Bernhard Schölkopf
Full Professor
Researches machine learning and causal inference, including causal representation learning, kernel methods and applications in the sciences.
Bernhard Schölkopf's lab at ETH Zurich works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 141 papers since 2023, 98 at top venues, mostly ICLR and NeurIPS.
At a glance
- papers, 2023–2026
- 37/ year
- at T1 venues
- 70%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 16 papers2026 · T1 10 · collecting
- 29 papers2025 · T1 21
- 40 papers2024 · T1 31
- 56 papers2023 · T1 36
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, ACL, AISTATS
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference40 papers
- Learning theory & optimization37 papers
- causal34 papers
- Efficient training & inference12 papers
- kernel2 papers
Paper list
From DBLP.
202616 papers
- Artif. Intell. MedicineA critical perspective on finite sample conformal prediction theory in medical applications (opens in a new tab)
- RO-MANDeclarative Reactive Robot Behaviors From Imperative Language Models (opens in a new tab)
- Proc. ACM Comput. Graph. Interact. Tech.Echoes of the Prior: A Computational Phenomenology of Forgetting (opens in a new tab)
- EACLHow Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities (opens in a new tab)
- CVPR (T1)Hyperbolic Busemann Neural Networks (opens in a new tab)
- ICLR (T1)Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies (opens in a new tab)
- ACL (T1)On the Emergence and Test-Time Use of Structural Information in Large Language Models (opens in a new tab)
- Trans. Mach. Learn. Res.Online Learning and Unlearning: Efficient Algorithms with Near-Optimal Regret Guarantees (opens in a new tab)
- ACL (T1)PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (opens in a new tab)
- ICLR (T1)Proper Velocity Neural Networks (opens in a new tab)
- ICLR (T1)Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles (opens in a new tab)
- ICLR (T1)Scaling Behavior of Discrete Diffusion Language Models (opens in a new tab)
202529 papers
- AISTATS (T1)Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation (opens in a new tab)
- CLeaRAlgorithmic causal structure emerging through compression (opens in a new tab)
- EMNLP (T1)Are Language Models Consequentialist or Deontological Moral Reasoners? (opens in a new tab)
- NeurIPS (T1)Are Language Models Efficient Reasoners? A Perspective from Logic Programming (opens in a new tab)
- Nat.Artificial intelligence for modelling infectious disease epidemics (opens in a new tab)
- ICLR (T1)Can Large Language Models Understand Symbolic Graphics Programs? (opens in a new tab)
- ICLR (T1)Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering (opens in a new tab)
- NeurIPS (T1)Counterfactual reasoning: an analysis of in-context emergence (opens in a new tab)
- ACL (T1)DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal (opens in a new tab)
- NeurIPS (T1)Do-PFN: In-Context Learning for Causal Effect Estimation (opens in a new tab)
- ICML (T1)Generalized Interpolating Discrete Diffusion (opens in a new tab)
- ICML (T1)Generative Intervention Models for Causal Perturbation Modeling (opens in a new tab)
202440 papers
- NAACL (T1)A diverse Multilingual News Headlines Dataset from around the World (opens in a new tab)
- Trans. Mach. Learn. Res.A Probabilistic Model behind Self- Supervised Learning (opens in a new tab)
- NAACL (T1)Analyzing the Role of Semantic Representations in the Era of Large Language Models (opens in a new tab)
- ICLR (T1)Can Large Language Models Infer Causation from Correlation? (opens in a new tab)
- AISTATS (T1)Causal Modeling with Stationary Diffusions (opens in a new tab)
- NeurIPS (T1)Causal vs. Anticausal merging of predictors (opens in a new tab)
- Findings of ACLCausalCite: A Causal Formulation of Paper Citations (opens in a new tab)
- ACL (T1)Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals (opens in a new tab)
- NeurIPS (T1)Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents (opens in a new tab)
- Trans. Mach. Learn. Res.Deep Backtracking Counterfactuals for Causally Compliant Explanations (opens in a new tab)
- ICLR (T1)Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding (opens in a new tab)
- ICML (T1)Detecting and Identifying Selection Structure in Sequential Data (opens in a new tab)
202356 papers
- ACL (T1)A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (opens in a new tab)
- Nat. Comput. Sci.A machine learning route between band mapping and band structure (opens in a new tab)
- NeurIPS (T1)A Measure-Theoretic Axiomatisation of Causality (opens in a new tab)
- ACM Comput. Surv.A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations (opens in a new tab)
- ICRA (T1)AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation (opens in a new tab)
- AISTATS (T1)BaCaDI: Bayesian Causal Discovery with Unknown Interventions (opens in a new tab)
- ICLR (T1)Benchmarking Offline Reinforcement Learning on Real-Robot Hardware (opens in a new tab)
- Findings of EMNLPBeyond Good Intentions: Reporting the Research Landscape of NLP for Social Good (opens in a new tab)
- ICLR (T1)Bridging the Gap to Real-World Object-Centric Learning (opens in a new tab)
- NeurIPS (T1)Causal Component Analysis (opens in a new tab)
- NeurIPS (T1)Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data (opens in a new tab)
- UAI (T1)Causal effect estimation from observational and interventional data through matrix weighted linear estimators (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
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Computer Science Doctorate (D-INFK)
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- Proficiency in written and spoken English
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