Sustainability and Machine Learning Group
Sustainability and Machine Learning Group
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Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources
Sahan Bulathwela
,
Marı́a Pérez-Ortiz
,
Emine Yilmaz
,
John Shawe-Taylor
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Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo …
James T. Wilson
,
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc P. Deisenroth
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GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing …
Vincent Dutordoir
,
Hugh Salimbeni
,
Eric Hambro
,
John McLeod
,
Felix Leibfried
,
Artem Artemev
,
Mark van der Wilk
,
James Hensman
,
Marc P. Deisenroth
,
ST John
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Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Christiana de Farias
,
Naresh Marturi
,
Rustam Stolkin
,
Yasemin Bekiroğlu
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Learning PAC-Bayes Priors for Probabilistic Neural Networks
Marı́a Pérez-Ortiz
,
Omar Rivasplata
,
Benjamin Guedj
,
Matthew Gleeson
,
Jingyu Zhang
,
John Shawe-Taylor
,
Miroslaw Bober
,
Josef Kittler
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Seasonal Arctic sea ice forecasting with probabilistic deep learning
Tom R Andersson
,
J Scott Hosking
,
Maria Perez-Ortiz
,
Brooks Paige
,
Andrew Elliott
,
Chris Russell
,
Stephen Law
,
Daniel C Jones
,
Jeremy Wilkinson
,
Tony Phillips
,
others
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Tighter Risk Certificates for Neural Networks
Marı́a Pérez-Ortiz
,
Omar Rivasplata
,
John Shawe-Taylor
,
Csaba Szepesvári
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High-Dimensional Bayesian Optimization with Manifold Gaussian Processes
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven …
Riccardo Moriconi
,
Marc Deisenroth
,
K. S. Sesh Kumar
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Code
A Foliated View of Transfer Learning
Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to …
Janith Petangoda
,
Nick A. M. Monk
,
Marc Deisenroth
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Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters …
Samuel Cohen
,
Michael Arbel
,
Marc Deisenroth
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