Andreas Krause
Full Professor
Leads the Learning & Adaptive Systems group, working on probabilistic machine learning, Bayesian optimization, safe reinforcement learning and uncertainty quantification.
Andreas Krause's lab at ETH Zurich works on probabilistic & causal inference and reinforcement learning. 102 papers since 2023, 90 at top venues, mostly NeurIPS and ICLR. 37 alumni, most often to Google DeepMind and Microsoft.
At a glance
- papers, 2023–2026
- 27/ year
- at T1 venues
- 88%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 37
Papers by yearfrom the lab homepage, 2023–2026
- 23 papers2026 · T1 21 · collecting
- 21 papers2025 · T1 19
- 27 papers2024 · T1 23
- 31 papers2023 · T1 27
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, AISTATS, JMLR
Research focuspaper titles that use each topic's words
- Reinforcement learning33 papers
- Probabilistic & causal inference12 papers
- bayesian optimization5 papers
- safe reinforcement1 paper
- adaptive systems0 papers
Paper list
From the lab homepage.
202623 papers
- NeurIPS (T1)A Theoretical Analysis of Test-Driven Code Generation
- ICML (T1)A Unified Density Operator View of Flow Control and Merging
- ICML (T1)ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
- ICML (T1)Constrained Molecular Generation via Sequential Flow Model Fine-Tuning
- ICML (T1)Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
- AutomaticaFinite-sample-based reachability for safe control with Gaussian Process dynamics
- NeurIPS (T1)Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
- ICLR (T1)Landing with the Score: Riemannian Optimization through Denoising
- ICML (T1)Locally Coherent Parallel Decoding in Diffusion Language Models
- NeurIPS (T1)Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models
- NeurIPS (T1)POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
- ICML (T1)Position: Anthropomorphic Misalignment Research Needs Stronger Evidence
202521 papers
- ICML (T1)Active Fine-Tuning of Multi-Task Policies
- ICLR (T1)ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
- NeurIPS (T1)Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework
- ICLR (T1)Composing Unbalanced Flows for Flexible Docking and Relaxation
- NeurIPS (T1)DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
- ICLR (T1)Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
- NeurIPS (T1)Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
- ICML (T1)Generative Intervention Models for Causal Perturbation Modeling
- ICML (T1)Learning Safety Constraints for Large Language Models
- AISTATS (T1)LITE: Efficiently Estimating Gaussian Probability of Maximality
- COLMLocal Mixtures of Experts: Essentially Free Test-Time Training via Model Merging
- ICLR (T1)MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
202427 papers
- ICLR (T1)Adversarial Causal Bayesian Optimization
- NeurIPS (T1)Bandits with Preference Feedback: A Stackelberg Game Perspective
- IROS (T1)Bridging the Sim-to-Real Gap with Bayesian Inference
- ICML (T1)Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
- AISTATS (T1)Causal Modeling with Stationary Diffusions
- RA-LData-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
- AISTATS (T1)Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
- ACS Central ScienceEnhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning
- AISTATS (T1)Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
- ICML (T1)Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
- ICML (T1)Global reinforcement learning: Beyond linear and convex rewards via submodular semi-gradient methods
- NeurIPS (T1)Group Robust Preference Optimization in Reward-free RLHF
202331 papers
- NeurIPS (T1)A Dynamical System View of Langevin-Based Non-Convex Sampling
- UAI (T1)Aligned Diffusion Schrödinger Bridges
- NeurIPS (T1)Anytime Model Selection in Linear Bandits
- AISTATS (T1)BaCaDI: Bayesian Causal Discovery with Unknown Interventions
- G3: Genes, Genomes, GeneticsBayesian optimization for demographic inference
- NeurIPS WorkshopBreeding Programs Optimization with Reinforcement Learning
- BioinformaticsChromaX: a Fast and Scalable Breeding Program Simulator
- NeurIPS (T1)Contextual Stochastic Bilevel Optimization
- NeurIPS (T1)Efficient Exploration in Continuous-time Model-based Reinforcement Learning
- TMLR (T1)Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
- Artificial Intelligence (T1)GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems
- ICRA (T1)Gradient-Based Trajectory Optimization With Learned Dynamics
Where alumni went37 listed, 68% to industry
Industry 21Academia 10
Most often to
- Google DeepMind
- Microsoft
Also to
- MIT
- Broad Institute
- EPFL
Apply to ETH ZurichRolling
Not announced yet
Computer Science Doctorate (D-INFK)
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- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
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Confirmed on the program page.
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