Niki Kilbertus
Professor
Works on AI for science, including hybrid mechanistic models, learning in dynamical systems, reinforcement learning and causality.
Niki Kilbertus's lab at TUM works on reinforcement learning and probabilistic & causal inference. 32 papers since 2023, 19 at top venues, mostly NeurIPS and ICLR. 9 PhD students. 4 alumni.
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 59%
- PhD students
- 9
- MS students
- 0
- alumni listed
- 4
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 6 · collecting
- 11 papers2025 · T1 5
- 8 papers2024 · T1 4
- 6 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, TMLR, AAAI
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference8 papers
- Reinforcement learning7 papers
- dynamical2 papers
- dynamical systems2 papers
- mechanistic0 papers
Paper list
From the lab homepage.
20267 papers
- TMLR (T1)Cluster-Dags as Powerful Background Knowledge For Causal Discovery
- NeurIPS (T1)Conservative Continuous-Time Treatment Optimization
- UAI (T1)Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
- FAccTFairness under uncertainty in sequential decisions
- NeurIPS (T1)Graph Distance Based on Cause-Effect Estimands with Latents
- ICLR (T1)Identifiability Challenges in Sparse Linear Ordinary Differential Equations
- NeurIPS (T1)OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems
202511 papers
- NeurIPS (T1)An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
- CLeaRAn Asymmetric Independence Model for Causal Discovery on Path Spaces
- Nature Cell BiologyBiases in machine learning models of human single-cell data
- ICML (T1)Generative Intervention Models for Causal Perturbation Modeling
- Scientific ReportsInstrumental Variable Estimation for Compositional Treatments
- ICML (T1)Learning Representations of Instruments for Partial Identification of Treatment Effects
- Communications in Mathematical SciencesNumerical Analysis of the Causal Action Principle in Low Dimensions
- NeurIPS WorkshopPredicting symbolic ODEs from multiple trajectories
- ICLR (T1)Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
- AAAI (T1)Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity
- CLeaRYour Assumed DAG is Wrong And Here’s How To Deal With It
20248 papers
- Nature MedicineCausal machine learning for predicting treatment outcomes
- TMLR (T1)Learning Counterfactually Invariant Predictors
- ICLR (T1)ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
- NeurIPS WorkshopProjected Neural Differential Equations for Power Grid Modeling with Constraints
- NeurIPS (T1)Targeted Sequential Indirect Experiment Design
- ICMLATowards Physically Consistent Deep Learning For Climate Model Parameterizations
- ICLR (T1)Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
- NeurIPS WorkshopUncertainty-Aware Optimal Treatment Selection for Clinical Time Series
20236 papers
- ICLR (T1)Modeling Content Creator Incentives on Algorithm-Curated Platforms
- ICML (T1)Predicting Ordinary Differential Equations with Transformers
- ICML (T1)Sequential Underspecified Instrument Selection for Cause-Effect Estimation
- NeurIPS (T1)Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
- CLeaRStochastic Causal Programming for Bounding Treatment Effects
- PLOS CompBioSupervised Learning and Model Analysis with Compositional Data
Where alumni went4 listed
Apply to TUMRolling
Not announced yet
Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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10 T1
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.