Marc Deisenroth
Google DeepMind Chair of Machine Learning and Artificial Intelligence
University College London
Interests
- Machine learning
- Gaussian processes
- Reinforcement learning
- Robotics
- Meta learning
Publications
- Aligning Time Series on Incomparable Spaces (2021)
- Learning Contact Dynamics using Physically Structured Neural Networks (2021)
- Matérn Gaussian Processes on Graphs (2021)
- Matérn Gaussian Processes on Riemannian Manifolds (2020)
- Probabilistic Active Meta-Learning (2020)
- High-Dimensional Bayesian Optimization with Manifold Gaussian Processes (2020)
- A Foliated View of Transfer Learning (2020)
- Estimating Barycenters of Measures in High Dimensions (2020)
- Efficiently Sampling Functions from Gaussian Process Posteriors (2020)
- Healing Products of Gaussian Process Experts (2020)
- Stochastic Differential Equations with Variational Wishart Diffusions (2020)
- Aligning Time Series on Incomparable Spaces (2020)
- Variational Integrator Networks for Physically Structured Embeddings (2020)
- Mathematics for Machine Learning (2020)
- High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes (2020)
- Variational Integrator Networks (2019)
- GPdoemd: A Python Package for Design of Experiments for Model Discrimination (2019)
- Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018)
- Probabilistic Model-based Imitation Learning (2013)
Seminar organization
- Dan Roy: Admissibility is Bayes Optimality with Infinitesimals (Jul 2022)
- Benjamin Chamberlain: A Continuous Perspective on Graph Neural Networks (Jul 2022)
- Michalis Titsias: Functional Regularisation for Continual Learning with Gaussian Processes (Jun 2022)
- Shahine Bouabid and Siu Chau: Deconditional Downscaling with Gaussian Processes (Feb 2022)
- Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa (Oct 2021)
- Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes (Mar 2021)
- Ruha Benjamin: Reimagining the Default Settings of Technology & Society (Mar 2021)
- César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations (Mar 2021)
- Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (Feb 2021)
- Dhruva Tirumala: Using behavior priors for data efficiency in Reinforcement Learning (Feb 2021)
- Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry (Nov 2020)
- Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks (Sep 2020)
- Tim G. J. Rudner: Inter-domain Deep Gaussian Processes (Aug 2020)
- Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions (Jul 2020)
- Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus? (Jun 2020)
- Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics (Mar 2020)
- Laura Mansfield: Can we use machine learning to predict global patterns of climate change? (Mar 2020)
- So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure (Mar 2020)
- Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation (Feb 2020)
- Keshi He: Toward AI-powered Robots Served for IndustrialProductions and Healthcare (Feb 2020)
- Vincent Adam: Variational inference for Gaussian Process models: multi-GP regression and time-series models (Feb 2020)
- Rituraj Kaushik: Data-efficient adaptation in robotics using priors from the simulator (Feb 2020)
- Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization (Feb 2020)
- Vincent Adam: Doubly Sparse Variational Gaussian Processes (Oct 2019)
- Samuel Kaski: Probabilistic Modelling with Experts (Oct 2019)
- Christian Walder: New Tricks for Estimating Gradients of Expectations (Sep 2019)
- Emtiyaz Khan: Learning-Algorithms from Bayesian Principle (Sep 2019)