Mark A. Girolami

According to our database1, Mark A. Girolami authored at least 135 papers between 1996 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2019
Bayesian Probabilistic Numerical Methods.
SIAM Review, 2019

Editorial: special edition on probabilistic numerics.
Statistics and Computing, 2019

The synthesis of data from instrumented structures and physics-based models via Gaussian processes.
J. Comput. Physics, 2019

Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved Simulation-Centric Dimension Reduction.
CoRR, 2019

Statistical Inference for Generative Models with Maximum Mean Discrepancy.
CoRR, 2019

Precision-Recall Balanced Topic Modelling.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multi-resolution Multi-task Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Minimum Stein Discrepancy Estimators.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stein Point Markov Chain Monte Carlo.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Bat detective - Deep learning tools for bat acoustic signal detection.
PLoS Computational Biology, 2018

How Deep Are Deep Gaussian Processes?
J. Mach. Learn. Res., 2018

Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success.
CoRR, 2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?".
CoRR, 2018

A Bayesian Conjugate Gradient Method.
CoRR, 2018

Bayesian Quadrature for Multiple Related Integrals.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Statistical analysis of differential equations: introducing probability measures on numerical solutions.
Statistics and Computing, 2017

Geometric MCMC for infinite-dimensional inverse problems.
J. Comput. Physics, 2017

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.
CoRR, 2017

Geometry and Dynamics for Markov Chain Monte Carlo.
CoRR, 2017

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Sampling Problem for Kernel Quadrature.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems.
J. Comput. Physics, 2016

A Bayesian approach to multiscale inverse problems with on-the-fly scale determination.
J. Comput. Physics, 2016

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems.
CoRR, 2016

Control Functionals for Quasi-Monte Carlo Integration.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Unbiased Bayes for Big Data: Paths of Partial Posteriors.
CoRR, 2015

Probabilistic Numerics and Uncertainty in Computations.
CoRR, 2015

Probabilistic Integration.
CoRR, 2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Ordinal Mixed Membership Models.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Pseudo-Marginal Bayesian Inference for Gaussian Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2014

Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions.
Entropy, 2014

mcmc_clib-an advanced MCMC sampling package for ode models.
Bioinformatics, 2014

Probabilistic Model Checking of DTMC Models of User Activity Patterns.
Proceedings of the Quantitative Evaluation of Systems - 11th International Conference, 2014

Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning.
Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Online Learning with (Multiple) Kernels: A Review.
Neural Computation, 2013

A comparative evaluation of stochastic-based inference methods for Gaussian process models.
Machine Learning, 2013

Exact-Approximate Bayesian Inference for Gaussian Processes.
CoRR, 2013

Analysing user behaviour through dynamic population models.
Proceedings of the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems, 2013

2012
Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations.
Neural Computation, 2012

Preface.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Discussion of the paper: "Sampling schemes for generalized linear Dirichlet process random effects models" by M. Kyung, J. Gill, and G. Casella.
Statistical Methods and Applications, 2011

ITFoM - The IT Future of Medicine.
Proceedings of the 2nd European Future Technologies Conference and Exhibition, 2011

Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation.
IJDMB, 2011

Protein interaction sentence detection using multiple semantic kernels.
J. Biomedical Semantics, 2011

On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor.
Proceedings of the Pattern Recognition and Image Analysis - 5th Iberian Conference, 2011

A First Course in Machine Learning.
Chapman and Hall / CRC machine learning and pattern recognition series, CRC Press, ISBN: 978-1-43-982414-6, 2011

2010
Multiclass relevance vector machines: sparsity and accuracy.
IEEE Trans. Neural Networks, 2010

Semi-parametric analysis of multi-rater data.
Statistics and Computing, 2010

Infinite factorization of multiple non-parametric views.
Machine Learning, 2010

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers.
BMC Bioinformatics, 2010

2009
Pattern recognition with a Bayesian kernel combination machine.
Pattern Recognition Letters, 2009

Combining feature spaces for classification.
Pattern Recognition, 2009

Reversible Jump MCMC for Non-Negative Matrix Factorization.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Estimating Bayes factors via thermodynamic integration and population MCMC.
Computational Statistics & Data Analysis, 2009

Probabilistic assignment of formulas to mass peaks in metabolomics experiments.
Bioinformatics, 2009

Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Classification of Protein Interaction Sentences via Gaussian Processes.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Inferring Meta-covariates in Classification.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Definition of Valid Proteomic Biomarkers: A Bayesian Solution.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana.
Proceedings of the Pattern Recognition in Bioinformatics, 2009

Analysis of SVM with Indefinite Kernels.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Bayesian inference for differential equations.
Theor. Comput. Sci., 2008

Classifying EEG for brain computer interfaces using Gaussian processes.
Pattern Recognition Letters, 2008

BioBayes: A software package for Bayesian inference in systems biology.
Bioinformatics, 2008

Bayesian ranking of biochemical system models.
Bioinformatics, 2008

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models.
Bioinformatics, 2008

ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction.
Bioinformatics, 2008

vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R.
Bioinformatics, 2008

Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection.
Bioinformatics, 2008

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins.
Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

2007
Employing Latent Dirichlet Allocation for fraud detection in telecommunications.
Pattern Recognition Letters, 2007

An empirical analysis of the probabilistic K-nearest neighbour classifier.
Pattern Recognition Letters, 2007

Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process.
Proceedings of the Gaussian Processes in Practice, 2007

Bayesian model-based inference of transcription factor activity.
BMC Bioinformatics, 2007

Detecting worm variants using machine learning.
Proceedings of the 2007 ACM Conference on Emerging Network Experiment and Technology, 2007

2006
Clustering via kernel decomposition.
IEEE Trans. Neural Networks, 2006

Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
Neural Computation, 2006

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Data Integration for Classification Problems Employing Gaussian Process Priors.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
The Latent Process Decomposition of cDNA Microarray Data Sets.
IEEE/ACM Trans. Comput. Biology Bioinform., 2005

Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains.
Data Min. Knowl. Discov., 2005

A Bayesian regression approach to the inference of regulatory networks from gene expression data.
Bioinformatics, 2005

Probabilistic hyperspace analogue to language.
Proceedings of the SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005

Hierarchic Bayesian models for kernel learning.
Proceedings of the Machine Learning, 2005

Disease Classification from Capillary Electrophoresis: Mass Spectrometry.
Proceedings of the Pattern Recognition and Data Mining, 2005

2004
Novelty detection employing an L2 optimal non-parametric density estimator.
Pattern Recognition Letters, 2004

Employing optimized combinations of one-class classifiers for automated currency validation.
Pattern Recognition, 2004

Biologically valid linear factor models of gene expression.
Bioinformatics, 2004

User biased document language modelling.
Proceedings of the SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

An Assessment of Feature Relevance in Predicting Protein Function from Sequence.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2004

2003
Probability Density Estimation from Optimally Condensed Data Samples.
IEEE Trans. Pattern Anal. Mach. Intell., 2003

Topic Identification in Dynamical Text by Complexity Pursuit.
Neural Processing Letters, 2003

On an equivalence between PLSI and LDA.
Proceedings of the SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28, 2003

Investigating the relationship between language model perplexity and IR precision-recall measures.
Proceedings of the SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28, 2003

Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2002
Mercer kernel-based clustering in feature space.
IEEE Trans. Neural Networks, 2002

Report on the 24th European Colloquium on Information Retrieval Research.
SIGMOD Record, 2002

Report on the 24th European colloquium on information retrieval research (ECIR 2002).
SIGIR Forum, 2002

Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus.
Neural Processing Letters, 2002

Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem.
Neural Computation, 2002

A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections.
J. Intell. Inf. Syst., 2002

A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams.
J. Intell. Inf. Syst., 2002

Latent variable models for the topographic organisation of discrete and strictly positive data.
Neurocomputing, 2002

A General Framework for a Principled Hierarchical Visualization of Multivariate Data.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2002

2001
The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models.
IEEE Trans. Neural Networks, 2001

A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2001

An Expectation-Maximization Approach to Nonlinear Component Analysis.
Neural Computation, 2001

A Variational Method for Learning Sparse and Overcomplete Representations.
Neural Computation, 2001

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression.
Neural Computing and Applications, 2001

Finding Topics in Dynamical Text: Application to Chat Line Discussions.
Proceedings of the Poster Proceedings of the Tenth International World Wide Web Conference, 2001

2000
Probabilistic Hierarchical Clustering Method for Organizing Collections of Text Documents.
Proceedings of the 15th International Conference on Pattern Recognition, 2000

Initialized and Guided EM-Clustering of Sparse Binary Data with Application to Text Based Documents.
Proceedings of the 15th International Conference on Pattern Recognition, 2000

A generative model for sparse discrete binary data with non-uniform categorical priors.
Proceedings of the ESANN 2000, 2000

The Organization and Visualization of Document Corpora: A Probabilistic Approach.
Proceedings of the 11th International Workshop on Database and Expert Systems Applications (DEXA'00), 2000

Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance.
Proceedings of the Artificial Neural Networks in Medicine and Biology, 2000

Extraction of Sleep-Spindles from the Electroencephalogram (EEG).
Proceedings of the Artificial Neural Networks in Medicine and Biology, 2000

1999
Blind source separation of more sources than mixtures using overcomplete representations.
IEEE Signal Process. Lett., 1999

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources.
Neural Computation, 1999

1998
A common neural-network model for unsupervised exploratory data analysis and independent component analysis.
IEEE Trans. Neural Networks, 1998

The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm.
Neural Processing Letters, 1998

An Alternative Perspective on Adaptive Independent Component Analysis Algorithms.
Neural Computation, 1998

A nonlinear model of the binaural cocktail party effect.
Neurocomputing, 1998

Noise reduction and speech enhancement via temporal anti-Hebbian learning.
Proceedings of the 1998 IEEE International Conference on Acoustics, 1998

1997
An extended exploratory projection pursuit network with linear and nonlinear anti-hebbian lateral connections applied to the cocktail party problem.
Neural Networks, 1997

Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm.
Int. J. Neural Syst., 1997

Generalised independent component analysis through unsupervised learning with emergent Bussgang properties.
Proceedings of International Conference on Neural Networks (ICNN'97), 1997

Kurtosis extrema and identification of independent components: a neural network approach.
Proceedings of the 1997 IEEE International Conference on Acoustics, 1997

Independence is far from normal.
Proceedings of the ESANN 1997, 1997

1996
A Temporal Model of Linear Anti-Hebbian Learning.
Neural Processing Letters, 1996


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