through the Lens of Causality ICLR 2022
Invariant Causal Representation Learning for Out-of-Distribution Generalization ICLR 2022
The Role of Pretrained Representations for the OOD Generalization of RL Agents ICLR 2022
Phenomenology of Double Descent in Finite-Width Neural Networks ICLR 2022
Learning soft interventions in complex equilibrium systems UAI 2022
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning ICML 2021
Backward-Compatible Prediction Updates: A Probabilistic Approach NIPS 2021
Regret Bounds for Gaussian-Process Optimization in Large Domains NIPS 2021
Dynamic Inference with Neural Interpreters NIPS 2021
The Inductive Bias of Quantum Kernels NIPS 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style NIPS 2021
Iterative Teaching by Label Synthesis NIPS 2021
Causal Influence Detection for Improving Efficiency in Reinforcement Learning NIPS 2021
DiBS: Differentiable Bayesian Structure Learning NIPS 2021
Independent mechanism analysis, a new concept? NIPS 2021
A Theory of Independent Mechanisms for Extrapolation in Generative Models AAAI 2021
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation AISTATS 2021
Geometrically Enriched Latent Spaces AISTATS 2021
Learning with Hyperspherical Uniformity AISTATS 2021
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP EMNLP 2021
A teacher-student framework to distill future trajectories ICLR 2021
On the Transfer of Disentangled Representations in Realistic Settings ICLR 2021
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning ICLR 2021
Spatially Structured Recurrent Modules ICLR 2021
Fast And Slow Learning Of Recurrent Independent Mechanisms ICLR 2021
Learning explanations that are hard to vary ICLR 2021
Predicting Infectiousness for Proactive Contact Tracing ICLR 2021
Recurrent Independent Mechanisms ICLR 2021
Bayesian Quadrature on Riemannian Data Manifolds ICML 2021
Function Contrastive Learning of Transferable Meta-Representations ICML 2021
Necessary and sufficient conditions for causal feature selection in time series with latent common causes ICML 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression ICML 2021
On Disentangled Representations Learned from Correlated Data ICML 2021
Neural Lyapunov Redesign L4DC 2021
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach L4DC 2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach NIPS 2020
On the design of consequential ranking algorithms UAI 2020
Semi-supervised learning, causality, and the conditional cluster assumption UAI 2020
Weakly-Supervised Disentanglement Without Compromises ICML 2020
Causal Discovery from Heterogeneous/Nonstationary Data JMLR 2020
From Variational to Deterministic Autoencoders ICLR 2020
Counterfactuals uncover the modular structure of deep generative models ICLR 2020
Fair Decisions Despite Imperfect Predictions AISTATS 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020
Disentangling Factors of Variations Using Few Labels ICLR 2020
Bayesian Online Prediction of Change Points UAI 2020
Testing Goodness of Fit of Conditional Density Models with Kernels UAI 2020
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems AAAI 2020
TriFinger: An Open-Source Robot for Learning Dexterity CORL 2020
Relative gradient optimization of the Jacobian term in unsupervised deep learning NIPS 2020
Learning Kernel Tests Without Data Splitting NIPS 2020
Causal analysis of Covid-19 Spread in Germany NIPS 2020
Perceiving the arrow of time in autoregressive motion NIPS 2019
Selecting causal brain features with a single conditional independence test per feature NIPS 2019
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset NIPS 2019
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA UAI 2019
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory UAI 2019
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise JMLR 2019
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 2019
Kernel Mean Matching for Content Addressability of GANs ICML 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations ICML 2019
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension ICML 2019
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness ICML 2019
On the Fairness of Disentangled Representations NIPS 2019
Kernel Stein Tests for Multiple Model Comparison NIPS 2019
Tempered Adversarial Networks ICML 2018
Informative Features for Model Comparison NIPS 2018
Spatio-temporal Transformer Network for Video Restoration ECCV 2018
Invariant Models for Causal Transfer Learning JMLR 2018
Group Invariance Principles for Causal Generative Models AISTATS 2018
Fidelity-Weighted Learning ICLR 2018
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models NIPS 2018
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions JMLR 2018
Differentially Private Database Release via Kernel Mean Embeddings ICML 2018
Detecting non-causal artifacts in multivariate linear regression models ICML 2018
On Matching Pursuit and Coordinate Descent ICML 2018
Learning Independent Causal Mechanisms ICML 2018
Learning Blind Motion Deblurring ICCV 2017
Discovering Causal Signals in Images CVPR 2017
Flexible Spatio-Temporal Networks for Video Prediction CVPR 2017
Avoiding Discrimination through Causal Reasoning NIPS 2017
AdaGAN: Boosting Generative Models NIPS 2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NIPS 2017
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination IJCAI 2017
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis ICCV 2017
Online Video Deblurring via Dynamic Temporal Blending Network ICCV 2017
Consistent Kernel Mean Estimation for Functions of Random Variables NIPS 2016
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks JMLR 2016
The Arrow of Time in Multivariate Time Series ICML 2016
Kernel Mean Shrinkage Estimators JMLR 2016
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm JMLR 2016
Domain Adaptation with Conditional Transferable Components ICML 2016
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels NIPS 2016
Self-Calibration of Optical Lenses ICCV 2015
Semi-Supervised Interpolation in an Anticausal Learning Scenario JMLR 2015
Inference of Cause and Effect with Unsupervised Inverse Regression AISTATS 2015
Towards a Learning Theory of Cause-Effect Inference ICML 2015
Removing systematic errors for exoplanet search via latent causes ICML 2015
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment IJCAI 2015
Towards building a Crowd-Sourced Sky Map AISTATS 2014
Seeing the Arrow of Time CVPR 2014
Consistency of Causal Inference under the Additive Noise Model ICML 2014
Kernel Mean Estimation via Spectral Filtering NIPS 2014
Causal Discovery with Continuous Additive Noise Models JMLR 2014
A Machine Learning Approach for Non-blind Image Deconvolution CVPR 2013
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. NIPS 2013
The Randomized Dependence Coefficient NIPS 2013
Causal Inference on Time Series using Restricted Structural Equation Models NIPS 2013
Domain Adaptation under Target and Conditional Shift ICML 2013
Modeling Information Propagation with Survival Theory ICML 2013
Domain Generalization via Invariant Feature Representation ICML 2013
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit CVPR 2013
Probabilistic Modeling of Human Movements for Intention Inference RSS 2012
Learning from Distributions via Support Measure Machines NIPS 2012
The representer theorem for Hilbert spaces: a necessary and sufficient condition NIPS 2012
A Kernel Two-Sample Test JMLR 2012
Semi-Supervised Domain Adaptation with Non-Parametric Copulas NIPS 2012
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance NIPS 2011
On Causal Discovery with Cyclic Additive Noise Models NIPS 2011
Identifying Cause and Effect on Discrete Data using Additive Noise Models AISTATS 2010
Hilbert Space Embeddings and Metrics on Probability Measures JMLR 2010
Switched Latent Force Models for Movement Segmentation NIPS 2010
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake NIPS 2010
Probabilistic latent variable models for distinguishing between cause and effect NIPS 2010
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NIPS 2009
Nonlinear causal discovery with additive noise models NIPS 2008
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance NIPS 2008
Diffeomorphic Dimensionality Reduction NIPS 2008
Characteristic Kernels on Groups and Semigroups NIPS 2008
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis NIPS 2008
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences NIPS 2008
An Analysis of Inference with the Universum NIPS 2007
Kernel Measures of Conditional Dependence NIPS 2007
A Kernel Statistical Test of Independence NIPS 2007
A Direct Method for Building Sparse Kernel Learning Algorithms JMLR 2006
Correcting Sample Selection Bias by Unlabeled Data NIPS 2006
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions NIPS 2006
Learning with Hypergraphs: Clustering, Classification, and Embedding NIPS 2006
Learning Dense 3D Correspondence NIPS 2006
A Kernel Method for the Two-Sample-Problem NIPS 2006
A Nonparametric Approach to Bottom-Up Visual Saliency NIPS 2006
A Local Learning Approach for Clustering NIPS 2006
Large Scale Multiple Kernel Learning JMLR 2006
Use of the Zero-Norm with Linear Models and Kernel Methods JMLR 2003
Regularized Principal Manifolds JMLR 2001