
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
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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 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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Learning from lions: inferring the utility of agents from their trajectories
We build a model using Gaussian processes to infer a spatiotemporal vec...
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Optimal client recommendation for market makers in illiquid financial products
The process of liquidity provision in financial markets can result in pr...
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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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Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for blackbox function...
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pMarkov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting
In this paper we introduce a novel online time series forecasting model ...
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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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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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Stochastic processes and feedbacklinearisation for online identification and Bayesian adaptive control of fullyactuated mechanical systems
This work proposes a new method for simultaneous probabilistic identific...
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Bayesian onemode projection for dynamic bipartite graphs
We propose a Bayesian methodology for onemode projecting a bipartite ne...
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Inferring agent objectives at different scales of a complex adaptive system
We introduce a framework to study the effective objectives at different ...
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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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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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LossCalibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
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Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent d...
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BCCNet: Bayesian classifier combination neural network
Machine learning research for developing countries can demonstrate clear...
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Automated bird sound recognition in realistic settings
We evaluated the effectiveness of an automated bird sound identification...
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SemiUnsupervised Learning with Deep Generative Models: Clustering and Classifying using UltraSparse Labels
We introduce semiunsupervised learning, an extreme case of semisupervi...
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Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Unstructured data from diverse sources, such as social media and aerial ...
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A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback
Computing Nash equilibria for strategic multiagent systems is challengi...
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Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
We introduce a recent symplectic integration scheme derived for solving ...
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Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Bayesian interpretations of neural network have a long history, dating b...
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Zeroshot and fewshot time series forecasting with ordinal regression recurrent neural networks
Recurrent neural networks (RNNs) are stateoftheart in several sequent...
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Stephen J. Roberts
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Professor of Machine Learning & Director, OxfordMan Institute of Quantitative Finance at University of Oxford since 1999, CSO & cofounder at Mind Foundry since 2016, Project Scientist of the Machine Learning Research Group, Professorial Fellow of Somerville College, former Director of the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS) and Director of the OxfordMan Institute.