Cikkek nyilvánosan hozzáférhető megbízással - Michael A OsborneTovábbi információ
Valahol hozzáférhető: 51
Gaussian process regression for forecasting battery state of health
RR Richardson, MA Osborne, DA Howey
Journal of Power Sources 357, 209-219, 2017
Megbízások: UK Engineering and Physical Sciences Research Council
A Gaussian process framework for modelling stellar activity signals in radial velocity data
V Rajpaul, S Aigrain, MA Osborne, S Reece, S Roberts
Monthly Notices of the Royal Astronomical Society 452 (3), 2269-2291, 2015
Megbízások: National Research Foundation, South Africa
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
RR Richardson, CR Birkl, MA Osborne, DA Howey
IEEE Transactions on Industrial Informatics 15 (1), 127-138, 2018
Megbízások: UK Engineering and Physical Sciences Research Council
Probabilistic numerics and uncertainty in computations
P Hennig, MA Osborne, M Girolami
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
Megbízások: German Research Foundation, UK Engineering and Physical Sciences Research …
On the limitations of representing functions on sets
E Wagstaff, F Fuchs, M Engelcke, I Posner, MA Osborne
International Conference on Machine Learning, 6487-6494, 2019
Megbízások: UK Engineering and Physical Sciences Research Council, UK Science and …
Battery health prediction under generalized conditions using a Gaussian process transition model
RR Richardson, MA Osborne, DA Howey
Journal of Energy Storage 23, 320-328, 2019
Megbízások: UK Engineering and Physical Sciences Research Council
Probabilistic integration
FX Briol, CJ Oates, M Girolami, MA Osborne, D Sejdinovic
Statistical Science 34 (1), 1-22, 2019
Megbízások: US National Science Foundation, Australian Research Council, UK Engineering …
GLASSES: Relieving the myopia of Bayesian optimisation
J González, M Osborne, N Lawrence
Artificial Intelligence and Statistics, 790-799, 2016
Megbízások: UK Biotechnology and Biological Sciences Research Council
Variational inference for Gaussian process modulated Poisson processes
C Lloyd, T Gunter, M Osborne, S Roberts
International Conference on Machine Learning, 1814-1822, 2015
Megbízások: UK Research & Innovation
Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees
FX Briol, C Oates, M Girolami, MA Osborne
Advances in Neural Information Processing Systems 28, 2015
Megbízások: UK Engineering and Physical Sciences Research Council, European Commission
Machine learning enables completely automatic tuning of a quantum device faster than human experts
H Moon, DT Lennon, J Kirkpatrick, NM van Esbroeck, LC Camenzind, ...
Nature communications 11 (1), 4161, 2020
Megbízások: Swiss National Science Foundation, UK Engineering and Physical Sciences …
Efficiently measuring a quantum device using machine learning
DT Lennon, H Moon, LC Camenzind, L Yu, DM Zumbühl, GAD Briggs, ...
npj Quantum Information 5 (1), 79, 2019
Megbízások: Swiss National Science Foundation, UK Engineering and Physical Sciences …
Radial bayesian neural networks: Beyond discrete support in large-scale bayesian deep learning
S Farquhar, MA Osborne, Y Gal
International Conference on Artificial Intelligence and Statistics, 1352-1362, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Universal approximation of functions on sets
E Wagstaff, FB Fuchs, M Engelcke, MA Osborne, I Posner
Journal of Machine Learning Research 23 (151), 1-56, 2022
Megbízások: UK Engineering and Physical Sciences Research Council
Advanced artificial agents intervene in the provision of reward
M Cohen, M Hutter, M Osborne
AI magazine 43 (3), 282-293, 2022
Megbízások: Australian Research Council
Data-driven energy management system with Gaussian process forecasting and MPC for interconnected microgrids
LK Gan, PF Zhang, J Lee, MA Osborne, DA Howey
IEEE Transactions on Sustainable Energy 12 (1), 695-704, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Prediction of tidal currents using Bayesian machine learning
D Sarkar, MA Osborne, TAA Adcock
Ocean Engineering 158, 221-231, 2018
Megbízások: UK Engineering and Physical Sciences Research Council
Bayesian optimization for iterative learning
V Nguyen, S Schulze, M Osborne
Advances in Neural Information Processing Systems 33, 9361-9371, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Robust multi-objective bayesian optimization under input noise
S Daulton, S Cakmak, M Balandat, MA Osborne, E Zhou, E Bakshy
International Conference on Machine Learning, 4831-4866, 2022
Megbízások: US Department of Defense
Spatial field reconstruction and sensor selection in heterogeneous sensor networks with stochastic energy harvesting
P Zhang, I Nevat, GW Peters, F Septier, MA Osborne
IEEE Transactions on Signal Processing 66 (9), 2245-2257, 2018
Megbízások: National Research Foundation, Singapore
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