
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperp...
01/29/2019 ∙ by Ahsan S. Alvi, et al. ∙ 14 ∙ shareread it

Adaptive Configuration Oracle for Online Portfolio Selection Methods
Financial markets are complex environments that produce enormous amounts...
08/22/2019 ∙ by Favour M. Nyikosa, et al. ∙ 9 ∙ shareread it

Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of blackbox problems that comprise both continuo...
06/20/2019 ∙ by Binxin Ru, et al. ∙ 5 ∙ shareread it

Optimization, fast and slow: optimally switching between local and Bayesian optimization
We develop the first Bayesian Optimization algorithm, BLOSSOM, which sel...
05/22/2018 ∙ by Mark McLeod, et al. ∙ 2 ∙ shareread it

Learning from lions: inferring the utility of agents from their trajectories
We build a model using Gaussian processes to infer a spatiotemporal vec...
09/07/2017 ∙ by Adam D. Cobb, et al. ∙ 0 ∙ shareread it

Optimal client recommendation for market makers in illiquid financial products
The process of liquidity provision in financial markets can result in pr...
04/27/2017 ∙ by Dieter Hendricks, et al. ∙ 0 ∙ shareread it

Distribution of Gaussian Process Arc Lengths
We present the first treatment of the arc length of the Gaussian Process...
03/23/2017 ∙ by Justin D. Bewsher, et al. ∙ 0 ∙ shareread it

Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for blackbox function...
03/13/2017 ∙ by Mark McLeod, et al. ∙ 0 ∙ shareread it

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 ...
10/09/2015 ∙ by YvesLaurent Kom Samo, et al. ∙ 0 ∙ shareread it

A Variational Bayesian StateSpace Approach to Online PassiveAggressive Regression
Online PassiveAggressive (PA) learning is a class of online marginbase...
09/08/2015 ∙ by Arnold Salas, et al. ∙ 0 ∙ shareread it

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic mod...
11/03/2014 ∙ by Tom Gunter, et al. ∙ 0 ∙ shareread it

Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for con...
11/02/2014 ∙ by Chris Lloyd, et al. ∙ 0 ∙ shareread it

Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
We introduce a means of automating machine learning (ML) for big data ta...
07/30/2014 ∙ by Thomas Nickson, et al. ∙ 0 ∙ shareread it

Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point ...
07/25/2014 ∙ by Tom Gunter, et al. ∙ 0 ∙ shareread it

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...
11/18/2013 ∙ by JanPeter Calliess, et al. ∙ 0 ∙ shareread it

Bayesian onemode projection for dynamic bipartite graphs
We propose a Bayesian methodology for onemode projecting a bipartite ne...
12/12/2012 ∙ by Ioannis Psorakis, et al. ∙ 0 ∙ shareread it

Inferring agent objectives at different scales of a complex adaptive system
We introduce a framework to study the effective objectives at different ...
12/04/2017 ∙ by Dieter Hendricks, et al. ∙ 0 ∙ shareread it

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
In this paper we use Gaussian Process (GP) regression to propose a novel...
05/02/2017 ∙ by Syed Ali Asad Rizvi, et al. ∙ 0 ∙ shareread it

Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dyn...
03/09/2018 ∙ by Favour M. Nyikosa, et al. ∙ 0 ∙ shareread it

Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine lear...
03/28/2018 ∙ by Zhikuan Zhao, et al. ∙ 0 ∙ shareread it

LossCalibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
05/10/2018 ∙ by Adam D. Cobb, et al. ∙ 0 ∙ shareread it

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...
02/22/2018 ∙ by Adam D. Cobb, et al. ∙ 0 ∙ shareread it

BCCNet: Bayesian classifier combination neural network
Machine learning research for developing countries can demonstrate clear...
11/29/2018 ∙ by Olga Isupova, et al. ∙ 0 ∙ shareread it

Automated bird sound recognition in realistic settings
We evaluated the effectiveness of an automated bird sound identification...
09/04/2018 ∙ by Timos Papadopoulos, et al. ∙ 0 ∙ shareread it

SemiUnsupervised Learning with Deep Generative Models: Clustering and Classifying using UltraSparse Labels
We introduce semiunsupervised learning, an extreme case of semisupervi...
01/24/2019 ∙ by Matthew Willetts, et al. ∙ 0 ∙ shareread it

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Unstructured data from diverse sources, such as social media and aerial ...
04/05/2019 ∙ by Edwin Simpson, et al. ∙ 0 ∙ shareread it

A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback
Computing Nash equilibria for strategic multiagent systems is challengi...
11/15/2018 ∙ by Anup Aprem, et al. ∙ 0 ∙ shareread it
Stephen J. Roberts
is this you? claim profile
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.