We are organising a Roboto Learning workshop at NeurIPS 2019 to highlight challenges in real world robotics, even details http://wp.doc.ic.ac.uk/sml/event/neurips-workshop-on-robot-learning-control-and-interaction-in-the-real-world/
Sanket Kamthe has been selected as a 2019 J.P. Morgan PhD Fellow. Congratulations! https://www.jpmorgan.com/global/technology/ai/awards/phd-fellowship-award-recipients
Hugh and James got papers accepted at NeurIPS, which is excellent news. Very well done! Gaussian Process Conditional Density Estimation Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc P. Deisenroth Orthogonally Decoupled Variational Gaussian Processes Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc P. Deisenroth Maximizing acquisition functions for Bayesian optimization James Wilson, Frank Hutter, Marc P…. Read more »
Congratulations to Ben Chamberlain for defending his PhD! Ben has done some great work on network embeddings, community detection at scale and inference of latent features of social media users. The PhD hat reflects some of the work Ben has done over the last years. 2/3 of PhD committee post-viva
Steindor’s paper on Meta Reinforcement Learning has been accepted at UAI 2018. Congratulations! Steindor Saemundsson, Katja Hofmann, Marc Peter Deisenroth Meta Reinforcement Learning with Latent Variable Gaussian Processes Conference on Uncertainty in Artificial Intelligence, 2018
Marc Deisenroth has been awarded The President’s Award for Outstanding Early Career Researcher
Congratulation to Simon, whose paper got accepted at ICML 2018 Simon Olofsson, Marc Peter Deisenroth, Ruth Misener Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches International Conference on Machine Learning, 2018
Congratulations to Ben Chamberlain for his accepted paper Benjamin P. Chamberlain, Josh Levy-Kramer, Clive Humby, Marc P. Deisenroth Real-Time Community Detection in Full Social Networks on a Laptop PLOS ONE, 2018
Congratulations to Sanket and Hugh: Their papers have been accepted at AISTATS 2018 Sanket Kamthe, Marc P. Deisenroth, Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman, Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models, Proceedings of the International… Read more »
Hugh Salimbeni has been been awarded a Best-Reviewer Award at NIPS 2017. Congratulations!
Welcome to Dr. Sesh Kumar, who just joined us from IST Austria.
We are happy to welcome Janith and James to the lab and Imperial.
Two papers from the group got accepted at NIPS: Hugh Salimbeni, Marc P. Deisenroth, Doubly Stochastic Variational Inference for Deep Gaussian Processes, Advances in Neural Information Processing Systems (NIPS), 2017 Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc P. Deisenroth, James Hensman, Identification of Gaussian Process State Space Models, Advances in Neural Information Processing Systems (NIPS), 2017 Congratulations to Hugh… Read more »
Marc Deisenroth will be giving an introduction to Mathematics for Machine Learning at the Deep Learning Indaba.
Marc Deisenroth will be giving a Gaussian Process Tutorial at the WASP Summer School in Stockholm.
Congratulations to Ben Chamberlain who got two papers accepted at ECML: Benjamin P. Chamberlain, Clive Humby, and Marc P. Deisenroth: Probabilistic Inference of Twitter Users’ Age based on What They Follow CH Bryan Liu, Benjamin P. Chamberlain, Duncan A. Little, and Angelo Cardoso: Generalising Random Forest Parameter Optimisation to Include Stability and Cost
Congratulations to Ben Chamberlain, whose paper Customer Life Time Value Prediction Using Embeddings has been accepted as at KDD 2017. The overall acceptance rate was 21.4%. Well done!
Georgi Ivanov and Hugh Salimbeni have been awarded a Microsoft Azure Sponsorship to support a research project. Congratulations!
Marc Deisenroth has been awarded a Microsoft Azure Sponsorship for teaching and research.
Hugh and Matt present their work at the NIPS workshop on Practical Bayesian Nonparametrics: Matthew C. H. Lee, Hugh Salimbeni, Marc P. Deisenroth, Ben Glocker, Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems, NIPS Workshop on Practical Bayesian Nonparametrics, 2016 Hugh Salimbeni, Marc P. Deisenroth, Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data, NIPS… Read more »
Dean Garlick and Riccardo Moriconi are joining as PhD students today. Welcome!
Congratulations to Roberto for a successful PhD completion!
Sanket Kamthe has been awarded a Google EMEA Scholarship for 2016/17. The Google EMEA Scholarship is highly competitive and only awarded to 10 students per year. Scholarships are awarded based on the strength of the applicant’s academic background, leadership skills and demonstrated passion for Computer Science. Congratulations!
Ben gave a talk at pydata on his work on Real-Time Community Detection in Large Social Networks on a Single Laptop.
ICML 2016 Workshop on Data-Efficient Machine Learning (24 June 2016) Website: https://sites.google.com/site/dataefficientml Submission deadline: 1 May 2016 Submission website: https://easychair.org/conferences/?conf=deml2016 1. Call for Papers We invite researchers to submit their recent work on the development and analysis of methods leading towards more data-efficient machine learning. A submission should take the form of an extended abstract of 2 pages in… Read more »
Lucas defended his PhD on March 10. Congratulations, Dr. Carstens!
Marc Deisenroth is a recipient of a Microsoft Research PhD Scholarship. The scholarship provides four-year funding for a PhD student working in the field of machine learning. More information can be found here.
Marc Deisenroth, Lecturer in Statistical Machine Learning in the Department of Computing, Imperial College London, is a recipient of a prestigious Google Faculty Research Award for 2016. The one-year award supports the work of world-class, permanent faculty members at top universities around the world with the aim of advancing cutting-edge research in computer science, engineering… Read more »
Doniyor’s paper on Bayesian optimization for biological processes has been accepted at ESCAPE 26. Congratulations! Doniyor Ulmasov, Caroline Baroukh, Benoit Chachuat, Marc P. Deisenroth, Ruth Misener: Bayesian Optimization with Dimension Scheduling: Application to Biological Systems Proceedings of the European Symposium on Computer Aided Process Engineering, June 2016
Simon Olofsson from Uppsala University joins the lab for a few months to work on his MSc project. Welcome!
Hugh Salimbeni and Sanket Kamthe are joining the lab. Welcome!
Our paper on Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin has been accepted at HUMANOIDS 2015. Congratulations, Roberto!
Our paper on learning deep dynamical models from image pixels will be published at SYSID. Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth Learning Deep Dynamical Models From Image Pixels IFAC Symposium on System Identification 2015 Congratulations, Niklas!
Our paper got accepted at Annals in Mathematics an AI. Congratulations, Roberto! R Calandra, A Seyfarth, J Peters, MP Deisenroth: Bayesian Optimization for Learning Gaits under Uncertainty To appear in Annals of Mathematics and Artificial Intelligence
Our paper was accepted at ICML. MP Deisenroth and JW Ng Distributed Gaussian Processes International Conference on Machine Learning, 2015
The MLSS slides of the GP tutorial are online now. [pdf]
Marc Deisenroth will give a tutorial on Gaussian Processes for Big Data Problems at the Machine Learning Summer School at Chalmers University (Gothenburg)
Gaussian process regression for really large data sets. The model is conceptually simple, and it can be applied to distributed systems. Marc Peter Deisenroth, Jun Wei Ng Distributed Gaussian Processes arXiv pre-print Abstract: We propose the generalised Bayesian Committee Machine (gBMC), a practical and scalable hierarchical Gaussian process model for large-scale distributed non-parametric regression. The… Read more »
How can an autonomous agent learn low-level closed-loop controllers from video streams? Check out our paper From Pixels to Torques: Policy Learning using Deep Dynamical Models for an idea.
Ben‘s article on Tackling Social Media Spam appeared in Digital Marketing Magazine
Check out our arXiv paper on Learning deep dynamical models from image pixels
Ben Chamberlain has been awarded an Industrial PhD Fellowship from the Royal Commission for the Exhibition of 1851. Congratulations!
Adrian Millea and Ben Chamberlain are joining the lab. Welcome!
Marc Deisenroth has been awarded an Imperial College Junior Research Fellowship on Robot Learning and Control from High-Dimensional Sensory Inputs with Application to Neurotechnology, sponsored by Aldo Faisal.
Our NIPS Workshop on Novel Trends and Applications in Reinforcement Learning is accepted. The objective of the workshop is to provide a platform for researchers from various areas (e.g., deep learning, game theory, robotics, computational neuroscience, information theory, Bayesian modelling) to disseminate and exchange ideas, evaluating their advantages and caveats. More details are here: http://tcrl14.wordpress.com/
Our paper Multi-Task Policy Search for Robotics received the Best Cognitive Robotics Paper Award at ICRA 2014. Marc P. Deisenroth, Peter Englert, Jan Peters, Dieter Fox Multi-Task Policy Search for Robotics Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014
The shocking news this morning was that Mike Stilman (1981-2014), a young robotics professor at Georgia Tech who just got tenured, passed away. Official Georgia Tech Statement
Sanket’s paper has been accepted at ICASSP 2014. Congratulations! Sanket Kamthe, Jan Peters, and Marc P. Deisenroth Multi-Modal Filtering for Non-linear Estimation International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014
Nooshin’s paper has been accepted at AISTATS 2014. Congratulations! Nooshin HajiGhassemi and Marc P. Deisenroth Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
We got three papers accepted at ICRA 2014 (acceptance rate 48%). Well done everybody! Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois Knoll, Jan Peters, and Marc P. Deisenroth Policy Search For Learning Robot Control Using Sparse Data. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA… Read more »
Our paper on Bayesian Gait Optimization for Bipedal Locomotion has been accepted at the Learning and Intelligent Optimization (LION) conference. Most credit goes to Roberto Calandra. Roberto Calandra, Nakul Gopalan, Andre Seyfarth, Jan Peters, and Marc P. Deisenroth. Bayesian Gait Optimization for Bipedal Locomotion. In Proceedings of the Learning and Intelligent Optimization Conference (LION), 2014.
Lucas got accepted at the Machine Learning Summer School (MLSS 2014) in Iceland. Congratulations!
Marc Deisenroth received one of the 100 Best Reviewer Awards at NIPS 2013. NIPS 2013 had 1200 reviewers.
Our paper “Gaussian Processes for Data-Efficient Learning in Robotics and Control” got accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (in the special issue on Bayesian Nonparametrics). The paper is available (Open Access).
Tony O’Hagan interviews Dennis Lindley for the Royal Statistical Society’s Bayes 250 Conference held in June 2013. They discuss the Bayesian paradigm. Very interesting.
I co-organize a NIPS-2013 workshop on Advances in Machine Learning for Sensorimotor Control