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20122022
most citedImproving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry

49 citations · 173 across the 21 of their papers we have counts for

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19 papers · 1 filter

stat.ML2021

The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

Arno Blaas, Stephen J. Roberts

It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited…

stat.ML2019

Implicit Priors for Knowledge Sharing in Bayesian Neural Networks

Jack K Fitzsimons, Sebastian M Schmon, Stephen J Roberts

Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties l…

stat.ML201918 cited

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo

Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham +1

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold H…

stat.ML2019

Adaptive Configuration Oracle for Online Portfolio Selection Methods

Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts

Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which…

stat.ML2019

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

Binxin Ru, Ahsan S. Alvi, Vu Nguyen +2

Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continu…

stat.ML201919 cited

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Ahsan S. Alvi, Binxin Ru, Jan Calliess +2

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead…