[edit]
List of Neil's Publications.
2022
Journal Papers
Bayesian learning via neural Schrödinger–Föllmer flows
; Statistics and Computing, 33(3):
[abs][Download PDF][doi]
Challenges in Machine Learning Deployment: A Survey of Case Studies
; ACM Comput. Surv., Association for Computing Machinery:
[abs][Website][arXiv version]
2021
Journal Papers
Differentially Private Regression and Classification with Sparse Gaussian Processes
; Journal of Machine Learning Research, 22(188):1-41
Solving Schrödinger Bridges via Maximum Likelihood
; Entropy, 23(9):1134
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
; Journal of Machine Learning Research, 22(8):1-51
2020
Journal Papers
Decision-making with Uncertainty
; Significance, 17(6):12-12
Conference Papers
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
; International Conference on Learning Representations, :
[abs][Download PDF][Software]
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][Publisher Website][Download PDF][doi][Data Trusts Initiative]
Intrinsic Gaussian Processes on Complex Constrained Domains
; Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(3):
[abs][Publisher Website][Download PDF][doi][Software][Supplementary Material]
Conference Papers
Variational Information Distillation for Knowledge Transfer
; Conference on Computer Vision and Pattern Recognition (CVPR), :9155-9163
[abs][Publisher Website][Download PDF][doi][Software][arXiv version]
Transferring Knowledge across Learning Processes
; International Conference on Learning Representations, :
[abs][Publisher Website][Download PDF][Software][arXiv version]
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
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
[abs][Publisher Website][Download PDF][Software][Supplementary Material]
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
Preferential Bayesian Optimization
; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1282-1291
Technical Reports
Manifold Alignment Determination: finding correspondences across different data views
; :
2016
Journal Papers
Detecting Periodicities with Gaussian processes
; PeerJ Computer Science, 4:e50
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
; Journal of Machine Learning Research, 17(42):1-62
Conference Papers
Chained Gaussian Processes
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:1431-1440
Recurrent Gaussian Processes
; Proceedings of the International Conference on Learning Representations, 3:
GLASSES: Relieving The Myopia Of Bayesian Optimisation
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:790-799
Variationally Auto-Encoded Deep Gaussian Processes
; Proceedings of the International Conference on Learning Representations, 3:
Batch Bayesian Optimization via Local Penalization
; Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:648-657
Technical Reports
Topslam: Waddington Landscape Recovery for Single Cell Experiments
; :
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
; :
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):
Conference Papers
Semi-described and Semi-supervised Learning with Gaussian Processes
; 31st Conference on Uncertainty in Artificial Intelligence (UAI), :
[abs][Download PDF]
2014
Journal Papers
Consistent Mapping of Government Malaria Records Across a Changing Territory Delimitation
; Malaria Journal, 13(Suppl 1):
Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes
; Nature Communications, 5(4890):
[abs][Download PDF][doi][Software]
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data
; PLoS Computat Biol, 10(5):
[abs][Publisher Website][Download PDF][doi][Supplementary Material]
Fast Nonparametric Clustering of Structured Time-Series
; IEEE Transactions on Pattern Analysis and Machine Intelligence, 37:383-393
Conference Papers
Malaria surveillance with multiple data sources using Gaussian process models
; 1st International Conference on the Use of Mobile ICT in Africa, :
[abs][Download PDF]
Metrics for Probabilistic Geometries
; Uncertainty in Artificial Intelligence, AUAI Press 30:800-808
[abs][Download PDF]
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, PMLR 33:47-56
Technical Reports
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
; :
Miscellaneous
2013
Journal Papers
Hierarchical Bayesian Modelling of Gene Expression Time Series Across Irregularly Sampled Replicates and Clusters
; BMC Bioinformatics, 14(252):
Linear Latent Force Models Using Gaussian Processes
; IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11):2693-2705
[abs][Download PDF][doi][Software][arXiv version]
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][Download PDF][doi]
Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors
; Bioinformatics, 29(11):1382-1389
[abs][Publisher Website][doi][Software]
Conference Papers
Gaussian Processes for Big Data
; Uncertainty in Artificial Intelligence, AUAI Press 29:
[abs][Download PDF][Software]
The Bigraphical Lasso
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1229-1237
[abs][Publisher Website][Download PDF][Software][Supplementary Material]
Deep Gaussian Processes
; Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics, PMLR 31:207-215
2012
Journal Papers
Kernels for Vector-Valued Functions: A Review
; Foundations and Trends in Machine Learning, 4(3):195-266
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][Download PDF][doi]
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
; Pattern Recognition, 10(4):
[abs][Publisher Website][doi][Software]
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][Download PDF][doi][Software]
Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development
; Nucleaic Acids Research, 40(9):3390-4001
[abs][Publisher Website][doi]
Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by Bayesian Model Comparison
; BMC Systems Biology, 6(53):
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
; Journal of Machine Learning Research, 13(51):1609-1638
Conference Papers
Fast variational inference in the Conjugate Exponential family
; Advances in Neural Information Processing Systems, 25:
Manifold Relevance Determination
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
[abs][Download PDF][Software]
Residual Component Analysis
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
[abs][Download PDF][Software]
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]
Technical Reports
Gaussian Processes for Big Data with Stochastic Variational Inference
; Submitted to NIPS 2012 Workshop, :
[abs]
2011
Journal Papers
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
; BMC Bioinformatics, 12(180):
[abs][Publisher Website][Download PDF][doi][Software][Bioconductor][GPTK R Toolkit]
Computationally Efficient Convolved Multiple Output Gaussian Processes
; Journal of Machine Learning Research, 12:1425-1466
[abs][Download PDF][Software]
tigre: Transcription Factor Inference through Gaussian Process Reconstruction of Expression for Bioconductor
; Bioinformatics, 27:1026-1027
Conference Papers
Efficient Inference in Matrix-Variate Gaussian Models with i.i.d. Observation Noise
; Neural Information Processing Systems, :630-638
Spectral Dimensionality Reduction via Maximum Entropy
; Proceedings of the Fourteenth International Workshop on Artificial Intelligence and Statistics, PMLR 15:51-59
Variational Gaussian Process Dynamical Systems
; Advances in Neural Information Processing Systems, MIT Press 24:
[abs][Publisher Website][Download PDF][Software][Supplementary Videos]
Book Chapters
Markov Chain Monte Carlo Algorithms for Gaussian Processes
; Bayesian Time Series Models, Cambridge University Press:
[abs]
Technical Reports
Linear Latent Force Models Using Gaussian Processes
; :
Kernels for Vector-Valued Functions: a Review
; :
[abs][Publisher Website][Download PDF][Software][arXiv version]
Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects
; :
2010
Journal Papers
TFInfer: a tool for probabilistic inference of transcription factor activities
; Bioinformatics, 26:2635-2636
Model-based Method for Transcription Factor Target Identification with Limited Data
; Proc. Natl. Acad. Sci. USA, 107(17):7793-7798
Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells
; The Journal of Physiology, 588:187-189
[abs][Publisher Website][doi]
Conference Papers
Bayesian Gaussian Process Latent Variable Model
; Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, PMLR 9:844-851
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
; Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, PMLR 9:25-32
Switched Latent Force Models for Movement Segmentation
; Advances in Neural Information Processing Systems, MIT Press 23:55-63
[abs][Download PDF]
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
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
; :
2009
Journal Papers
puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
; BMC Bioinformatics, 10(211):
Conference Papers
Non-Linear Matrix Factorization with Gaussian Processes
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 26:
[abs][Download PDF][Software]
Latent Force Models
; Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, PMLR 5:9-16
Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery
; British Machine Vision Conference, :
[abs][Download PDF][Software]
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
; Advances in Neural Information Processing Systems, MIT Press 21:217-224
Technical Reports
Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
; :
Sparse Convolved Multiple Output Gaussian Processes
; , :
[abs][Publisher Website][Download PDF][Software][arXiv version]
2008
Journal Papers
Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities
; Bioinformatics, 24:i70-i75
Conference Papers
Efficient Sampling for Gaussian Process Inference using Control Variables
; Advances in Neural Information Processing Systems, MIT Press 21:1681-1688
[abs][Publisher Website][Download PDF][Supplementary Material]
Sparse Convolved Gaussian Processes for Multi-output Regression
; Advances in Neural Information Processing Systems, MIT Press 21:57-64
Ambiguity Modeling in Latent Spaces
; Machine Learning for Multimodal Interaction (MLMI 2008), Springer-Verlag:62-73
Topologically-Constrained Latent Variable Models
; Proceedings of the International Conference in Machine Learning, Omnipress 25:1080-1087
[abs][Download PDF][doi]
Gaussian Process Latent Variable Models For Human Pose Estimation
; Machine Learning for Multimodal Interaction (MLMI 2007), Springer-Verlag 4892:132-143
[abs][Download PDF][doi][Software]
2007
Conference Papers
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
Modelling transcriptional regulation using Gaussian Processes
; Advances in Neural Information Processing Systems, MIT Press 19:785-792
[abs][Download PDF][Software]
Hierarchical Gaussian Process Latent Variable Models
; Proceedings of the International Conference in Machine Learning, Omnipress 24:481-488
[abs][Download PDF][Software]
WiFi-SLAM Using Gaussian Process Latent Variable Models
; Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), :2480-2485
[abs][Download PDF]
Miscellaneous
2006
Journal Papers
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
; Journal of Machine Learning Research, 7:455-491
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
; Bioinformatics, 22(22):2275-2281
Conference Papers
Local Distance Preservation in the GP-LVM through Back Constraints
; Proceedings of the International Conference in Machine Learning, Omnipress 23:513-520
[abs][Download PDF][doi][Software]
Missing Data in Kernel PCA
; ECML, Berlin, 2006, Springer-Verlag:751-758
[abs][Download PDF][Software]
Identifying submodules of cellular regulatory networks
; International Conference on Computational Methods in Systems Biology, Springer-Verlag:
Fast Variational Inference for Gaussian Process Models through KL-Correction
; ECML, Berlin, 2006, Springer-Verlag:270-281
[abs][Download PDF][Software]
Book Chapters
Gaussian Processes and the Null-Category Noise Model
; Semi-supervised Learning, MIT Press:152-165
Technical Reports
Large Scale Learning with the Gaussian Process Latent Variable Model
; (CS-06-05):
[abs][Download PDF][Software]
The Gaussian Process Latent Variable Model
; (CS-06-03):
[abs][Download PDF][Paper Source Files][Software][Main Software]
A Probabilistic Model to Integrate Chip and Microarray Data
; (CS-06-02):
[abs][Download PDF][Software]
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][doi][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][Download PDF][Software][psgz]
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][doi][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
Gaussian Process Models for Visualisation of High Dimensional Data
; Advances in Neural Information Processing Systems, MIT Press 16:329-336
[abs][Publisher Website][Download PDF][Paper Source Files][Software][psgz]
Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle
; International Conference on Acoustics, Speech and Signal Processing, :
[abs][Download PDF]
Technical Reports
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
; (CS-04-08):
[abs][Download PDF][Software][psgz]
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
; (CS-04-13):
[abs][Download PDF][Software][psgz]
Matching Kernels through Kullback-Leibler Divergence Minimisation
; (CS-04-12):
[abs][Download PDF][psgz]
The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine
; (CS-04-07):
2003
Journal Papers
A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays
; Biochemical Transations, 31(6):1510-1512
[abs][Download PDF]
Conference Papers
Bayesian Processing of Microarray Images
; Neural Networks for Signal Processing XIII, IEEE:71-80
[abs][Publisher Website][Download PDF][doi][Software][psgz]
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][Download PDF][Software]
A Variational Approach to Robust Bayesian Interpolation
; Neural Networks for Signal Processing XIII, IEEE:229-238
[abs][Download PDF]
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
; Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, :
[abs][Download PDF][psgz]
Fast Sparse Gaussian Process Methods: The Informative Vector Machine
; Advances in Neural Information Processing Systems, MIT Press 15:625-632
Technical Reports
2002
Conference Papers
Optimising Synchronisation Times for Mobile Devices
; Advances in Neural Information Processing Systems, MIT Press 14:1401-1408
[abs][Download PDF]
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][Download PDF][psgz]
Conference Papers
Probabilistic Modelling of Replica Divergence
; Proceedings of the 8th Workshop on Hot Topics in Operating Systems HOTOS (VIII), :
[abs][Download PDF]
Node Relevance Determination
; The {XI}th Italian Workshop on Neural Networks, Springer-Verlag:
[abs][Download PDF][Software]
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 18:
[abs][Download PDF][Software][]
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][Download PDF][psgz]
Technical Reports
Miscellaneous
2000
Technical Reports
1999
Technical Reports
1998
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