Stephen J. Roberts

Orcid: 0000-0002-9305-9268

Affiliations:
  • University of Oxford, UK


According to our database1, Stephen J. Roberts authored at least 253 papers between 1994 and 2024.

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Bibliography

2024
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers.
Nat. Mac. Intell., 2024

Iterate Averaging in the Quest for Best Test Error.
J. Mach. Learn. Res., 2024

Deep Learning for Options Trading: An End-To-End Approach.
Proceedings of the 5th ACM International Conference on AI in Finance, 2024

2023
On Sequential Bayesian Inference for Continual Learning.
Entropy, June, 2023

On the Sample Complexity of Lipschitz Constant Estimation.
Trans. Mach. Learn. Res., 2023

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies.
CoRR, 2023

Learning to Learn Financial Networks for Optimising Momentum Strategies.
CoRR, 2023

Network Momentum across Asset Classes.
CoRR, 2023

The instabilities of large learning rate training: a loss landscape view.
CoRR, 2023

SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters.
CoRR, 2023

Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies.
CoRR, 2023

Nonparametric Boundary Geometry in Physics Informed Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Effectiveness of World Models for Continual Reinforcement Learning.
Proceedings of the Conference on Lifelong Learning Agents, 2023

2022
Adversarial Robustness Guarantees for Gaussian Processes.
J. Mach. Learn. Res., 2022

Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training.
J. Mach. Learn. Res., 2022

Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19.
CoRR, 2022

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers.
CoRR, 2022

A large-scale and PCR-referenced vocal audio dataset for COVID-19.
CoRR, 2022

The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning.
CoRR, 2022

Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity.
CoRR, 2022

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications.
Algorithms, 2022

Learning General World Models in a Handful of Reward-Free Deployments.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels.
Proceedings of the International Conference on Machine Learning, 2022

Revisiting Design Choices in Offline Model Based Reinforcement Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Robust and Scalable SDE Learning: A Functional Perspective.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Uncertainty Estimation with a VAE-Classifier Hybrid Model.
Proceedings of the IEEE International Conference on Acoustics, 2022

On-the-fly Strategy Adaptation for ad-hoc Agent Coordination.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Marginalising over Stationary Kernels with Bayesian Quadrature.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Same State, Different Task: Continual Reinforcement Learning without Interference.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Optimal pricing in black box producer-consumer Stackelberg games using revealed preference feedback.
Neurocomputing, 2021

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture.
CoRR, 2021

One-Shot Transfer Learning of Physics-Informed Neural Networks.
CoRR, 2021

Revisiting Design Choices in Model-Based Offline Reinforcement Learning.
CoRR, 2021

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems.
CoRR, 2021

Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective.
CoRR, 2021

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection.
CoRR, 2021

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures.
CoRR, 2021

OffCon<sup>3</sup>: What is state of the art anyway?
CoRR, 2021

The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks.
CoRR, 2021

A Bayesian Optimization Approach to Compute Nash Equilibrium of Potential Games Using Bandit Feedback.
Comput. J., 2021

Towards tractable optimism in model-based reinforcement learning.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Hierarchical Indian buffet neural networks for Bayesian continual learning.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

HumBugDB: A Large-scale Acoustic Mosquito Dataset.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment.
Proceedings of the 38th International Conference on Machine Learning, 2021

Improving VAEs' Robustness to Adversarial Attack.
Proceedings of the 9th International Conference on Learning Representations, 2021

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Learning Bijective Feature Maps for Linear ICA.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Bioacoustic detection with wavelet-conditioned convolutional neural networks.
Neural Comput. Appl., 2020

Building Cross-Sectional Systematic Strategies By Learning to Rank.
CoRR, 2020

Explaining the Adaptive Generalisation Gap.
CoRR, 2020

Relaxed-Responsibility Hierarchical Discrete VAEs.
CoRR, 2020

On Optimism in Model-Based Reinforcement Learning.
CoRR, 2020

Deep Learning for Portfolio Optimisation.
CoRR, 2020

VIGN: Variational Integrator Graph Networks.
CoRR, 2020

Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN).
CoRR, 2020

Iterate Averaging Helps: An Alternative Perspective in Deep Learning.
CoRR, 2020

One-Shot Bayes Opt with Probabilistic Population Based Training.
CoRR, 2020

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio.
CoRR, 2020

Lazily Adapted Constant Kinky Inference for nonparametric regression and model-reference adaptive control.
Autom., 2020

SafePILCO: A Software Tool for Safe and Data-Efficient Policy Synthesis.
Proceedings of the Quantitative Evaluation of Systems - 17th International Conference, 2020

Effective Diversity in Population Based Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Explicit Regularisation in Gaussian Noise Injections.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Bayesian Optimisation over Multiple Continuous and Categorical Inputs.
Proceedings of the 37th International Conference on Machine Learning, 2020

Ready Policy One: World Building Through Active Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

An Overview of Gaussian process Regression for Volatility Forecasting.
Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication, 2020

Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Safety Guarantees for Iterative Predictions with Gaussian Processes.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Adversarial Robustness Guarantees for Classification with Gaussian Processes.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books.
IEEE Trans. Signal Process., 2019

Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-Down Ward.
IEEE J. Biomed. Health Informatics, 2019

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning.
Entropy, 2019

A General Framework for Fair Regression.
Entropy, 2019

MLRG Deep Curvature.
CoRR, 2019

A Maximum Entropy approach to Massive Graph Spectra.
CoRR, 2019

Indian Buffet Neural Networks for Continual Learning.
CoRR, 2019

Implicit Priors for Knowledge Sharing in Bayesian Neural Networks.
CoRR, 2019

Safety Guarantees for Planning Based on Iterative Gaussian Processes.
CoRR, 2019

Deep Reinforcement Learning for Trading.
CoRR, 2019

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo.
CoRR, 2019

Regularising Deep Networks with DGMs.
CoRR, 2019

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders.
CoRR, 2019

Balancing Reconstruction Quality and Regularisation in ELBO for VAEs.
CoRR, 2019

Adaptive Configuration Oracle for Online Portfolio Selection Methods.
CoRR, 2019

Disentangling Improves VAEs' Robustness to Adversarial Attacks.
CoRR, 2019

Robustness Quantification for Classification with Gaussian Processes.
CoRR, 2019

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs.
CoRR, 2019

Enhancing Time Series Momentum Strategies Using Deep Neural Networks.
CoRR, 2019

WiSE-VAE: Wide Sample Estimator VAE.
CoRR, 2019

Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels.
CoRR, 2019

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation.
Proceedings of the 36th International Conference on Machine Learning, 2019

WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Safe Policy Search Using Gaussian Process Models.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring.
IEEE J. Biomed. Health Informatics, 2018

Provenance Network Analytics - An approach to data analytics using data provenance.
Data Min. Knowl. Discov., 2018

Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity.
CoRR, 2018

BCCNet: Bayesian classifier combination neural network.
CoRR, 2018

Intersectionality: Multiple Group Fairness in Expectation Constraints.
CoRR, 2018

A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback.
CoRR, 2018

Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods.
CoRR, 2018

Semi-unsupervised Learning of Human Activity using Deep Generative Models.
CoRR, 2018

Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness.
CoRR, 2018

Automated bird sound recognition in realistic settings.
CoRR, 2018

Sequential sampling of Gaussian process latent variable models.
CoRR, 2018

Loss-Calibrated Approximate Inference in Bayesian Neural Networks.
CoRR, 2018

Entropic Spectral Learning in Large Scale Networks.
CoRR, 2018

Quantum algorithms for training Gaussian Processes.
CoRR, 2018

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting.
CoRR, 2018

Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning.
CoRR, 2018

VBALD - Variational Bayesian Approximation of Log Determinants.
CoRR, 2018

Improved Stochastic Trace Estimation using Mutually Unbiased Bases.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC.
Proceedings of the Scalable Uncertainty Management - 12th International Conference, 2018

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control.
Proceedings of the 16th European Control Conference, 2018

Fast mosquito acoustic detection with field cup recordings: an initial investigation.
Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events, 2018

Bayesian Nonparametrics and Feedback-Linearisation of Discretised Control-Affine Systems.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning.
Proceedings of the 2018 AAAI Spring Symposia, 2018

Novel Exploration Techniques (NETs) for Malaria Policy Interventions.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Safe Policy Search with Gaussian Process Models.
CoRR, 2017

Cost-sensitive detection with variational autoencoders for environmental acoustic sensing.
CoRR, 2017

Mosquito detection with low-cost smartphones: data acquisition for malaria research.
CoRR, 2017

Learning from lions: inferring the utility of agents from their trajectories.
CoRR, 2017

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes.
CoRR, 2017

Mosquito Detection with Neural Networks: The Buzz of Deep Learning.
CoRR, 2017

Bayesian Inference of Log Determinants.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Optimal Client Recommendation for Market Makers in Illiquid Financial Products.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Entropic Trace Estimates for Log Determinants.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Likelihood-based artefact detection in continuously-acquired patient vital signs.
Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

Entropic determinants of massive matrices.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Bayesian optimisation of Gaussian processes for identifying the deteriorating patient.
Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics, 2017

Distribution of Gaussian Process Arc Lengths.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Identifying Sources of Discrimination Risk in the Life Cycle of Machine Intelligence Applications under New European Union Regulations.
Proceedings of the 2017 AAAI Spring Symposia, 2017

2016
String and Membrane Gaussian Processes.
J. Mach. Learn. Res., 2016

A Disaster Response System based on Human-Agent Collectives.
J. Artif. Intell. Res., 2016

Human-agent collaboration for disaster response.
Auton. Agents Multi Agent Syst., 2016

Bayesian Gaussian processes for identifying the deteriorating patient.
Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016

Latent Point Process Allocation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems.
Proceedings of the Decision Making: Uncertainty, Imperfection, 2015

Modeling the Thermal Dynamics of Buildings: A Latent-Force- Model-Based Approach.
ACM Trans. Intell. Syst. Technol., 2015

Detecting bird sound in unknown acoustic background using crowdsourced training data.
CoRR, 2015

A Sparse Gaussian Process Framework for Photometric Redshift Estimation.
CoRR, 2015

Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning.
Proceedings of the 24th International Conference on World Wide Web, 2015

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Variational Inference for Gaussian Process Modulated Poisson Processes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

HAC-ER: A Disaster Response System based on Human-Agent Collectives.
Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015

2014
Maritime abnormality detection using Gaussian processes.
Knowl. Inf. Syst., 2014

Efficient state-space inference of periodic latent force models.
J. Mach. Learn. Res., 2014

Communication Communities in MOOCs.
CoRR, 2014

Human-agent collectives.
Commun. ACM, 2014

Efficient Bayesian Nonparametric Modelling of Structured Point Processes.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

AtomicOrchid: human-agent collectives to the rescue.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

2013
Dynamic Bayesian Combination of Multiple Imperfect Classifiers.
Proceedings of the Decision Making and Imperfection, 2013

Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems.
CoRR, 2013

Multi-Agent Planning with Mixed-Integer Programming and Adaptive Interaction Constraint Generation (Extended Abstract).
Proceedings of the Sixth Annual Symposium on Combinatorial Search, 2013

Discovering Latent Association Structure via Bayesian one-mode Projection of Temporal Bipartite Graphs.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013

Interpretation of Crowdsourced Activities Using Provenance Network Analysis.
Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing, 2013

AgentSwitch: towards smart energy tariff selection.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2013

2012
Real-time information processing of environmental sensor network data using bayesian gaussian processes.
ACM Trans. Sens. Networks, 2012

Bayesian Quadrature for Ratios.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Bayesian one-mode projection for dynamic bipartite graphs
CoRR, 2012

A tutorial on variational Bayesian inference.
Artif. Intell. Rev., 2012

Active Learning of Model Evidence Using Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Network Analysis on Provenance Graphs from a Crowdsourcing Application.
Proceedings of the Provenance and Annotation of Data and Processes, 2012

Online Maritime Abnormality Detection Using Gaussian Processes and Extreme Value Theory.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Towards Optimization-Based Multi-Agent Collision-Avoidance Under Continuous Stochastic Dynamics.
Proceedings of the Multiagent Pathfinding, Papers from the 2012 AAAI Workshop, 2012

2011
Bayesian inference for an adaptive Ordered Probit model: An application to Brain Computer Interfacing.
Neural Networks, 2011

Graph marginalization for rapid assignment in wide-area surveillance.
Ad Hoc Networks, 2011

Determining intent using hard/soft data and Gaussian process classifiers.
Proceedings of the 14th International Conference on Information Fusion, 2011

2010
Robust Measurement Validation in Target Tracking Using Geometric Structure.
IEEE Signal Process. Lett., 2010

The Near Constant Acceleration Gaussian Process Kernel for Tracking.
IEEE Signal Process. Lett., 2010

Sequential non-stationary dynamic classification with sparse feedback.
Pattern Recognit., 2010

Sequential Dynamic Classification Using Latent Variable Models.
Comput. J., 2010

Sequential Bayesian Prediction in the Presence of Changepoints and Faults.
Comput. J., 2010

Bayesian optimization for sensor set selection.
Proceedings of the 9th International Conference on Information Processing in Sensor Networks, 2010

An introduction to Gaussian processes for the Kalman filter expert.
Proceedings of the 13th Conference on Information Fusion, 2010

Active Data Selection for Sensor Networks with Faults and Changepoints.
Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, 2010

2009
Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling.
Neural Networks, 2009

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.
Medical Biol. Eng. Comput., 2009

Bayesian Methods for Image Super-Resolution.
Comput. J., 2009

Sequential Bayesian prediction in the presence of changepoints.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Multi-sensor fault recovery in the presence of known and unknown fault types.
Proceedings of the 12th International Conference on Information Fusion, 2009

2008
On Similarities between Inference in Game Theory and Machine Learning.
J. Artif. Intell. Res., 2008

Information Agents for Pervasive Sensor Networks.
Proceedings of the Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008), 2008

Sequential Bayesian estimation for adaptive classification.
Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008

Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes.
Proceedings of the 7th International Conference on Information Processing in Sensor Networks, 2008

Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain Computer Interfacing.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2008

On-line novelty detection using the Kalman filter and extreme value theory.
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

Decentralized predictive sensor allocation.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

2007
Gene Microarray Analysis Using Angular Distribution Decomposition.
J. Comput. Biol., 2007

Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?
EURASIP J. Adv. Signal Process., 2007

Drum'n'Bayes: on-Line variational Inference for beat tracking and rhythm Recognition.
Proceedings of the 2007 International Computer Music Conference, 2007

An Iterative Signal Detection Algorithm Based on Bayesian Belief Propagation Ideas.
Proceedings of the 15th International Conference on Digital Signal Processing, 2007

Rumours and reputation: evaluating multi-dimensional trust within a decentralised reputation system.
Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), 2007

A Multi-Dimensional Trust Model for Heterogeneous Contract Observations.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006
Bayesian Image Super-resolution, Continued.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Nonlinear, Biophysically-Informed Speech Pathology Detection.
Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006

Computational Mechanism Design for Information Fusion within Sensor Networks.
Proceedings of the 9th International Conference on Information Fusion, 2006

Optimizing and Learning for Super-resolution.
Proceedings of the British Machine Vision Conference 2006, 2006

Computational mechanism design for multi-sensor information fusion.
Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), 2006

2005
A Theoretical Analysis of the Selection of Differentially Expressed Genes.
J. Bioinform. Comput. Biol., 2005

A Simple, Quasi-linear, Discrete Model of Vocal Fold Dynamics.
Proceedings of the Nonlinear Analyses and Algorithms for Speech Processing, 2005

Depth of anaesthesia assessment with generative polyspectral models.
Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005

Data-adaptive test statistics for microarray data.
Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004
Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.
IEEE Trans. Biomed. Eng., 2004

Hierarchy, priors and wavelets: structure and signal modelling using ICA.
Signal Process., 2004

Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis.
Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

Probabilistic Consistency Analysis for Gene Selection.
Proceedings of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004

A Theoretical Analysis of Gene Selection.
Proceedings of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004

2003
Gene ranking using bootstrapped P-values.
SIGKDD Explor., 2003

Data decomposition using independent component analysis with prior constraints.
Pattern Recognit., 2003

Variational Mixture of Bayesian Independent Component Analyzers.
Neural Comput., 2003

A Sampled Texture Prior for Image Super-Resolution.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Markov Models for Automated ECG Interval Analysis.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002
Maximum a Posteriori Estimation of Coupled Hidden Markov Models.
J. VLSI Signal Process., 2002

Variational Bayes for generalized autoregressive models.
IEEE Trans. Signal Process., 2002

Towards the automatic analysis of complex human body motions.
Image Vis. Comput., 2002

Adaptive Classification by Variational Kalman Filtering.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Ensemble hidden Markov models for biosignal analysis.
Proceedings of the 14th International Conference on Digital Signal Processing, 2002

2001
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo.
IEEE Trans. Pattern Anal. Mach. Intell., 2001

Bayesian time series classification.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

A Probabilistic Approach to High-Resolution Sleep Analysis.
Proceedings of the Artificial Neural Networks, 2001

Mixtures of Independent Component Analysers.
Proceedings of the Artificial Neural Networks, 2001

Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo.
Proceedings of the Artificial Neural Networks, 2001

2000
Blind Source Separation for Non-Stationary Mixing.
J. VLSI Signal Process., 2000

Inferring the eigenvalues of covariance matrices from limited, noisy data.
IEEE Trans. Signal Process., 2000

Maximum certainty data partitioning.
Pattern Recognit., 2000

The Bayesian Paradigm: Second Generation Neural Computing.
Proceedings of the Artificial Neural Networks in Biomedicine, 2000

Independent Components Analysis.
Proceedings of the Artificial Neural Networks in Biomedicine, 2000

1999
Bayesian neural networks for classification: how useful is the evidence framework?
Neural Networks, 1999

An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers.
Neural Networks, 1999

Independent Component Analysis: A Flexible Nonlinearity and Decorrelating Manifold Approach.
Neural Comput., 1999

Dynamic Models for Nonstationary Signal Segmentation.
Comput. Biomed. Res., 1999

EEG-based communication via dynamic neural network models.
Proceedings of the International Joint Conference Neural Networks, 1999

Dynamic logistic regression.
Proceedings of the International Joint Conference Neural Networks, 1999

Neural networks for predicting Kaposi's sarcoma.
Proceedings of the International Joint Conference Neural Networks, 1999

1998
Stochastic complexity measures for physiological signal analysis.
IEEE Trans. Biomed. Eng., 1998

Bayesian Approaches to Gaussian Mixture Modeling.
IEEE Trans. Pattern Anal. Mach. Intell., 1998

1997
Parametric and non-parametric unsupervised cluster analysis.
Pattern Recognit., 1997

1996
Scale-space unsupervised cluster analysis.
Proceedings of the 13th International Conference on Pattern Recognition, 1996

1994
A Probabilistic Resource Allocating Network for Novelty Detection.
Neural Comput., 1994


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