Mark A. Girolami

发表

Combining feature spaces for classification

Theodoros Damoulas, Mark A. Girolami, M. Girolami, 2009, Pattern Recognit..

Probabilistic hyperspace analogue to language

Leif Azzopardi, Mark A. Girolami, Malcolm K. Crowe, 2005, SIGIR '05.

User biased document language modelling

C. J. van Rijsbergen, Leif Azzopardi, Mark A. Girolami, 2004, SIGIR '04.

Employing optimized combinations of one-class classifiers for automated currency validation

Chao He, Mark A. Girolami, Gary Ross, 2004, Pattern Recognit..

Geometry and Dynamics for Markov Chain Monte Carlo pdf

Mark A. Girolami, Anthony D. Kennedy, Alessandro Barp, 2017, ArXiv.

A Bayesian approach to multiscale inverse problems with on-the-fly scale determination

Mark A. Girolami, Nicholas Zabaras, Louis Ellam, 2016, J. Comput. Phys..

Topic Identification in Dynamical Text by Complexity Pursuit

Ata Kabán, Ella Bingham, Mark A. Girolami, 2003, Neural Processing Letters.

Independence is far from normal

Colin Fyfe, Mark A. Girolami, C. Fyfe, 1997, ESANN.

Noise reduction and speech enhancement via temporal anti-Hebbian learning

Mark A. Girolami, M. Girolami, 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

Proceedings of the 21th international conference on Artificial neural networks - Volume Part I

Timo Honkela, Mark A. Girolami, Duch Włodzisław, 2011 .

Discussion of the paper: “Sampling schemes for generalized linear Dirichlet process random effects models” by M. Kyung, J. Gill, and G. Casella

Maurizio Filippone, Mark A. Girolami, Antonietta Mira, 2011, Stat. Methods Appl..

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models

Mark A. Girolami, Steven Niederer, Chris J. Oates, 2016, NIPS.

On an equivalence between PLSI and LDA

Ata Kabán, Mark A. Girolami, M. Girolami, 2003, SIGIR.

Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation

Mark A. Girolami, J. G. Taylor, M. Girolami, 1999 .

Statistical analysis of differential equations: introducing probability measures on numerical solutions

Andrew M. Stuart, Simo Särkkä, Mark A. Girolami, 2016, Statistics and Computing.

Estimating Bayes factors via thermodynamic integration and population MCMC

Mark A. Girolami, Ben Calderhead, M. Girolami, 2009, Comput. Stat. Data Anal..

Probabilistic Model Checking of DTMC Models of User Activity Patterns

Muffy Calder, Mark A. Girolami, Oana Andrei, 2014, QEST.

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems pdf

Mark A. Girolami, Chris J. Oates, Tim Sullivan, 2016, ArXiv.

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

Rohitash Chandra, Mark A. Girolami, Sally Cripps, 2018, Geoscientific Model Development.

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

Alexandros Kalousis, Mark A. Girolami, Walter Kolch, 2010, BMC Bioinformatics.

Semi-parametric analysis of multi-rater data

Simon Rogers, Mark A. Girolami, Tamara Polajnar, 2010, Stat. Comput..

The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm

Mark A. Girolami, M. Girolami, 1998, Neural Processing Letters.

Putting the Scientist in the Loop -- Accelerating Scientific Progress with Interactive Machine Learning

Mark A. Girolami, Kate E. Jones, Michael Terry, 2014, 2014 22nd International Conference on Pattern Recognition.

Pseudo-Marginal Bayesian Inference for Gaussian Processes

Maurizio Filippone, Mark A. Girolami, M. Girolami, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations

Mark A. Girolami, Mahesan Niranjan, Ke Yuan, 2012, Neural Computation.

A generative model for sparse discrete binary data with non-uniform categorical priors

Mark A. Girolami, 2000, ESANN.

ITFoM - The IT Future of Medicine

Mark A. Girolami, Hans Lehrach, Frank J. Bruggeman, 2011, FET.

A Bayesian regression approach to the inference of regulatory networks from gene expression data

Simon Rogers, Mark A. Girolami, M. Girolami, 2005, Bioinform..

Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval

Fabio Crestani, C. J. van Rijsbergen, Mark A. Girolami, 2002 .

Latent variable models for the topographic organisation of discrete and strictly positive data

Mark A. Girolami, 2002, Neurocomputing.

How Deep Are Deep Gaussian Processes?

Andrew M. Stuart, Mark A. Girolami, Aretha L. Teckentrup, 2017, J. Mach. Learn. Res..

Kurtosis extrema and identification of independent components: a neural network approach

Colin Fyfe, Mark A. Girolami, C. Fyfe, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

The organisation and visualisation of document corpora: a probabilistic approach

Ata Kabán, Mark A. Girolami, Alexei Vinokourov, 2000, Proceedings 11th International Workshop on Database and Expert Systems Applications.

Probabilistic assignment of formulas to mass peaks in metabolomics experiments

Simon Rogers, Mark A. Girolami, Rainer Breitling, 2009, Bioinform..

Pattern recognition with a Bayesian kernel combination machine

Theodoros Damoulas, Mark A. Girolami, M. Girolami, 2009, Pattern Recognit. Lett..

On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor

Mark A. Girolami, Roberto Paredes, 2011, IbPRIA.

A common neural-network model for unsupervised exploratory data analysis and independent component analysis

Andrzej Cichocki, Shun-ichi Amari, Mark A. Girolami, 1998, IEEE Trans. Neural Networks.

Investigating the relationship between language model perplexity and IR precision-recall measures

C. J. van Rijsbergen, Leif Azzopardi, Mark A. Girolami, 2003, SIGIR.

Reversible Jump MCMC for Non-Negative Matrix Factorization

Mingjun Zhong, Mark A. Girolami, 2009, AISTATS.

Bayesian ranking of biochemical system models

Mark A. Girolami, Vladislav Vyshemirsky, M. Girolami, 2008, Bioinform..

Geometric MCMC for infinite-dimensional inverse problems

Andrew M. Stuart, Alexandros Beskos, Mark A. Girolami, 2016, J. Comput. Phys..

The latent process decomposition of cDNA microarray data sets

Colin Campbell, Rainer Breitling, Simon Rogers, 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Finding Topics in Dynamical Text: Application to Chat Line Discussions

Ata Kabán, Ella Bingham, Mark A. Girolami, 2001, WWW Posters.

Analysis of SVM with Indefinite Kernels

Colin Campbell, Mark A. Girolami, Yiming Ying, 2009, NIPS.

Mercer kernel-based clustering in feature space

Mark A. Girolami, M. Girolami, 2002, IEEE Trans. Neural Networks.

Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins

Colin Campbell, Theodoros Damoulas, Mark A. Girolami, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection

Theodoros Damoulas, Mark A. Girolami, M. Girolami, 2008, Bioinform..

Bat detective—Deep learning tools for bat acoustic signal detection

Mark A. Girolami, Gabriel J. Brostow, Jon Russ, 2017, bioRxiv.

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

Michael A. Osborne, Mark A. Girolami, Chris J. Oates, 2015, NIPS.

Report on the 24th European colloquium on information retrieval research (ECIR 2002)

Fabio Crestani, Mark A. Girolami, M. Girolami, 2002, SIGF.

Ordinal Mixed Membership Models

Seppo Virtanen, Mark A. Girolami, 2015, ICML.

Bayesian Quadrature for Multiple Related Integrals pdf

Mark A. Girolami, François-Xavier Briol, Xiaoyue Xi, 2018, ICML.

A Bayesian Conjugate Gradient Method pdf

Mark A. Girolami, Chris J. Oates, Jon Cockayne, 2018, Bayesian Analysis.

Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation

Simon Rogers, Mark A. Girolami, Tamara Polajnar, 2011, Int. J. Data Min. Bioinform..

Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions

Mark A. Girolami, Samuel Livingstone, M. Girolami, 2014, Entropy.

Bayesian inference for differential equations

Mark A. Girolami, M. Girolami, 2008, Theor. Comput. Sci..

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources

Terrence J. Sejnowski, Te-Won Lee, Mark A. Girolami, 1999, Neural Comput..

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation

Gavin C. Cawley, Mark A. Girolami, Nicola L. C. Talbot, 2006, NIPS.

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?" pdf

Michael A. Osborne, Mark A. Girolami, Dino Sejdinovic, 2018, Statistical Science.

Bayesian model-based inference of transcription factor activity

Raya Khanin, Simon Rogers, Mark A. Girolami, 2007, BMC Bioinformatics.

Employing Latent Dirichlet Allocation for fraud detection in telecommunications

Mark A. Girolami, Dongshan Xing, M. Girolami, 2007, Pattern Recognit. Lett..

On the Sampling Problem for Kernel Quadrature

Mark A. Girolami, Chris J. Oates, François-Xavier Briol, 2017, ICML.

BioBayes: A software package for Bayesian inference in systems biology

Mark A. Girolami, Vladislav Vyshemirsky, M. Girolami, 2008, Bioinform..

A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams

Ata Kabán, Mark A. Girolami, M. Girolami, 2002, Journal of Intelligent Information Systems.

Clustering via kernel decomposition

Jan Larsen, Mark A. Girolami, Anna Szymkowiak-Have, 2006, IEEE Transactions on Neural Networks.

Classification of Protein Interaction Sentences via Gaussian Processes

Simon Rogers, Mark A. Girolami, Tamara Polajnar, 2009, PRIB.

Multiclass Relevance Vector Machines: Sparsity and Accuracy

Theodoros Damoulas, Mark A. Girolami, Ioannis Psorakis, 2010, IEEE Transactions on Neural Networks.

Infinite factorization of multiple non-parametric views

Samuel Kaski, Simon Rogers, Mark A. Girolami, 2010, Machine Learning.

Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus

Ata Kabán, Mark A. Girolami, 2004, Neural Processing Letters.

Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles

Ata Kabán, Mark A. Girolami, M. Girolami, 2003, NIPS.

Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process

Simon Rogers, Mark A. Girolami, 2007, Gaussian Processes in Practice.

A comparative evaluation of stochastic-based inference methods for Gaussian process models

Maurizio Filippone, Mingjun Zhong, Mark A. Girolami, 2013, Machine Learning.

A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data

Ata Kabán, Mark A. Girolami, M. Girolami, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

Neil D. Lawrence, Mark A. Girolami, Ben Calderhead, 2008, NIPS.

Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics

Mark A. Girolami, Tamara Polajnar, 2009, PRIB.

An empirical analysis of the probabilistic K-nearest neighbour classifier

Mark A. Girolami, S. Manocha, M. Girolami, 2007, Pattern Recognit. Lett..

Unbiased Bayes for Big Data: Paths of Partial Posteriors pdf

Mark A. Girolami, Dino Sejdinovic, Heiko Strathmann, 2015, ArXiv.

Detecting worm variants using machine learning

Mark A. Girolami, Joseph S. Sventek, Oliver Sharma, 2007, CoNEXT '07.

Definition of Valid Proteomic Biomarkers: A Bayesian Solution

Mark A. Girolami, Harald Mischak, Keith Harris, 2009, PRIB.

Protein interaction sentence detection using multiple semantic kernels

Theodoros Damoulas, Mark A. Girolami, Tamara Polajnar, 2011, J. Biomed. Semant..

A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices pdf

Mingjun Zhong, Mark A. Girolami, M. Girolami, 2012, ICML.

Biologically valid linear factor models of gene expression

Rainer Breitling, Mark A. Girolami, M. Girolami, 2004, Bioinform..

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Mark A. Girolami, Alastair Gregory, F. Din-Houn Lau, 2018, J. Comput. Phys..

Disease Classification from Capillary Electrophoresis: Mass Spectrometry

Simon Rogers, Mark A. Girolami, Harald Mischak, 2005, ICAPR.

Hierarchic Bayesian models for kernel learning

Simon Rogers, Mark A. Girolami, M. Girolami, 2005, ICML.

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

Andrzej Cichocki, Roman Rosipal, Mark A. Girolami, 2001, Neural Computing & Applications.

A temporal model of linear anti-Hebbian learning

Colin Fyfe, Mark A. Girolami, C. Fyfe, 1996, Neural Processing Letters.

A Variational Method for Learning Sparse and Overcomplete Representations

Mark A. Girolami, M. Girolami, 2001, Neural Computation.

Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel

Mark A. Girolami, Vassilios Stathopoulos, Kate E. Jones, 2014, AISTATS.

A First Course in Machine Learning

Simon Rogers, Mark A. Girolami, M. Girolami, 2011, Chapman and Hall / CRC machine learning and pattern recognition series.

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

Colin Fyfe, Mark A. Girolami, C. Fyfe, 1997, Neural Networks.

Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains

Ata Kabán, Mark A. Girolami, M. Girolami, 2005, Data Mining and Knowledge Discovery.

Data Integration for Classification Problems Employing Gaussian Process Priors

Mingjun Zhong, Mark A. Girolami, 2006, NIPS.

Bayesian Probabilistic Numerical Methods

Mark A. Girolami, Chris J. Oates, Tim Sullivan, 2017, SIAM Rev..

Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm

Colin Campbell, Theodoros Damoulas, Mark A. Girolami, 2009, PRIB.

Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm

Colin Fyfe, Mark A. Girolami, C. Fyfe, 1997, Int. J. Neural Syst..

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btn055 Sequence analysis ParCrys: a Parzen window density estimation approach

Mark A. Girolami, Geoffrey J. Barton, Ian M. Overton, 2022 .

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems pdf

Mark A. Girolami, Chris J. Oates, Tim Sullivan, 2017, ArXiv.

Probability Density Estimation from Optimally Condensed Data Samples

Chao He, Mark A. Girolami, M. Girolami, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

Terrence J. Sejnowski, Te-Won Lee, Mark A. Girolami, 1999, Neural Computation.

Blind source separation of more sources than mixtures using overcomplete representations

Terrence J. Sejnowski, Michael S. Lewicki, Te-Won Lee, 1999, IEEE Signal Processing Letters.

Analysing user behaviour through dynamic population models

Matthew Chalmers, Alistair Morrison, Mark A. Girolami, 2013, CHI Extended Abstracts.

Probabilistic Integration pdf

Michael A. Osborne, Mark A. Girolami, Dino Sejdinovic, 2015, ArXiv.

A nonlinear model of the binaural cocktail party effect

Mark A. Girolami, M. Girolami, 1998, Neurocomputing.

An Assessment of Feature Relevance in Predicting Protein Function from Sequence

Mark A. Girolami, Aik Choon Tan, Ali Al-Shahib, 2004, IDEAL.

vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R

Mark A. Girolami, Nicola Lama, 2008, Bioinform..

Inferring Meta-covariates in Classification

Mark A. Girolami, Keith Harris, Lisa McMillan, 2009, PRIB.

Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems

Mark A. Girolami, Mike Christie, Shiwei Lan, 2015, J. Comput. Phys..

The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models

Mark A. Girolami, M. Girolami, 2001, IEEE Trans. Neural Networks.

Bayesian ranking of biochemical system models

Mark A. Girolami, Vladislav Vyshemirsky, M. Girolami, 2008, Bioinform..

Novelty detection employing an L2 optimal non-parametric density estimator

Chao He, Mark A. Girolami, M. Girolami, 2004, Pattern Recognit. Lett..

A General Framework for a Principled Hierarchical Visualization of Multivariate Data

Peter Tiño, Ata Kabán, Mark A. Girolami, 2002, IDEAL.

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm

Robert Jenssen, Deniz Erdogmus, Torbjørn Eltoft, 2006, NIPS.

Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana

Andrew J. Millar, Rónán Daly, Mark A. Girolami, 2009, PRIB.

A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections

Mark A. Girolami, Alexei Vinokourov, 2004, Journal of Intelligent Information Systems.

Classifying EEG for brain computer interfaces using Gaussian processes

Anatole Lécuyer, Mingjun Zhong, Fabien Lotte, 2008, Pattern Recognit. Lett..

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models

Tao Liu, Simon Rogers, Mark A. Girolami, 2008, Bioinform..

Online Learning with (Multiple) Kernels: A Review

Tom Diethe, Mark A. Girolami, M. Girolami, 2013, Neural Computation.

Exact-Approximate Bayesian Inference for Gaussian Processes pdf

Maurizio Filippone, Mark A. Girolami, M. Girolami, 2013, ArXiv.

An Expectation-Maximization Approach to Nonlinear Component Analysis

Roman Rosipal, Mark A. Girolami, M. Girolami, 2001, Neural Computation.

An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Mark A. Girolami, M. Girolami, 1998, Neural Computation.

A probabilistic hierarchical clustering method for organising collections of text documents

Mark A. Girolami, Alexei Vinokourov, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

Predictive response-relevant clustering of expression data provides insights into disease processes

Mark A. Girolami, Martin W. McBride, Anna F. Dominiczak, 2010, Nucleic acids research.

Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation

Mark A. Girolami, M. Girolami, Vassilios Stathopoulos, 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.