Recent & Upcoming Talks

In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be …

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their …

I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based …

We derive a family of Monte Carlo estimators for gradients of expectations, which is related to the log-derivative trick, but involves …

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive …

People

Principal Investigators

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Marc Deisenroth

DeepMind Chair in Artificial Intelligence

Machine learning, Gaussian processes, Reinforcement learning, Robotics, Meta learning

Postdoctoral Researchers

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K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity

PhD Students

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Alexander Terenin

PhD Student

Machine learning, Bayesian statistics, Geometric learning, Meta learning

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James Wilson

PhD Student

Machine learning, Bayesian optimization, Practical approximate inference

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Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning

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Riccardo Moriconi

PhD Student

Machine learning, Bayesian optimization

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Samuel Cohen

PhD Student

Machine learning, Optimal transport

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Sanket Kamthe

PhD Student

Machine learning, Reinforcement learning, Optimal control, Copulas

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Simon Olofsson

PhD Student

Machine learning, Bayesian optimization, Mechanistic models, Model discrimination

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Steindór Sæmundsson

PhD Student

Machine learning, Meta learning, Structural priors

Visitors

Alumni

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Benjamin Chamberlain

PhD (10/2014 - 08/2018)

Machine learning, Community detection, Representation of graphs, Hyperbolic embeddings

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Hugh Salimbeni

PhD (10/2015-10/2019)

Machine learning, Deep probabilistic models, Approximate inference

Recent Publications

Quickly discover relevant content by filtering publications.

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. It is widely believed that MCMC methods are …

Mathematics for Machine Learning is a book that motivates people to learn mathematical concepts. The book is not intended to cover …

Recent News

We are looking for a (Senior) Research Fellow at the intersection of climate science and machine learning.

We are looking for a (Senior) Research Fellow at the intersection of robotics and machine learning.

Rasmus Larsen visits SML

Samuel Cohen joins SML