S. Roberts

发表

Gaussian Processes for Global Optimization

Michael A. Osborne, R. Garnett, S. Roberts, 2008 .

Learning interaction dynamics with coupled hidden Markov models

S. Roberts, I. Rezek, P. Sykacek, 2000 .

Bayesian methods for autoregressive models

S. Roberts, W. Penny, 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

Bayesian Inference of Log Determinants pdf

Stephen J. Roberts, Michael A. Osborne, Maurizio Filippone, 2017, UAI.

Blitzkriging: Kronecker-structured Stochastic Gaussian Processes

Michael A. Osborne, S. Roberts, T. Nickson, 2015, 1510.07965.

Non-stationary independent component analysis

S. Roberts, R. Everson, 1999 .

Bayesian multivariate autoregressive models with structured priors

S. Roberts, W. Penny, 2002 .

Bayesian Combination of Multiple , Imperfect Classifiers

C. Lintott, S. Roberts, Edwin Simpson, 2011 .

The Near Constant Acceleration Gaussian Process Kernel for Tracking

Steven Reece, Stephen J. Roberts, S. Roberts, 2010, IEEE Signal Processing Letters.

Novelty detection using extreme value statistics

S. Roberts, 1999 .

Variational Bayes for 1-dimensional Mixture Models

S. Roberts, W. Penny, 2000 .

Active Learning of Model Evidence Using Bayesian Quadrature

Carl E. Rasmussen, Roman Garnett, Zoubin Ghahramani, 2012, NIPS.

Bayesian optimization for sensor set selection

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2010, IPSN '10.

Generalized Spectral Kernels

Yves-Laurent Kom Samo, S. Roberts, 2015, 1506.02236.

Estimation of coupled hidden Markov models with application to biosignal interaction modelling

S. Roberts, I. Rezek, 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

Gaussian Processes for Prediction

Stephen J. Roberts, Michael A. Osborne, Michael A. Osborne, 2007 .

Independent Components Analysis

Richard M. Everson, Stephen J. Roberts, S. Roberts, 2000, Artificial Neural Networks in Biomedicine.

Variational Bayes for non-Gaussian autoregressive models

S. Roberts, W. Penny, 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

Independent Component Analysis: Principles and Practice

Richard M. Everson, Stephen J. Roberts, S. Roberts, 2001 .

EXTREME VALUE STATISTICS FOR NOVELTY DETECTION IN BIOMEDICAL DATA PROCESSING

S. Roberts, 2000 .

Chapter 1 Dynamic Bayesian Combination of Multiple Imperfect Classifiers

S. Roberts, Arfon M. Smith, Edwin Simpson, 2012 .

Regularisation of RBF-networks with the Bayesian evidence scheme

S. Roberts, D. Husmeier, 1999 .

Minimum entropy data partitioning

S. Roberts, I. Rezek, R. Everson, 1999 .

VARIATIONAL BAYESIAN LEARNING FOR WAVELET INDEPENDENT COMPONENT ANALYSIS

I. Daubechies, S. Roberts, E. Roussos, 2005 .

Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control*

Carl E. Rasmussen, Stephen J. Roberts, Jan M. Maciejowski, 2018, 2018 European Control Conference (ECC).

O Con: What is State-of-the-Art Anyway?

Philip J. Ball, S. Roberts, 2021 .

A Gaussian process framework for modelling stellar activity signals in radial velocity data

Steven Reece, Stephen J. Roberts, Michael A. Osborne, 2015, 1506.07304.

A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy

S. Aigrain, University of Exeter, N. P. Gibson, 2011, 1109.3251.

Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks

S. Roberts, R. Dybowski, 2001 .

Independent Data Decomposition

S. Roberts, R. Choudrey, 2022 .

Automated assessment of vigilance using committees of radial basis function analysers

S. Roberts, I. Rezek, R. Everson, 2000 .

Cycle-by-Cycle Variations in Exhaust Temperatures Using Thermocouple Compensation Techniques

S. Roberts, A. Swain, R. Stone, 2006 .

Augmenting machine learning photometric redshifts with Gaussian mixture models

S. Roberts, M. Jarvis, C. Schreiber, 2020, 2009.01952.

IncreasingDepth of Anesthesia Assessment Better Higher-Order Statistics with Generative Polyspectral Models

S. Roberts, I. Rezek, R. Conradt, 2007 .

Increasing the depth of anesthesia assessment.

S. Roberts, I. Rezek, R. Conradt, 2007, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

Precise time series photometry for the Kepler-2.0 mission

S. Roberts, M. Irwin, S. Hodgkin, 2014, 1412.6304.

Coordination vs . information in multi-agent decision processes

S. Roberts, M. Kaufman, 2010 .

A Bayesian take on option pricing with Gaussian processes

S. Roberts, Martin Tegner, 2021, 2112.03718.

Unsupervised Learning of Ranking Functions for High-Dimensional Data

S. Roberts, S. Mukherjee, 2022 .

Robust cell nuclei segmentation using statistical modelling

Stephen J. Roberts, Anil A. Bharath, Theodoros Mouroutis, 1998 .

A flexible non-linearity and decorrelating manifold approach to ICA

Richard M. Everson, Stephen J. Roberts, S. Roberts, 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

from limited, noisy data

S. Roberts, R. Everson, 1999 .

Instantaneous Exhaust Temperature Measurements Using Thermocouple Compensation Techniques

Stephen J. Roberts, M. L. G. Oldfield, Richard Stone, 2004 .

Improving photometric redshift estimation using GPz: size information, post processing and improved photometry

Stephen J. Roberts, Matt J. Jarvis, S. Roberts, 2017, 1712.02256.

Sequential Bayesian Decision Making for Multi-Armed Bandit

S. J. Roberts, S. Roberts, R. McInerney, 2010 .

Local Quantum Computing for Fast Probably MAP Inference in Graphical Models

S. Roberts, I. Rezek, C. Fox, 2007 .

Provenance Network Analytics - An approach to data analytics using data provenance

Stephen Roberts, Luc Moreau, Trung Dong Huynh, 2018, Data Min. Knowl. Discov..

Sequential Gaussian Process Prediction in the Presence of Changepoints or Faults

Michael A. Osborne, R. Garnett, S. Roberts, 2009 .

Variational Bayes for Generalized

William D. Penny, Stephen J. Roberts, S. Roberts, 2002 .

Toward an Information Theoretic Approach to Managing Multiple Decision Makers

S. Roberts, Edwin Simpson, S. Reece, 2012 .

MiDGaP : Mixture Density Gaussian Processes

S. Roberts, Jaleh Zand, 2017 .

Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 0803

S. Roberts, Seung Min Lee, 2009 .

ENSEMBLE HIDDEN MARKOVMODELS FOR BIOSIGNAL ANALYSIS

S. Roberts, I. Rezek, 2008 .

Gaussian Process Segmentation of Co-Moving Animals

Steven Reece, Stephen J. Roberts, Richard P. Mann, 2011 .

Adaptive Bayesian Optimisation for Online Portfolio Selection

Stephen J. Roberts, Favour M. Nyikosa, Michael A. Osborne, 2015 .

Shape representation with firing intersections

M. Brady, S. Roberts, J. Yoon, 2009 .

Compact Hough Transform and a Maximum Likelihood Approach toCell Nuclei

S. Roberts, A. Bharath, DetectionTheodoros Mouroutis, 2007 .

Robust Cell Nuclei Segmentation Using Statistical ModellingTheodoros

S. Roberts, A. Bharath, Mouroutis, 1998 .

Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring.

Glen Wright Colopy, S. Roberts, D. Clifton, 2018, IEEE journal of biomedical and health informatics.

Scale-space unsupervised cluster analysis

S. Roberts, 1996, Proceedings of 13th International Conference on Pattern Recognition.

Mixtures of Independent Component Analysers

William D. Penny, Stephen J. Roberts, S. Roberts, 2001, ICANN.

Determining intent using hard/soft data and Gaussian process classifiers

Steven Reece, Stephen J. Roberts, David Nicholson, 2011, 14th International Conference on Information Fusion.

1 Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks a

S. Roberts, R. Dybowski, 2000 .

AtomicOrchid: Human-Agent Collectives to the Rescue (Demonstration)

Sarvapali D. Ramchurn, Nicholas R. Jennings, Tom Rodden, 2014 .

Optimal pricing in black box producer-consumer Stackelberg games using revealed preference feedback

Stephen J. Roberts, Anup Aprem, S. Roberts, 2021, Neurocomputing.

A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback pdf

Stephen J. Roberts, Anup Aprem, S. Roberts, 2018, Comput. J..

Intersectionality: Multiple Group Fairness in Expectation Constraints pdf

Stephen J. Roberts, Michael A. Osborne, Jack K. Fitzsimons, 2018, ArXiv.

Gaussian Processes for Global Optimization

Michael A. Osborne, R. Garnett, S. Roberts, 2008 .

Latent Point Process Allocation

Stephen J. Roberts, Michael A. Osborne, Chris M. Lloyd, 2016, AISTATS.

Experiments with an EEG-based computer interface

Stephen J. Roberts, William D. Penny, S. Roberts, 1999 .

Astrophysically robust systematics removal using variational inference: application to the first month of Kepler data

S. Roberts, S. Reece, S. Aigrain, 2013, 1308.3644.

Image processing methods using ICA mixture models

Richard M. Everson, Stephen J. Roberts, T. W. Lee, 2001 .

Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis

S. Roberts, W. Fruehwirt, Leonhard Hochfilzer, 2020 .

Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness pdf

Stephen J. Roberts, Michael A. Osborne, Jack K. Fitzsimons, 2018, ArXiv.

Acoustic emission in aerospace structures

S. Roberts, P. Wells, W. A. Wright, 1999 .

Sequential Dynamic Classification Using Latent Variable Models Technical Report PARG 08-02

S. Roberts, Seung Min Lee, 2008 .

Latent Poisson Process Allocation

Michael A. Osborne, S. Roberts, T. Nickson, 2016 .

Statistics of Stellar Variability in Kepler Data with ARC Systematics Removal

S. Roberts, S. Aigrain, A. McQuillan, 2011, Proceedings of the International Astronomical Union.

Maritime abnormality detection using Gaussian processes

S. Roberts, I. Rezek, S. Reece, 2014, Knowledge and Information Systems.

Multi-Armed Bandit Bayesian Decision Making

Stephen Roberts, R. E. McInerney, S. Roberts, 2010 .

Latent class and trait models for data classification and visualisation

Richard M. Everson, Stephen J. Roberts, Mark Girolami, 2001 .

IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance (RISE), Benicassim, Spain, January 2008

David J. Hand, Erol Gelenbe, Niall M. Adams, 2008 .

Variational Mixture of Bayesian Independent Component Analyzers

Stephen J. Roberts, Rizwan Choudrey, S. Roberts, 2003, Neural Computation.

ICA: model order selection and dynamic source models

Richard M. Everson, Stephen J. Roberts, William D. Penny, 2001 .

Particle Filters for Non-Stationary ICA

Richard M. Everson, Stephen J. Roberts, S. Roberts, 2000 .

A Disaster Response System based on Human-Agent Collectives

Tom Rodden, Luc Moreau, Wenchao Jiang, 2015, J. Artif. Intell. Res..

Human–agent collaboration for disaster response

Steven Reece, Sarvapali D. Ramchurn, Feng Wu, 2015, Autonomous Agents and Multi-Agent Systems.

Gene Microarray Analysis Using Angular Distribution Decomposition

Karen Lees, Stephen J. Roberts, Pari Skamnioti, 2007, J. Comput. Biol..

Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo

Stephen J. Roberts, Christopher C. Holmes, Dave Denison, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis

Stephen J. Roberts, Nauman Shah, S. Roberts, 2013 .

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2014, NIPS.

Bayesian Approaches to Gaussian Mixture Modeling

William D. Penny, Stephen J. Roberts, Dirk Husmeier, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

A Probabilistic Resource Allocating Network for Novelty Detection

Stephen J. Roberts, Lionel Tarassenko, L. Tarassenko, 1994, Neural Computation.

A tutorial on variational Bayesian inference

Stephen J. Roberts, Charles W. Fox, S. Roberts, 2012, Artificial Intelligence Review.

Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling

Stephen J. Roberts, Matthew Dyson, John Q. Gan, 2009, Neural Networks.

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation

Stephen J. Roberts, Peter Sykacek, Maria Stokes, 2004, IEEE Transactions on Biomedical Engineering.

Adaptive Classification by Variational Kalman Filtering

Stephen J. Roberts, Peter Sykacek, S. Roberts, 2002, NIPS.

An introduction to Gaussian processes for the Kalman filter expert

Steven Reece, Stephen J. Roberts, S. Roberts, 2010, 2010 13th International Conference on Information Fusion.

Robust Measurement Validation in Target Tracking Using Geometric Structure

Stephen J. Roberts, Ji Won Yoon, S. Roberts, 2010, IEEE Signal Processing Letters.

Hierarchy, priors and wavelets: structure and signal modelling using ICA

Stephen J. Roberts, Evangelos Roussos, Rizwan Choudrey, 2004, Signal Process..

Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis

Stephen J. Roberts, Rizwan Choudrey, S. Roberts, 2004, Deterministic and Statistical Methods in Machine Learning.

Data decomposition using independent component analysis with prior constraints

Stephen J. Roberts, Rizwan Choudrey, S. Roberts, 2003, Pattern Recognit..

Independent Component Analysis: Source Assessment Separation, a Bayesian Approach

S. J. Roberts, S. Roberts, 1998 .

Variational Bayes for generalized autoregressive models

William D. Penny, Stephen J. Roberts, S. Roberts, 2002, IEEE Trans. Signal Process..

Hidden Markov independent components for biosignal analysis

Richard M. Everson, Stephen J. Roberts, William D. Penny, 2000 .

Inferring social network structure in ecological systems from spatio-temporal data streams

Iead Rezek, Ioannis Psorakis, Ben C Sheldon, 2012, Journal of The Royal Society Interface.

Parametric and non-parametric unsupervised cluster analysis

Stephen J. Roberts, S. Roberts, 1997, Pattern Recognit..

Sequential Bayesian Prediction in the Presence of Changepoints and Faults

Steven Reece, Roman Garnett, Stephen J. Roberts, 2010, Comput. J..

Multi-sensor fault recovery in the presence of known and unknown fault types

Steven Reece, Stephen J. Roberts, David Nicholson, 2009, 2009 12th International Conference on Information Fusion.

Sequential Bayesian prediction in the presence of changepoints

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2009, ICML '09.

Learning from data streams with concept drift

Roman Garnett, Stephen J. Roberts, R. Garnett, 2008 .

Quantum algorithms for training Gaussian Processes pdf

Stephen J. Roberts, Michael A. Osborne, Joseph Fitzsimons, 2018, Physical Review A.

VBALD - Variational Bayesian Approximation of Log Determinants pdf

Stephen J. Roberts, Diego Granziol, Bin Xin Ru, 2018, ArXiv.

Improved Stochastic Trace Estimation using Mutually Unbiased Bases pdf

Stephen J. Roberts, Michael A. Osborne, Jack K. Fitzsimons, 2016, UAI.

Bayesian one-mode projection for dynamic bipartite graphs pdf

Stephen J. Roberts, Iead Rezek, Ioannis Psorakis, 2012, ArXiv.

Hidden Markov Independent Component Analysis

Richard M. Everson, Stephen J. Roberts, William D. Penny, 2000 .

Depth of anaesthesia assessment with generative polyspectral models

Stephen J. Roberts, Iead Rezek, R. Conradt, 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

Tracking Ica For Eye-Movement Artefact Removal

Richard M. Everson, Stephen J. Roberts, Iead Rezek, 1999 .

Anomaly Detection and Removal Using Non-Stationary Gaussian Processes

Michael A. Osborne, R. Garnett, S. Roberts, 2015, 1507.00566.

Inferring the eigenvalues of covariance matrices from limited, noisy data

Richard M. Everson, Stephen J. Roberts, S. Roberts, 2000, IEEE Trans. Signal Process..

Independent Component Analysis: A Flexible Nonlinearity and Decorrelating Manifold Approach

Richard M. Everson, Stephen J. Roberts, S. Roberts, 1999, Neural Computation.

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Stephen J. Roberts, Bin Xin Ru, Michael A. Osborne, 2019, ICML.

Practical Bayesian Optimization for Variable Cost Objectives

Stephen J. Roberts, Michael A. Osborne, Mark McLeod, 2017, 1703.04335.

Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo

Stephen J. Roberts, Christopher C. Holmes, Dave Denison, 2001, ICANN.

Dynamic Bayesian Combination of Multiple Imperfect Classifiers

Stephen J. Roberts, Edwin Simpson, Arfon Smith, 2012, Decision Making and Imperfection.

Bayesian Quadrature for Ratios

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2012, AISTATS.

Bayesian Quadrature for Ratios: Now With Even More Bayesian Quadrature

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2012 .

On-line novelty detection using the Kalman filter and extreme value theory

Hyoungjoo Lee, Stephen J. Roberts, S. Roberts, 2008, 2008 19th International Conference on Pattern Recognition.

An Anthology of Probabilistic Models for Medical Informatics

Stephen J. Roberts, Richard Dybowski, S. Roberts, 2005 .

Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

Stephen J. Roberts, Andrew Zisserman, Lyndsey C. Pickup, 2007, EURASIP J. Adv. Signal Process..

Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals

Stephen J. Roberts, Iead Rezek, Peter Sykacek, 2003, AISTATS.

Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity pdf

Stephen J. Roberts, Georg Dorffner, Heinrich Garn, 2018, ArXiv.

Bayesian neural networks for classification: how useful is the evidence framework?

William D. Penny, Stephen J. Roberts, S. Roberts, 1999, Neural Networks.

On Optimism in Model-Based Reinforcement Learning pdf

Krzysztof Choromanski, Stephen Roberts, Aldo Pacchiano, 2020, ArXiv.

Ready Policy One: World Building Through Active Learning

Krzysztof Choromanski, Stephen Roberts, Aldo Pacchiano, 2020, ICML.

Distributed Data Fusion

Nicholas R. Jennings, Stephen J. Roberts, Alex Rogers, 2017 .

Rumours and reputation: evaluating multi-dimensional trust within a decentralised reputation system

Steven Reece, Nicholas R. Jennings, Stephen J. Roberts, 2007, AAMAS '07.

Novelty, confidence and errors in connectionist systems

Stephen J. Roberts, William D. Penny, D. Pillot, 1996 .

Bayesian Nonparametrics and Feedback-Linearisation of Discretised Control-Affine Systems

Stephen J. Roberts, Antonis Papachristodoulou, Jan-P. Calliess, 2018, 2018 IEEE Conference on Decision and Control (CDC).

Conservative decision-making and interference in uncertain dynamical systems

Stephen J. Roberts, Michael A. Osborne, Jan-Peter Calliess, 2014 .

Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems pdf

Stephen J. Roberts, Antonis Papachristodoulou, Jan-P. Calliess, 2013, ArXiv.

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment pdf

Stephen Roberts, Jack Parker-Holder, Cong Lu, 2021, ICML.

Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis

Stephen J. Roberts, Iead Rezek, S. Roberts, 2005 .

Effective Diversity in Population-Based Reinforcement Learning

K. Choromanski, S. Roberts, Jack Parker-Holder, 2020, NeurIPS.

Ghost in the time series: no planet for Alpha Cen B

Stephen J. Roberts, Suzanne Aigrain, Vinesh Rajpaul, 2015, 1510.05598.

Gaussian processes for time-series modelling

S Roberts, M Osborne, M Ebden, 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Marginalising over Stationary Kernels with Bayesian Quadrature

Michael A. Osborne, S. Roberts, M. Osborne, 2021, AISTATS.

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo pdf

Stephen J. Roberts, Atilim Gunecs Baydin, Andrew Markham, 2019, ArXiv.

Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits

Stephen Roberts, Vu Nguyen, Jack Parker-Holder, 2020, NeurIPS.

Extreme value statistics for novelty detection in biomedical signal processing

Stephen J. Roberts, S. Roberts, 2000 .

Provenance Network Analytics

S. Roberts, L. Moreau, T. D. Huynh, 2018, Data mining and knowledge discovery.

Online Maritime Abnormality Detection Using Gaussian Processes and Extreme Value Theory

Steven Reece, Stephen J. Roberts, Mark Smith, 2012, 2012 IEEE 12th International Conference on Data Mining.

Bayesian Methods for Image Super-Resolution

Stephen J. Roberts, Andrew Zisserman, Lyndsey C. Pickup, 2009, Comput. J..

Network Analysis on Provenance Graphs from a Crowdsourcing Application

Sarvapali D. Ramchurn, Luc Moreau, Stephen J. Roberts, 2012, IPAW.

Maximum certainty data partitioning

Stephen J. Roberts, Iead Rezek, Richard Everson, 2000, Pattern Recognit..

Towards the automatic analysis of complex human body motions

Andrew Blake, Jens Rittscher, Stephen J. Roberts, 2002, Image Vis. Comput..

Statistics of stellar variability from Kepler. I. Revisiting Quarter 1 with an astrophysically robust systematics correction

Stephen Roberts, Suzanne Aigrain, S. Roberts, 2011, 1111.5580.

Revisiting Design Choices in Offline Model Based Reinforcement Learning

Michael A. Osborne, Philip J. Ball, S. Roberts, 2021, ICLR.

Bayes Consistent Classification of EEG Data by Approximate Marginalization

Stephen J. Roberts, Iead Rezek, Peter Sykacek, 2005 .

A Probabilistic Approach to High-Resolution Sleep Analysis

Stephen J. Roberts, Georg Dorffner, Arthur Flexer, 2001, ICANN.

Short Memories? The Impact of SEC Enforcement on Insider Leakage

S. Roberts, Sid Ghoshal, Martin Bengtzen, 2019, Journal of Law, Finance, and Accounting.

Markov Models for Automated ECG Interval Analysis

Stephen J. Roberts, Lionel Tarassenko, Nicholas P. Hughes, 2003, NIPS.

Inferring agent objectives at different scales of a complex adaptive system

Stephen J. Roberts, Dieter Hendricks, Richard Everett, 2017, 1712.01137.

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination

S. Roberts, Jack Parker-Holder, Jaleh Zand, 2022, AAMAS.

Automated bird sound recognition in realistic settings pdf

Stephen J. Roberts, Timos Papadopoulos, Katherine J. Willis, 2018, ArXiv.

Optimization, fast and slow: optimally switching between local and Bayesian optimization pdf

Stephen J. Roberts, Michael A. Osborne, Mark McLeod, 2018, ICML.

Bayesian Optimization for Dynamic Problems

Stephen J. Roberts, Favour M. Nyikosa, Michael A. Osborne, 2018, 1803.03432.

A tutorial on variational Bayesian inference

S. Roberts, Charles W. Fox, 2011, Artificial Intelligence Review.

Bayesian Time Series Models: Bayesian Gaussian process models for multi-sensor time series prediction

Sarvapali D. Ramchurn, Nicholas R. Jennings, Stephen J. Roberts, 2011 .

Decentralized predictive sensor allocation

Stephen J. Roberts, Mark Briers, Mark Ebden, 2008, 2008 47th IEEE Conference on Decision and Control.

Real-time information processing of environmental sensor network data using bayesian gaussian processes

Nicholas R. Jennings, Stephen J. Roberts, Michael A. Osborne, 2012, ACM Trans. Sens. Networks.

2008 International Conference on Information Processing in Sensor Networks Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes

Sarvapali D. Ramchurn, Nicholas R. Jennings, Stephen J. Roberts, 2022 .

Information Agents for Pervasive Sensor Networks

Sarvapali D. Ramchurn, Nicholas R. Jennings, Stephen J. Roberts, 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

Efficient state-space inference of periodic latent force models

Steven Reece, Nicholas R. Jennings, Stephen J. Roberts, 2013, J. Mach. Learn. Res..

Bayesian Optimisation over Multiple Continuous and Categorical Inputs pdf

Michael A. Osborne, Vu Nguyen, Binxin Ru, 2019, ICML.

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

Mark Girolami, Ka-Veng Yuen, Sin-Chi Kuok, 2020 .

Distributed Data Fusion Design: Overarching Design Concerns and Some New Approaches

Steven Reece, Nicholas R. Jennings, Stephen J. Roberts, 2012 .

Variational Inference for Gaussian Process Modulated Poisson Processes

Stephen J. Roberts, Michael A. Osborne, Chris M. Lloyd, 2014, ICML.

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

Stephen J. Roberts, Yves-Laurent Kom Samo, S. Roberts, 2014, ICML.

Efficient Bayesian Nonparametric Modelling of Structured Point Processes

Stephen J. Roberts, Michael A. Osborne, Chris M. Lloyd, 2014, UAI.

Novel Exploration Techniques (NETs) for Malaria Policy Interventions pdf

Stephen J. Roberts, Sekou Remy, Aisha Walcott-Bryant, 2017, AAAI.

Using Sparse Gaussian Processes for Predicting Robust Inertial Confinement Fusion Implosion Yields

S. Roberts, S. Rose, M. Jarvis, 2019, IEEE Transactions on Plasma Science.

Robust, open-source removal of systematics in Kepler data

S. Aigrain, S. Reece, H. Parviainen, 2017, 1706.03064.

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes pdf

Stephen J. Roberts, Michael A. Osborne, Syed Ali Asad Rizvi, 2017, ArXiv.

Gaussian Processes for Timeseries Modelling.

Stephen J. Roberts, Michael A. Osborne, Suzanne Aigrain, 2012 .

Probabilistic Modeling in Bioinformatics and Medical Informatics

Stephen J. Roberts, Dirk Husmeier, Richard Dybowski, 2010 .

Incentive Engineering through Subgraph Matching — with Application to Task Allocation

Stephen Roberts, Luc Moreau, Dong Huynh, 2015 .

Uncertainty Estimation with a VAE-Classifier Hybrid Model

S. Roberts, R. Clark, A. Trigoni, 2022, IEEE International Conference on Acoustics, Speech, and Signal Processing.

Communication Communities in MOOCs pdf

Stephen J. Roberts, Michael A. Osborne, Rebecca Eynon, 2014, ArXiv.

Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Stephen J. Roberts, Robert Birke, Shuyu Lin, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

OffCon3: What is state of the art anyway?

Philip J. Ball, Stephen J. Roberts, Philip J. Ball, 2021, ArXiv.

Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning

Stephen J. Roberts, Richard Everett, S. Roberts, 2018, AAAI Spring Symposia.

Bayesian Image Super-resolution, Continued

Stephen J. Roberts, Andrew Zisserman, Lyndsey C. Pickup, 2006, NIPS.

A Sampled Texture Prior for Image Super-Resolution

Stephen J. Roberts, Andrew Zisserman, Lyndsey C. Pickup, 2003, NIPS.

Bayesian inference for an adaptive Ordered Probit model: An application to Brain Computer Interfacing

Stephen J. Roberts, Matthew Dyson, John Q. Gan, 2011, Neural Networks.

Maximum a Posteriori Estimation of Coupled Hidden Markov Models

Stephen J. Roberts, Iead Rezek, Michael Gibbs, 2002, J. VLSI Signal Process..

Novelty, confidence & errors in connectionist systems

Stephen J. Roberts, William D. Penny, D. Pillot, 1996 .

Title : Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks Year of publication : 2001

S. Roberts, R. Dybowski, 2009 .

1 Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks a

S. Roberts, R. Dybowski, 2000 .

Coupled hidden Markov models for biosignal interaction modelling

Stephen J. Roberts, Peter Sykacek, L. Rezek, 2000 .

Predicting Economic Indicators from Web Text Using Sentiment Composition

Stephen J. Roberts, Edwin Simpson, Stephen Pulman, 2014 .

Active Data Selection for Sensor Networks with Faults and Changepoints

Roman Garnett, Stephen J. Roberts, Michael A. Osborne, 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

Visualizing uncertainty in reliability functions with application to aero engine overhaul

Stephen J. Roberts, Armin Stranjak, Mark Ebden, 2010 .

Machine Learning-Based Financial Statement Analysis

S. Roberts, A. Amel-Zadeh, D. Kaiser, 2020 .

Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring

David A. Clifton, Stephen J. Roberts, Glen Wright Colopy, 2018, IEEE Journal of Biomedical and Health Informatics.

Identifying Sources of Discrimination Risk in the Life Cycle of Machine Intelligence Applications under New European Union Regulations

Stephen J. Roberts, Michael A. Osborne, Syed Ali Asad Rizvi, 2017, AAAI Spring Symposia.

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents

Stephen J. Roberts, Andrew Markham, Richard Everett, 2019, AAMAS.

GPz: non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts

Stephen J. Roberts, Matt J. Jarvis, S. Roberts, 2016, 1604.03593.

A Sparse Gaussian Process Framework for Photometric Redshift Estimation pdf

Stephen J. Roberts, Ibrahim A. Almosallam, Matt J. Jarvis, 2015, ArXiv.

Detecting bird sound in unknown acoustic background using crowdsourced training data pdf

Stephen J. Roberts, Timos Papadopoulos, Kathy Willis, 2015, ArXiv.

SafePILCO: a software tool for safe and data-efficient policy synthesis

Stephen Roberts, Alessandro Abate, Kyriakos Polymenakos, 2020, QEST.

Safe Policy Search Using Gaussian Process Models

Stephen J. Roberts, Alessandro Abate, Kyriakos Polymenakos, 2019, AAMAS.

Safe Policy Search with Gaussian Process Models pdf

Stephen J. Roberts, Alessandro Abate, Kyriakos Polymenakos, 2017, ArXiv.

Neural networks: friends or foes?

Stephen J. Roberts, William D. Penny, S. Roberts, 1997 .

A Multi-Dimensional Trust Model for Heterogeneous Contract Observations

Steven Reece, Nicholas R. Jennings, Stephen J. Roberts, 2007, AAAI.

Computational Mechanism Design for Information Fusion within Sensor Networks

Nicholas R. Jennings, Stephen J. Roberts, Alex Rogers, 2006, 2006 9th International Conference on Information Fusion.

A Probabilistic Approach to Nonparametric Local Volatility

Stephen J. Roberts, Martin Tegnér, S. Roberts, 2019, 1901.06021.

Sequential sampling of Gaussian process latent variable models pdf

Stephen J. Roberts, Martin Tegner, Benjamin Bloem-Reddy, 2018, ArXiv.

A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression

Stephen J. Roberts, Michael A. Osborne, Arnold Salas, 2015, 1509.02438.

Data-adaptive test statistics for microarray data

Stephen J. Roberts, Mark J. van der Laan, Sach Mukherjee, 2005, ECCB/JBI.

Sequential Dynamic Classification Using Latent Variable Models

Stephen J. Roberts, Seung Min Lee, S. Roberts, 2010, Comput. J..

An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers

William D. Penny, Stephen J. Roberts, Dirk Husmeier, 1999, Neural Networks.

Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling

J. Q. Gan, S. Roberts, J. Yoon, 2009, Neural Networks.

EEG-based communication via dynamic neural network models

William D. Penny, Stephen J. Roberts, S. Roberts, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Graph marginalization for rapid assignment in wide-area surveillance

Stephen J. Roberts, Mark Ebden, S. Roberts, 2011, Ad Hoc Networks.

Broad learning robust semi-active structural control: A nonparametric approach

M. Girolami, S. Roberts, K. Yuen, 2022 .

An ensemble learning approach to independent component analysis

Stephen J. Roberts, William D. Penny, Rizwan Choudrey, 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

Steven Reece, Nicholas R. Jennings, Stephen J. Roberts, 2008, J. Artif. Intell. Res..

Distribution of Gaussian Process Arc Lengths

Stephen J. Roberts, Michael A. Osborne, Alessandra Tosi, 2017, AISTATS.

String and Membrane Gaussian Processes

Stephen J. Roberts, Yves-Laurent Kom Samo, S. Roberts, 2015, J. Mach. Learn. Res..

String Gaussian Process Kernels

Stephen Roberts, Yves-Laurent Kom Samo, S. Roberts, 2015, 1506.02239.

Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-Down Ward

David A. Clifton, Stephen J. Roberts, Glen Wright Colopy, 2019, IEEE Journal of Biomedical and Health Informatics.

Bayesian Gaussian processes for identifying the deteriorating patient

David A. Clifton, Stephen J. Roberts, Marco A. F. Pimentel, 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Bayesian Combination of Multiple , Imperfect Classifiers

C. Lintott, S. Roberts, Edwin Simpson, 2011 .

Dynamic logistic regression

William D. Penny, Stephen J. Roberts, S. Roberts, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Supervised and unsupervised learning in radial basis function classifiers

Stephen J. Roberts, I. Tarassenko, S. Roberts, 1994 .

Optimal client recommendation for market makers in illiquid financial products pdf

Stephen J. Roberts, Dieter Hendricks, S. Roberts, 2017, ECML/PKDD.

Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN) pdf

Stephen Roberts, Jaleh Zand, S. Roberts, 2020, Signals.

A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training

J. Q. Gan, C. S. L. Tsui, S. Roberts, 2009, Medical & Biological Engineering & Computing.