Statistical Machine Learning Group
Statistical Machine Learning Group
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Marc Deisenroth
DeepMind Chair of Machine Learning and Artificial Intelligence
University College London
Interests
Machine learning
Gaussian processes
Reinforcement learning
Robotics
Meta learning
Latest
Dr. Rosca
Student Projects 2022
Dr. Wilson
Dan Roy: Admissibility is Bayes Optimality with Infinitesimals
Benjamin Chamberlain: A Continuous Perspective on Graph Neural Networks
Dr. Petangoda
Paper accepted at TMLR
Michalis Titsias: Functional Regularisation for Continual Learning with Gaussian Processes
Paper accepted at TMLR
Paper accepted at JMLR
Shahine Bouabid and Siu Chau: Deconditional Downscaling with Gaussian Processes
SML@NeurIPS 2021
Dr. Terenin
Dr. Sæmundsson
Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa
Three papers accepted at ICML 2021
Papers accepted at AIES 2021
Best Student Paper Award at AISTATS 2021
Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes
Ruha Benjamin: Reimagining the Default Settings of Technology & Society
César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations
Dr. Kamthe
Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Dr. Moriconi
Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation accepted at ICRA 2021
Dhruva Tirumala: Using behavior priors for data efficiency in Reinforcement Learning
Four Papers Acccepted at AISTATS 2021 and ICLR 2021
Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry
SML@NeurIPS 2020
Welcome Dr. So Takao
Welcome, Jackie Kay
Welcome, Sicelukwanda Zwane
Welcome, Yicheng Luo
Two Papers Acccepted at NeurIPS 2020
Matérn Gaussian Processes on Riemannian Manifolds
Probabilistic Active Meta-Learning
MSc Project Submissions
Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks
Tim G. J. Rudner: Inter-domain Deep Gaussian Processes
Paper accepted at Machine Learning Journal
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes
A Foliated View of Transfer Learning
Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions
Estimating Barycenters of Measures in High Dimensions
Honorable Mention Award for Outstanding Paper at ICML 2020
Efficiently Sampling Functions from Gaussian Process Posteriors
Healing Products of Gaussian Process Experts
Stochastic Differential Equations with Variational Wishart Diffusions
Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus?
Aligning Time Series on Incomparable Spaces
Variational Integrator Networks for Physically Structured Embeddings
Three papers accepted at ICML 2020
Mathematics for Machine Learning
Dr. Olofsson
Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics
Laura Mansfield: Can we use machine learning to predict global patterns of climate change?
So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure
Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation
Keshi He: Toward AI-powered Robots Served for IndustrialProductions and Healthcare
Vincent Adam: Variational inference for Gaussian Process models: multi-GP regression and time-series models
Rituraj Kaushik: Data-efficient adaptation in robotics using priors from the simulator
Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization
High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes
Two papers accepted at AISTATS 2020
Variational Integrator Networks
Research Fellow/Senior Research Fellow position in Machine Learning for Climate Science
Research Fellow/Senior Research Fellow position in Machine Learning for Robotics
Vincent Adam: Doubly Sparse Variational Gaussian Processes
Welcome to the team, Rasmus
Dr. Salimbeni
Samuel Kaski: Probabilistic Modelling with Experts
Welcome to the team, Samuel
Four papers accepted at NeurISP workshops
Christian Walder: New Tricks for Estimating Gradients of Expectations
Emtiyaz Khan: Learning-Algorithms from Bayesian Principle
Paper on fast submodular function minimization accepted at NeurIPS 2019
Sesh Kumar awarded DSI Fellowship
Paper on deep GPs with importance-weighted variational inference accepted at ICML 2019
JP Morgan Fellowship Award to Sanket Kamthe
GPdoemd: A Python Package for Design of Experiments for Model Discrimination
Three papers accepted at NeurIPS 2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Dr. Chamberlain
Meta-learning paper accepted at UAI 2018
Marc Deisenroth receives Early Career Award
Paper on Real-Time Community Detection accepted at PLOS ONE
Probabilistic Model-based Imitation Learning
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