Marc Deisenroth
Professor
Researches data-efficient machine learning, probabilistic modelling and decision making, with applications in climate and weather, nuclear fusion and robotics.
Marc Deisenroth's lab at UCL works on reinforcement learning, probabilistic & causal inference and efficient training & inference. 25 papers since 2023, 18 at top venues, mostly NeurIPS and IROS.
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 72%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 4 papers2025 · T1 4
- 11 papers2024 · T1 7
- 9 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, IROS, AISTATS, ICML, CVPR
Research focuspaper titles that use each topic's words
- Reinforcement learning4 papers
- Probabilistic & causal inference2 papers
- Efficient training & inference2 papers
- weather1 paper
- data-efficient0 papers
Paper list
From the lab homepage.
20261 paper
- Machine Learning: Science and TechnologyUncertainty Quantification of Surrogate Models Using Conformal Prediction (opens in a new tab)
20254 papers
- ICML (T1)Calibrated Physics-Informed Uncertainty Quantification (opens in a new tab)
- NeurIPS (T1)Infinite Neural Operators: Gaussian Processes on Functions (opens in a new tab)
- NeurIPS (T1)Parameter Efficient Fine-tuning via Explained Variance Adaptation (opens in a new tab)
- ICRA (T1)Semantic Cross-Pose Correspondence from a Single Example (opens in a new tab)
202411 papers
- AISTATS (T1)A Unifying Variational Framework for Gaussian Process Motion Planning (opens in a new tab)
- Frontiers in Remote SensingCo-located OLCI Optical Imagery and SAR Altimetry from Sentinel-3 for Enhanced Arctic Spring Sea Ice Surface Classification (opens in a new tab)
- ICML (T1)Gaussian Processes on Cellular Complexes (opens in a new tab)
- Climate InformaticsInterpretable Deep Gaussian Processes for Geospatial Tasks (opens in a new tab)
- UAI (T1)Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems (opens in a new tab)
- IROS (T1)Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials (opens in a new tab)
- Nuclear FusionPlasma Surrogate Modelling using Fourier Neural Operators (opens in a new tab)
- NeurIPS (T1)Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks (opens in a new tab)
- NeurIPS (T1)Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling (opens in a new tab)
- Environmental Data ScienceScalable Data Assimilation with Message Passing (opens in a new tab)
- Nature Communications (T1)Scalable Interpolation of Satellite Altimetry Data with Probabilistic Machine Learning (opens in a new tab)
20239 papers
- AISTATS (T1)Actually Sparse Variational Gaussian Processes (opens in a new tab)
- TMLR (T1)Faster Training of Neural ODEs Using Gauß–Legendre Quadrature (opens in a new tab)
- IROS (T1)Neural Field Movement Primitives for Joint Modelling of Scenes and Motions (opens in a new tab)
- ICLR (T1)Optimal Transport for Offline Imitation Learning (opens in a new tab)
- FAccTQueer In AI: A Case Study in Community-Led Participatory AI (opens in a new tab)
- CASESafe Trajectory Sampling in Model-based Reinforcement Learning (opens in a new tab)
- IROS (T1)Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-finger Hands (opens in a new tab)
- NeurIPS (T1)Thin and Deep Gaussian Processes (opens in a new tab)
- CVPR (T1)Understanding Deep Generative Models with Generalized Empirical Likelihoods (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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