[edit]
Publications
2020
Conference Papers
2019
Journal Papers
Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance
; International Data Privacy Law, Oxford Academic 9(4):236-252
[abs]
Intrinsic Gaussian Processes on Complex Constrained Domains
; :
Conference Papers
Variational Information Distillation for Knowledge Transfer
; Conference on Computer Vision and Pattern Recognition (CVPR), :9155-9163
[abs]
2018
Journal Papers
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
; IEEE Transactions on Automatic Control, IEEE 64(7):2953-2960
[abs]
The Emergence of Organizing Structure in Conceptual Representation
; Cognitive Science, 42(S3):1-24
Conference Papers
Structured Variationally Auto-encoded Optimization
; Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3273-3281
Differentially Private Regression with Gaussian Processes
; Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1195-1203
2017
Journal Papers
Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation
; Bioinformatics, 23:3776-3783
Conference Papers
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
; Advances in Neural Information Processing Systems, Curran Associates, Inc. 30:5131-5139
[abs]
Preferential Bayesian Optimization
; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1282-1291
[abs]
Technical Reports
2016
Journal Papers
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
; Journal of Machine Learning Research, 17(42):1-62
[abs]
Conference Papers
Recurrent Gaussian Processes
; Proceedings of the International Conference on Learning Representations, 3:
[abs]
GLASSES: Relieving The Myopia Of Bayesian Optimisation
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:790-799
[abs]
Chained Gaussian Processes
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:1431-1440
[abs]
Batch Bayesian Optimization via Local Penalization
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:648-657
[abs]
Variationally Auto-Encoded Deep Gaussian Processes
; Proceedings of the International Conference on Learning Representations, 3:
[abs]
Technical Reports
2015
Journal Papers
Genome-wide Modeling of Transcription Kinetics Reveals Patterns of RNA Production Delays
; Proc. Natl. Acad. Sci. USA, 112(42):13115-13120
A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data
; BMC Bioinformatics, 16(279):
[abs]
Conference Papers
Semi-described and Semi-supervised Learning with Gaussian Processes
; 31st Conference on Uncertainty in Artificial Intelligence (UAI), :
[abs]
2014
Journal Papers
Consistent Mapping of Government Malaria Records Across a Changing Territory Delimitation
; Malaria Journal, 13(Suppl 1):
[abs]
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data
; PLoS Computat Biol, 10(5):
Fast nonparametric clustering of structured time-series
; IEEE Transactions on Pattern Analysis and Machine Intelligence, :
[abs]
Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes
; Nature Communications, 5(4890):
[abs]
Conference Papers
Malaria surveillance with multiple data sources using Gaussian process models
; 1st International Conference on the Use of Mobile ICT in Africa, :
[abs]
Metrics for Probabilistic Geometries
; Uncertainty in Artificial Intelligence, AUAI Press 30:800-808
[abs]
Tilted Variational Bayes
; Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, PMLR 33:356-364
Hybrid Discriminative-Generative Approaches with Gaussian Processes
; Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 33 33:47-56
[abs]
Technical Reports
Miscellaneous
2013
Journal Papers
Miscellaneous
; :
[abs]
2013
Conference Papers
The Bigraphical Lasso
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1229-1237
Miscellaneous
; :
[abs]
2013
Journal Papers
Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors
; Bioinformatics, 29(11):1382-1389
[abs]
Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity
; Acta Neuropathologica, 125(1):
[abs]
Conference Papers
Book Chapters
Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data
; Data Mining for Systems Biology, Springer-Verlag:
[abs]
2012
Journal Papers
Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition
; PLoS Computational Biology, 8(5):0-0
[abs]
Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by Bayesian Model Comparison
; BMC Systems Biology, 6(53):
[abs]
Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies
; PLoS Computat Biol, Public Library of Science 8:0-0
[abs]
Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development
; Nucleaic Acids Research, 40(9):3390-4001
[abs]
Kernels for Vector-Valued Functions: A Review
; Foundations and Trends in Machine Learning, 4(3):195-266
[abs]
Conference Papers
Residual Component Analysis
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
[abs]
Fast variational inference in the Conjugate Exponential family
; Advances in Neural Information Processing Systems, 25:
[abs]
Manifold Relevance Determination
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
[abs]
Technical Reports
Gaussian Processes for Big Data with Stochastic Variational Inference
; Submitted to NIPS 2012 Workshop, :
[abs]
2011
Journal Papers
Computationally Efficient Convolved Multiple Output Gaussian Processes
; Journal of Machine Learning Research, 12:1425-1466
[abs]
tigre: Transcription Factor Inference through Gaussian Process Reconstruction of Expression for Bioconductor
; Bioinformatics, 27:1026-1027
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
; Pattern Recognition, 10(4):
[abs]
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
; BMC Bioinformatics, 12(180):
Conference Papers
Efficient Inference in Matrix-Variate Gaussian Models with i.i.d. Observation Noise
; Neural Information Processing Systems, :
[abs]
Spectral Dimensionality Reduction via Maximum Entropy
; Proceedings of the Fourteenth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 15 15:51-59
[abs]
Variational Gaussian Process Dynamical Systems
; Advances in Neural Information Processing Systems, MIT Press 24:
Book Chapters
Markov chain Monte Carlo algorithms for Gaussian processes
; Bayesian Time Series Models, Cambridge University Press:
[abs]
Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
; Handbook of Statistical Systems Biology, John Wiley and Sons:376-394
[abs]
Technical Reports
Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects
; :
[abs]
2010
Journal Papers
Model-based Method for Transcription Factor Target Identification with Limited Data
; Proc. Natl. Acad. Sci. USA, 107(17):7793-7798
[abs]
Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells
; The Journal of Physiology, 588:187-189
[abs]
TFInfer: a tool for probabilistic inference of transcription factor activities
; Bioinformatics, 26:2635-2636
[abs]
Conference Papers
Bayesian Gaussian Process Latent Variable Model
; Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 9 9:844-851
[abs]
Switched Latent Force Models for Movement Segmentation
; Advances in Neural Information Processing Systems, MIT Press 23:55-63
[abs]
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
; Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 9 9:25-32
[abs]
Book Chapters
Introduction to Learning and Inference in Computational Systems Biology
; Learning and Inference in Computational Systems Biology, MIT Press:
[abs]
Gaussian Processes for Missing Species in Biochemical Systems
; Learning and Inference in Computational Systems Biology, MIT Press:
[abs]
A Brief Introduction to Bayesian Inference
; Learning and Inference in Computational Systems Biology, MIT Press:
[abs]
Technical Reports
2009
Journal Papers
puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
; BMC Bioinformatics, 10(211):
[abs]
Conference Papers
Efficient Sampling for Gaussian Process Inference using Control Variables
; Advances in Neural Information Processing Systems, MIT Press 21:1681-1688
[abs]
Non-Linear Matrix Factorization with Gaussian Processes
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 26:
[abs]
Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery
; British Machine Vision Conference, :
[abs]
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
; Advances in Neural Information Processing Systems, MIT Press 21:217-224
[abs]
Latent Force Models
; Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 5 5:9-16
[abs]
Sparse Convolved Gaussian Processes for Multi-output Regression
; Advances in Neural Information Processing Systems, MIT Press 21:57-64
[abs]
Technical Reports
2008
Journal Papers
Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities
; Bioinformatics, 24:0-0
[abs]
Conference Papers
Topologically-Constrained Latent Variable Models
; Proceedings of the International Conference in Machine Learning, Omnipress 25:1080-1087
[abs]
Gaussian Process Latent Variable Models For Human Pose Estimation
; Machine Learning for Multimodal Interaction (MLMI 2007), Springer-Verlag 4892:132-143
[abs]
Ambiguity Modeling in Latent Spaces
; Machine Learning for Multimodal Interaction (MLMI 2008), Springer-Verlag:62-73
[abs]
2007
Conference Papers
Modelling transcriptional regulation using Gaussian Processes
; Advances in Neural Information Processing Systems, MIT Press 19:785-792
[abs]
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
; Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Omnipress:243-250
[abs]
Hierarchical Gaussian Process Latent Variable Models
; Proceedings of the International Conference in Machine Learning, Omnipress 24:481-488
[abs]
WiFi-SLAM Using Gaussian Process Latent Variable Models
; Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), :2480-2485
[abs]
Gaussian Process Latent Variable Models for Fault Detection
; Computational Intelligence and Data Mining, :287-292
[abs]
Miscellaneous
2006
Journal Papers
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
; Journal of Machine Learning Research, 7:455-491
[abs][JMLR Abstract]
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
; Bioinformatics, 22(22):2275-2281
[abs]
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
; Bioinformatics, 22(14):1753-1759
Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression
; Bioinformatics, 22(17):2107-2113
[abs]
Conference Papers
Identifying submodules of cellular regulatory networks
; International Conference on Computational Methods in Systems Biology, Springer-Verlag:
[abs]
Local Distance Preservation in the GP-LVM through Back Constraints
; Proceedings of the International Conference in Machine Learning, Omnipress 23:513-520
[abs]
Fast Variational Inference for Gaussian Process Models through KL-Correction
; ECML, Berlin, 2006, Springer-Verlag:270-281
[abs]
Book Chapters
Gaussian Processes and the Null-Category Noise Model
; Semi-supervised Learning, MIT Press:152-165
Technical Reports
2005
Journal Papers
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
; Journal of Machine Learning Research, 6:1783-1816
[abs][C++ Software][MATLAB Software][JMLR PDF][JMLR Abstract]
A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips
; Bioinformatics, 21(18):3637-3644
Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis
; Neurocomputing, 69:123-141
Accounting for Probe-level Noise in Principal Component Analysis of Microarray Data
; Bionformatics, 21(19):3748-3754
[abs][Advance Access][Pre-print PDF][Bioinformatics Abstract]
Conference Papers
A Hybrid MaxEnt/HMM Based ASR System
; Proceedings of Interspeech 2005 --- 9th European Conference on Speech Communication and Technology, :
[abs]
Automatic Determination of the Number of Clusters Using Spectral Algorithms
; Procedings of MLSP'05, :
[abs]
Semi-supervised Learning via Gaussian Processes
; Advances in Neural Information Processing Systems, MIT Press 17:753-760
[abs]
Book Chapters
Extensions of the Informative Vector Machine
; Deterministic and Statistical Methods in Machine Learning, Springer-Verlag 3635:56-87
[abs]
Technical Reports
Miscellaneous
2004
Journal Papers
Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference
; Bioinformatics, 20(4):518-526
[abs][Pre-print PDF]
Conference Papers
Learning to Learn with the Informative Vector Machine
; Proceedings of the International Conference in Machine Learning, Omnipress 21:512-519
[abs]
Gaussian Process Models for Visualisation of High Dimensional Data
; Advances in Neural Information Processing Systems, MIT Press 16:329-336
[abs]
Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle
; International Conference on Acoustics, Speech and Signal Processing, :
[abs]
Technical Reports
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
; (CS-04-13):
[abs]
The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine
; (CS-04-07):
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
; (CS-04-08):
[abs]
2003
Journal Papers
A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays
; Biochemical Transations, 31(6):1510-1512
[abs]
Conference Papers
Variational Inference for Visual Tracking
; Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press I:773-780
[abs]
A Variational Approach to Robust Bayesian Interpolation
; Neural Networks for Signal Processing XIII, IEEE:229-238
[abs]
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
; Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, :
[abs]
Bayesian Processing of Microarray Images
; Neural Networks for Signal Processing XIII, IEEE:71-80
[abs]
Fast Sparse Gaussian Process Methods: The Informative Vector Machine
; Advances in Neural Information Processing Systems, MIT Press 15:625-632
[abs]
Technical Reports
2002
Conference Papers
Optimising Synchronisation Times for Mobile Devices
; Advances in Neural Information Processing Systems, MIT Press 14:1401-1408
[abs]
Technical Reports
2001
Journal Papers
A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
; Neural Computing and Applications, 10(1):39-47
[abs]
Conference Papers
Probabilistic Modelling of Replica Divergence
; Proceedings of the 8th Workshop on Hot Topics in Operating Systems HOTOS (VIII), :
[abs]
Node Relevance Determination
; The {XI}th Italian Workshop on Neural Networks, Springer-Verlag:
[abs]
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 18:
[abs]
Variational Learning for Multi-layer networks of Linear Threshold Units
; Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman:245-252
[abs]
Technical Reports
Miscellaneous
2000
Technical Reports
1999
Technical Reports
1998
Conference Papers
Mixture Representations for Inference and Learning in Boltzmann Machines
; Uncertainty in Artificial Intelligence, Morgan Kauffman 14:320-327
[abs]
Approximating Posterior Distributions in Belief Networks using Mixtures
; Advances in Neural Information Processing Systems, MIT Press 10:416-422
[abs]
Technical Reports
0001
subscribe via RSS
-
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
-
Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance
-
Variational Information Distillation for Knowledge Transfer
-
Transferring Knowledge across Learning Processes
-
Intrinsic Gaussian Processes on Complex Constrained Domains
-
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
-
Structured Variationally Auto-encoded Optimization
-
Differentially Private Regression with Gaussian Processes
-
The Emergence of Organizing Structure in Conceptual Representation
-
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
-
Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation
-
Preferential Bayesian Optimization
-
Living Together: Mind and Machine Intelligence
-
Data Readiness Levels
-
Manifold Alignment Determination: finding correspondences across different data views
-
Topslam: Waddington Landscape Recovery for Single Cell Experiments
-
Differentially Private Gaussian Processes
-
Recurrent Gaussian Processes
-
GLASSES: Relieving The Myopia Of Bayesian Optimisation
-
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
-
Detecting Periodicities with Gaussian processes
-
Chained Gaussian Processes
-
Batch Bayesian Optimization via Local Penalization
-
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
-
Variationally Auto-Encoded Deep <span>G</span>aussian Processes
-
Genome-wide Modeling of Transcription Kinetics Reveals Patterns of RNA Production Delays
-
A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data
-
Semi-described and Semi-supervised Learning with Gaussian Processes
-
Malaria surveillance with multiple data sources using Gaussian process models
-
Consistent Mapping of Government Malaria Records Across a Changing Territory Delimitation
-
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
-
Metrics for Probabilistic Geometries
-
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data
-
Tilted Variational Bayes
-
Nested Variational Compression in Deep <span>G</span>aussian Processes
-
Fast nonparametric clustering of structured time-series
-
Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes
-
Gaussian Process Models with Parallelization and <span>GPU</span> acceleration
-
Hybrid Discriminative-Generative Approaches with <span>G</span>aussian Processes
-
Hierarchical Bayesian Modelling of Gene Expression Time Series Across Irregularly Sampled Replicates and Clusters
-
-
The Bigraphical Lasso
-
-
Deep Gaussian Processes
-
Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data
-
Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors
-
Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in <span>ALS</span> show distinct gene expression characteristics and decreased susceptibility to excitotoxicity
-
Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition
-
Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by <span>B</span>ayesian Model Comparison
-
Residual Component Analysis
-
Fast variational inference in the Conjugate Exponential family
-
<span>G</span>aussian Processes for Big Data with Stochastic Variational Inference
-
Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies
-
Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development
-
Manifold Relevance Determination
-
Kernels for Vector-Valued Functions: A Review
-
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
-
Computationally Efficient Convolved Multiple Output <span>Gaussian</span> Processes
-
tigre: Transcription Factor Inference through Gaussian Process Reconstruction of Expression for Bioconductor
-
Markov chain <span>M</span>onte <span>C</span>arlo algorithms for <span>G</span>aussian processes
-
Efficient Inference in Matrix-Variate <span>G</span>aussian Models with i.i.d. Observation Noise
-
Overlapping Mixtures of <span>G</span>aussian Processes for the Data Association Problem
-
Spectral Dimensionality Reduction via Maximum Entropy
-
<span>G</span>aussian Process Inference for Differential Equation Models of Transcriptional Regulation
-
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through <span>Gaussian</span> Process Regression
-
Residual Component Analysis
-
Accurate modeling of confounding variation in <span>eQTL</span> studies leads to a great increase in power to detect trans-regulatory effects
-
Variational <span>Gaussian</span> Process Dynamical Systems
-
Linear Latent Force Models Using <span>G</span>aussian Processes
-
Kernels for Vector-Valued Functions: a Review
-
Model-based Method for Transcription Factor Target Identification with Limited Data
-
Elementary properties of <span>CaV</span>1.3 <span>Ca</span>2+ channels expressed in mouse cochlear inner hair cells
-
Bayesian <span>G</span>aussian Process Latent Variable Model
-
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
-
Introduction to Learning and Inference in Computational Systems Biology
-
Gaussian Processes for Missing Species in Biochemical Systems
-
A Brief Introduction to <span>B</span>ayesian Inference
-
TFInfer: a tool for probabilistic inference of transcription factor activities
-
Switched Latent Force Models for Movement Segmentation
-
Efficient Multioutput <span>G</span>aussian Processes through Variational Inducing Kernels
-
Efficient Sampling for <span>G</span>aussian Process Inference using Control Variables
-
puma: a <span>B</span>ioconductor package for Propagating Uncertainty in Microarray Analysis
-
Non-Linear Matrix Factorization with <span>G</span>aussian Processes
-
Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery
-
Accelerating <span>B</span>ayesian Inference over Nonlinear Differential Equations with <span>G</span>aussian Processes
-
Variational Inducing Kernels for Sparse Convolved Multiple Output <span>G</span>aussian Processes
-
Sparse Convolved Multiple Output <span>G</span>aussian Processes
-
Latent Force Models
-
Sparse Convolved <span>G</span>aussian Processes for Multi-output Regression
-
Topologically-Constrained Latent Variable Models
-
Probabilistic approach to detecting dependencies between data sets
-
<span>G</span>aussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities
-
<span>G</span>aussian Process Latent Variable Models For Human Pose Estimation
-
Ambiguity Modeling in Latent Spaces
-
Variational Optimisation by Marginal Matching
-
Model-driven detection of Clean Speech Patches in Noise
-
Modelling transcriptional regulation using <span>G</span>aussian Processes
-
Learning for Larger Datasets with the <span>G</span>aussian Process Latent Variable Model
-
Hierarchical <span>G</span>aussian Process Latent Variable Models
-
<span>WiFi-SLAM</span> Using <span>G</span>aussian Process Latent Variable Models
-
<span>G</span>aussian Process Latent Variable Models for Fault Detection
-
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
-
Identifying submodules of cellular regulatory networks
-
Missing Data in Kernel <span>PCA</span>
-
A Probabilistic Model to Integrate Chip and Microarray Data
-
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
-
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
-
Propagating Uncertainty in Microarray Data Analysis
-
Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression
-
Large Scale Learning with the Gaussian Process Latent Variable Model
-
Gaussian Processes and the Null-Category Noise Model
-
The <span>G</span>aussian Process Latent Variable Model
-
Local Distance Preservation in the <span>GP-LVM</span> through Back Constraints
-
Fast Variational Inference for <span>G</span>aussian <span>P</span>rocess Models through <span>KL</span>-Correction
-
Probabilistic Non-linear Principal Component Analysis with <span>G</span>aussian Process Latent Variable Models
-
A Hybrid <span>MaxEnt/HMM</span> Based <span>ASR</span> System
-
A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips
-
Variational inference for <span>S</span>tudent-$t$ models: Robust <span>B</span>ayesian interpolation and generalised component analysis
-
Automatic Determination of the Number of Clusters Using Spectral Algorithms
-
Accounting for Probe-level Noise in Principal Component Analysis of Microarray Data
-
Semi-supervised Learning via <span>G</span>aussian Processes
-
MOCAP Toolbox for MATLAB
-
Extensions of the Informative Vector Machine
-
Variational Inference in <span>G</span>aussian Processes via Probabilistic Point Assimilation
-
Reducing the Variability in <span>cDNA</span> Microarray Image Processing by <span>B</span>ayesian Inference
-
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
-
Matching Kernels through <span>K</span>ullback-<span>L</span>eibler Divergence Minimisation
-
Learning to Learn with the Informative Vector Machine
-
The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine
-
Probabilistic Non-linear Principal Component Analysis with <span>G</span>aussian Process Latent Variable Models
-
<span>G</span>aussian Process Models for Visualisation of High Dimensional Data
-
Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle
-
A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays
-
Variational Inference for Visual Tracking
-
A Variational Approach to Robust <span>B</span>ayesian Interpolation
-
Fast Forward Selection to Speed Up Sparse <span>G</span>aussian Process Regression
-
<span>B</span>ayesian Processing of Microarray Images
-
Fast Sparse <span>G</span>aussian Process Methods: The Informative Vector Machine
-
Generalised Component Analysis
-
Variational Inference Guide
-
Optimising Synchronisation Times for Mobile Devices
-
Sparse <span>B</span>ayesian Learning: The Informative Vector Machine
-
A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
-
Probabilistic Modelling of Replica Divergence
-
The Structure of Neural Network Posteriors
-
Node Relevance Determination
-
Estimating a Kernel <span>F</span>isher Discriminant in the Presence of Label Noise
-
Variational Learning for Multi-layer networks of Linear Threshold Units
-
A Sparse <span>B</span>ayesian Compression Scheme — The Informative Vector Machine
-
Variational Learning for Multi-layer networks of Linear Threshold Units
-
Variational <span>B</span>ayesian Independent Component Analysis
-
A Variational Bayesian Committee of Neural Networks
-
Mixture Representations for Inference and Learning in <span>B</span>oltzmann Machines
-
Markovian inference in belief networks
-
Approximating Posterior Distributions in Belief Networks using Mixtures
-
MOCAP Toolbox for <span>MATLAB</span>