
Knowing The What But Not The Where in Bayesian Optimization
Bayesian optimization has demonstrated impressive success in finding the...
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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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Automated Model Selection with Bayesian Quadrature
We present a novel technique for tailoring Bayesian quadrature (BQ) to m...
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Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
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AReS and MaRS  Adversarial and MMDMinimizing Regression for SDEs
Stochastic differential equations are an important modeling class in man...
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ODIN: ODEInformed Regression for Parameter and State Inference in TimeContinuous Dynamical Systems
Parameter inference in ordinary differential equations is an important p...
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Adaptive Configuration Oracle for Online Portfolio Selection Methods
Financial markets are complex environments that produce enormous amounts...
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Bayesian Optimization for Iterative Learning
The success of deep (reinforcement) learning systems crucially depends o...
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Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of blackbox problems that comprise both continuo...
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Optimization, fast and slow: optimally switching between local and Bayesian optimization
We develop the first Bayesian Optimization algorithm, BLOSSOM, which sel...
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Contextual Policy Optimisation
Policy gradient methods have been successfully applied to a variety of r...
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Distributionally Ambiguous Optimization Techniques in Batch Bayesian Optimization
We propose a novel, theoreticallygrounded, acquisition function for bat...
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Distribution of Gaussian Process Arc Lengths
We present the first treatment of the arc length of the Gaussian Process...
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Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of b...
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Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for blackbox function...
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Alternating Optimisation and Quadrature for Robust Control
Bayesian optimisation has been successfully applied to a variety of rein...
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Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the...
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Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
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Blitzkriging: Kroneckerstructured Stochastic Gaussian Processes
We present Blitzkriging, a new approach to fast inference for Gaussian p...
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A Variational Bayesian StateSpace Approach to Online PassiveAggressive Regression
Online PassiveAggressive (PA) learning is a class of online marginbase...
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FrankWolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference pro...
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Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithm...
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Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic mod...
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Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for con...
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Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
We introduce a means of automating machine learning (ML) for big data ta...
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Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point ...
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Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
In this paper we use Gaussian Process (GP) regression to propose a novel...
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Gaussian Process Regression for Insitu Capacity Estimation of Lithiumion Batteries
Accurate onboard capacity estimation is of critical importance in lithi...
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Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dyn...
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Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine lear...
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Battery health prediction under generalized conditions using a Gaussian process transition model
Accurately predicting the future health of batteries is necessary to ens...
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Michael A. Osborne
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Dyson Associate Professor in Machine Learning at University of Oxford