730 citations · 2.8k across the 62 of their papers we have counts for
10 papers · 1 filter
Batch Selection for Parallelisation of Bayesian Quadrature
Ed Wagstaff, Saad Hamid, Michael Osborne
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive d…
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
Francois-Xavier Briol, Chris J. Oates, Mark Girolami +2
This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to…
Intersectionality: Multiple Group Fairness in Expectation Constraints
Jack Fitzsimons, Michael Osborne, Stephen Roberts
Group fairness is an important concern for machine learning researchers, developers, and regulators. However, the strictness to which models must be constrained to be considered fa…
Efficiently measuring a quantum device using machine learning
D. T. Lennon, H. Moon, L. C. Camenzind +6
Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases…
Battery health prediction under generalized conditions using a Gaussian process transition model
Robert R. Richardson, Michael A. Osborne, David A. Howey
Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments. T…
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Mark McLeod, Michael A. Osborne, Stephen J. Roberts
We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step. Thi…
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Supratik Paul, Michael A. Osborne, Shimon Whiteson
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical set…
Quantum algorithms for training Gaussian Processes
Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne +2
Gaussian processes (GPs) are important models in supervised machine learning. Training in Gaussian processes refers to selecting the covariance functions and the associated paramet…
VBALD - Variational Bayesian Approximation of Log Determinants
Diego Granziol, Edward Wagstaff, Bin Xin Ru +2
Evaluating the log determinant of a positive definite matrix is ubiquitous in machine learning. Applications thereof range from Gaussian processes, minimum-volume ellipsoids, metri…
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting
Pengfei Zhang, Ido Nevat, Gareth W. Peters +2
We address the two fundamental problems of spatial field reconstruction and sensor selection in het- erogeneous sensor networks. We consider the case where two types of sensors are…