Neil D. Lawrence

According to our database, Neil D. Lawrence authored at least 170 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On Feature Learning for Titi Monkey Activity Detection.

Aditya Ravuri, Jen Muir, Neil D. Lawrence

CoRR, 2024

Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE.

Aditya Ravuri, Neil D. Lawrence

CoRR, 2024

Requirements are All You Need: The Final Frontier for End-User Software Engineering.

Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen

CoRR, 2024

Scalable Amortized GPLVMs for Single Cell Transcriptomics Data.

Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence

CoRR, 2024

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation.

Christian Cabrera, Andrei Paleyes, Neil D. Lawrence

Proceedings of the IEEE/ACM International Workshop New Trends in Software Architecture, 2024

Can causality accelerate experimentation in software systems?

Andrei Paleyes, Han-Bo Li, Neil D. Lawrence

Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering, 2024

2023

Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134.

Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence

Entropy, February, 2023

Bayesian learning via neural Schrödinger-Föllmer flows.

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Stat. Comput., 2023

Challenges in Deploying Machine Learning: A Survey of Case Studies.

Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

ACM Comput. Surv., 2023

Automated discovery of trade-off between utility, privacy and fairness in machine learning models.

Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes

CoRR, 2023

Dimensionality Reduction as Probabilistic Inference.

Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence

CoRR, 2023

AI for Science: An Emerging Agenda.

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery

CoRR, 2023

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective.

Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence

CoRR, 2023

Causal fault localisation in dataflow systems.

Andrei Paleyes, Neil David Lawrence

Proceedings of the 3rd Workshop on Machine Learning and Systems, 2023

Dataflow graphs as complete causal graphs.

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

Proceedings of the 2nd IEEE/ACM International Conference on AI Engineering, 2023

2022

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382).

Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery

Dagstuhl Reports, September, 2022

Ice Core Dating using Probabilistic Programming.

Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser

CoRR, 2022

The Effect of Task Ordering in Continual Learning.

Samuel J. Bell, Neil D. Lawrence

CoRR, 2022

Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data.

Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo

CoRR, 2022

Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference.

Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

CoRR, 2022

Modeling the Machine Learning Multiverse.

Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs.

Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence

Proceedings of the Machine Learning in Computational Biology, 21-22 November 2022, Online, 2022

An empirical evaluation of flow based programming in the machine learning deployment context.

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 2022

Two-way Sparse Network Inference for Count Data.

Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Generalised GPLVM with Stochastic Variational Inference.

Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Differentially Private Regression and Classification with Sparse Gaussian Processes.

Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence

J. Mach. Learn. Res., 2021

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis.

Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek

J. Mach. Learn. Res., 2021

Solving Schrödinger Bridges via Maximum Likelihood.

Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence

Entropy, 2021

Emulation of physical processes with Emukit.

Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González

CoRR, 2021

Behavioral Experiments for Understanding Catastrophic Forgetting.

Samuel J. Bell, Neil D. Lawrence

CoRR, 2021

Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment.

Corinna Cortes, Neil D. Lawrence

CoRR, 2021

Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures.

Andrei Paleyes, Christian Cabrera, Neil D. Lawrence

CoRR, 2021

Benchmarking Real-Time Reinforcement Learning.

Pierre Thodoroff, Wenyu Li, Neil D. Lawrence

Proceedings of the NeurIPS 2021 Workshop on Pre-Registration in Machine Learning, 2021

2020

Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes.

Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan

IEEE Trans. Ind. Informatics, 2020

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients.

Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems.

Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence

IEEE Trans. Autom. Control., 2019

Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design.

Neil D. Lawrence

CoRR, 2019

Deep Gaussian Processes for Multi-fidelity Modeling.

Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González

CoRR, 2019

A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant.

Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan

Appl. Soft Comput., 2019

Usability of Probabilistic Programming Languages.

Alan F. Blackwell, Luke Church, Martin Erwig, James Geddes, Andy Gordon, Maria I. Gorinova, Atilim Gunes Baydin, Bradley Gram-Hansen, Tobias Kohn, Neil D. Lawrence, Vikash Mansinghka, Brooks Paige, Tomas Petricek, Diana Robinson, Advait Sarkar, Oliver Strickson

Proceedings of the 30th Annual Workshop of the Psychology of Programming Interest Group, 2019

Meta-Surrogate Benchmarking for Hyperparameter Optimization.

Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Transferring Knowledge across Learning Processes.

Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou

Proceedings of the 7th International Conference on Learning Representations, 2019

Variational Information Distillation for Knowledge Transfer.

Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Gaussian Process Regression for Binned Data.

Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence

CoRR, 2018

Intrinsic Gaussian processes on complex constrained domains.

Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson

CoRR, 2018

Structured Variationally Auto-encoded Optimization.

Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence

Proceedings of the 35th International Conference on Machine Learning, 2018

Differentially Private Regression with Gaussian Processes.

Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Auto-Differentiating Linear Algebra.

Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence

CoRR, 2017

Living Together: Mind and Machine Intelligence.

Neil D. Lawrence

CoRR, 2017

Data Readiness Levels.

Neil D. Lawrence

CoRR, 2017

Manifold Alignment Determination: finding correspondences across different data views.

Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek

CoRR, 2017

Efficient inference for sparse latent variable models of transcriptional regulation.

Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray

Bioinform., 2017

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes.

Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Parallelizable sparse inverse formulation Gaussian processes (SpInGP).

Alexander Grigorievskiy, Neil D. Lawrence, Simo Särkkä

Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

Preferential Bayesian Optimization.

Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Detecting periodicities with Gaussian processes.

Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence

PeerJ Prepr., 2016

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

J. Mach. Learn. Res., 2016

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model.

Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence

CoRR, 2016

Differentially Private Gaussian Processes.

Michael T. Smith, Max Zwiessele, Neil D. Lawrence

CoRR, 2016

Recurrent Gaussian Processes.

César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

Proceedings of the 4th International Conference on Learning Representations, 2016

The Emergence of Organizing Structure in Conceptual Representation.

Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum

CoRR, 2016

Variational Auto-encoded Deep Gaussian Processes.

Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence

Proceedings of the 4th International Conference on Learning Representations, 2016

An integrated probabilistic framework for robot perception, learning and memory.

Uriel Martinez-Hernandez, Andreas C. Damianou, Daniel Camilleri, Luke W. Boorman, Neil D. Lawrence, Tony J. Prescott

Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics, 2016

iCub Visual Memory Inspector: Visualising the iCub's Thoughts.

Daniel Camilleri, Andreas C. Damianou, Harry Jackson, Neil D. Lawrence, Tony J. Prescott

Proceedings of the Biomimetic and Biohybrid Systems - 5th International Conference, 2016

A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity.

Muhammad Arifur Rahman, Neil D. Lawrence

Proceedings of the 7th ACM International Conference on Bioinformatics, 2016

Chained Gaussian Processes.

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

GLASSES: Relieving The Myopia Of Bayesian Optimisation.

Javier González, Michael A. Osborne, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Batch Bayesian Optimization via Local Penalization.

Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Fast Nonparametric Clustering of Structured Time-Series.

James Hensman, Magnus Rattray, Neil D. Lawrence

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Spike and Slab Gaussian Process Latent Variable Models.

Zhenwen Dai, James Hensman, Neil D. Lawrence

CoRR, 2015

A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data.

Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego di Bernardo

BMC Bioinform., 2015

Semi-described and semi-supervised learning with Gaussian processes.

Andreas C. Damianou, Neil D. Lawrence

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes.

Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence

Proceedings of the Advanced Analysis and Learning on Temporal Data, 2015

Monitoring Short Term Changes of Malaria Incidence in Uganda with Gaussian Processes.

Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence

Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data, 2015

A Top-Down Approach for a Synthetic Autobiographical Memory System.

Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott

Proceedings of the Biomimetic and Biohybrid Systems - 4th International Conference, 2015

2014

Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data.

Ciira Wa Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray

PLoS Comput. Biol., 2014

Fast variational inference for nonparametric clustering of structured time-series.

James Hensman, Magnus Rattray, Neil D. Lawrence

CoRR, 2014

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

CoRR, 2014

Gaussian Process Models with Parallelization and GPU acceleration.

Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence

CoRR, 2014

Metrics for Probabilistic Geometries.

Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Hybrid Discriminative-Generative Approach with Gaussian Processes.

Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Tilted Variational Bayes.

James Hensman, Max Zwiessele, Neil D. Lawrence

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Gaussian Processes for Natural Language Processing.

Trevor Cohn, Daniel Preotiuc-Pietro, Neil D. Lawrence

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014

2013

Linear Latent Force Models Using Gaussian Processes.

Mauricio A. Álvarez, David Luengo, Neil D. Lawrence

IEEE Trans. Pattern Anal. Mach. Intell., 2013

Preface: Intelligent interactive data visualization.

Barbara Hammer, Daniel A. Keim, Neil D. Lawrence, Guy Lebanon

Data Min. Knowl. Discov., 2013

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

James Hensman, Neil D. Lawrence, Magnus Rattray

BMC Bioinform., 2013

Detecting regulatory gene-environment interactions with unmeasured environmental factors.

Nicoló Fusi, Christoph Lippert, Karsten M. Borgwardt, Neil D. Lawrence, Oliver Stegle

Bioinform., 2013

Gaussian Processes for Big Data.

James Hensman, Nicoló Fusi, Neil D. Lawrence

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

The Bigraphical Lasso.

Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou

Proceedings of the 30th International Conference on Machine Learning, 2013

Deep Gaussian Processes.

Andreas C. Damianou, Neil D. Lawrence

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Overlapping Mixtures of Gaussian Processes for the data association problem.

Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence

Pattern Recognit., 2012

Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition.

Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman

PLoS Comput. Biol., 2012

Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies.

Nicoló Fusi, Oliver Stegle, Neil D. Lawrence

PLoS Comput. Biol., 2012

Editor's Note.

Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Preface.

Neil D. Lawrence, Mark A. Girolami

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

A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models.

Neil D. Lawrence

J. Mach. Learn. Res., 2012

Kernels for Vector-Valued Functions: A Review.

Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence

Found. Trends Mach. Learn., 2012

Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models

Alfredo A. Kalaitzis, Neil D. Lawrence

CoRR, 2012

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.

Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray

BMC Syst. Biol., 2012

Fast Variational Inference in the Conjugate Exponential Family.

James Hensman, Magnus Rattray, Neil D. Lawrence

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

Residual Components Analysis.

Alfredo A. Kalaitzis, Neil D. Lawrence

Proceedings of the 29th International Conference on Machine Learning, 2012

Manifold Relevance Determination.

Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Spectral Dimensionality Reduction via Maximum Entropy.

Neil D. Lawrence

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Computationally Efficient Convolved Multiple Output Gaussian Processes.

Mauricio A. Álvarez, Neil D. Lawrence

J. Mach. Learn. Res., 2011

Residual Component Analysis

Alfredo A. Kalaitzis, Neil D. Lawrence

CoRR, 2011

A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression.

Alfredo A. Kalaitzis, Neil D. Lawrence

BMC Bioinform., 2011

tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor.

Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence

Bioinform., 2011

Efficient inference in matrix-variate Gaussian models with \iid observation noise.

Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Variational Gaussian Process Dynamical Systems.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Model-based method for transcription factor target identification with limited data.

Antti Honkela, Charles Girardot, Eleanor Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray

Proc. Natl. Acad. Sci. USA, 2010

Bayesian Gaussian Process Latent Variable Model.

Michalis K. Titsias, Neil D. Lawrence

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels.

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction

Neil D. Lawrence

CoRR, 2010

TFInfer: a tool for probabilistic inference of transcription factor activities.

Hafiz Muhammad Shahzad Asif, Matthew D. Rolfe, Jeffrey Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti

Bioinform., 2010

Switched Latent Force Models for Movement Segmentation.

Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Invited Talk Abstracts.

Yi Ma, Fei Sha, Lawrence Carin, Gilad Lerman, Neil D. Lawrence

Proceedings of the Manifold Learning and Its Applications, 2010

Gaussian Processes for Missing Species in Biochemical Systems.

Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias

Proceedings of the Learning and Inference in Computational Systems Biology., 2010

A Brief Introduction to Bayesian Inference.

Neil D. Lawrence, Magnus Rattray

Proceedings of the Learning and Inference in Computational Systems Biology., 2010

2009

Latent Force Models.

Mauricio A. Álvarez, David Luengo, Neil D. Lawrence

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

puma: a Bioconductor package for propagating uncertainty in microarray analysis.

Richard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray

BMC Bioinform., 2009

Non-linear matrix factorization with Gaussian processes.

Neil D. Lawrence, Raquel Urtasun

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery.

John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence

Proceedings of the British Machine Vision Conference, 2009

2008

Efficient Sampling for Gaussian Process Inference using Control Variables.

Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes.

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

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Sparse Convolved Gaussian Processes for Multi-output Regression.

Mauricio A. Álvarez, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Ambiguity Modeling in Latent Spaces.

Carl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence

Proceedings of the Machine Learning for Multimodal Interaction, 5th International Workshop, 2008

Topologically-constrained latent variable models.

Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence

Proceedings of the Machine Learning, 2008

Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities.

Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence

Proceedings of the ECCB'08 Proceedings, 2008

2007

Learning for Larger Datasets with the Gaussian Process Latent Variable Model.

Neil D. Lawrence

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Gaussian Process Latent Variable Models for Human Pose Estimation.

Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence

Proceedings of the Machine Learning for Multimodal Interaction , 2007

Model-driven detection of clean speech patches in noise.

Jonathan Laidler, Martin Cooke, Neil D. Lawrence

Proceedings of the 8th Annual Conference of the International Speech Communication Association, 2007

WiFi-SLAM Using Gaussian Process Latent Variable Models.

Brian Ferris, Dieter Fox, Neil D. Lawrence

Proceedings of the IJCAI 2007, 2007

Hierarchical Gaussian process latent variable models.

Neil D. Lawrence, Andrew J. Moore

Proceedings of the Machine Learning, 2007

Modeling Human Locomotion with Topologically Constrained Latent Variable Models.

Raquel Urtasun, David J. Fleet, Neil D. Lawrence

Proceedings of the Human Motion, 2007

Gaussian Process Latent Variable Models for Fault Detection.

Luka Eciolaza, Muhammad Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2007

2006

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis.

Tonatiuh Peña Centeno, Neil D. Lawrence

J. Mach. Learn. Res., 2006

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription.

Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence

Bioinform., 2006

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities.

Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray

Bioinform., 2006

Probe-level measurement error improves accuracy in detecting differential gene expression.

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

Bioinform., 2006

Propagating uncertainty in microarray data analysis.

Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence

Briefings Bioinform., 2006

Modelling transcriptional regulation using Gaussian Processes.

Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Local distance preservation in the GP-LVM through back constraints.

Neil D. Lawrence, Joaquin Quiñonero Candela

Proceedings of the Machine Learning, 2006

Missing Data in Kernel PCA.

Guido Sanguinetti, Neil D. Lawrence

Proceedings of the Machine Learning: ECML 2006, 2006

Fast Variational Inference for Gaussian Process Models Through KL-Correction.

Nathaniel John King, Neil D. Lawrence

Proceedings of the Machine Learning: ECML 2006, 2006

Identifying Submodules of Cellular Regulatory Networks.

Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence

Proceedings of the Computational Methods in Systems Biology, International Conference, 2006

Gaussian Processes and the Null-Category Noise Model.

Neil D. Lawrence, Michael I. Jordan

Proceedings of the Semi-Supervised Learning, 2006

2005

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models.

Neil D. Lawrence

J. Mach. Learn. Res., 2005

Variational inference for Student-<i>t</i> models: Robust Bayesian interpolation and generalised component analysis.

Michael E. Tipping, Neil D. Lawrence

Neurocomputing, 2005

Accounting for probe-level noise in principal component analysis of microarray data.

Guido Sanguinetti, Marta Milo, Magnus Rattray, Neil D. Lawrence

Bioinform., 2005

A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips.

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

Bioinform., 2005

A hybrid Maxent/HMM based ASR system.

Yasser Hifny, Steve Renals, Neil D. Lawrence

Proceedings of the 9th European Conference on Speech Communication and Technology, 2005

2004

Reducing the variability in cDNA microarray image processing by Bayesian inference.

Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier

Bioinform., 2004

Semi-supervised Learning via Gaussian Processes.

Neil D. Lawrence, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Learning to learn with the informative vector machine.

Neil D. Lawrence, John C. Platt

Proceedings of the Machine Learning, 2004

Acoustic space dimensionality selection and combination using the maximum entropy principle.

Yasser H. Abdel-Haleem, Steve Renals, Neil D. Lawrence

Proceedings of the 2004 IEEE International Conference on Acoustics, 2004

Extensions of the Informative Vector Machine.

Neil D. Lawrence, John C. Platt, Michael I. Jordan

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

2003

A variational approach to robust Bayesian interpolation.

Michael E. Tipping, Neil D. Lawrence

Proceedings of the NNSP 2003, 2003

Bayesian processing of microarray images.

Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier

Proceedings of the NNSP 2003, 2003

Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data.

Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Variational Inference for Visual Tracking.

Jaco Vermaak, Neil D. Lawrence, Patrick Pérez

Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), 2003

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression.

Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

Fast Sparse Gaussian Process Methods: The Informative Vector Machine.

Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics.

Boaz Lerner, Neil D. Lawrence

Neural Comput. Appl., 2001

Note Relevance Determination.

Neil D. Lawrence

Proceedings of the 12th Italian Workshop on Neural Nets, 2001

Optimising Synchronisation Times for Mobile Devices.

Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise.

Neil D. Lawrence, Bernhard Schölkopf

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

Probabilistic Modelling of Replica Divergence.

Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop

Proceedings of HotOS-VIII: 8th Workshop on Hot Topics in Operating Systems, 2001

Variational Learning for Multi-Layer Networks of Linear Threshold Units.

Neil D. Lawrence

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

1998

Mixture Representations for Inference and Learning in Boltzmann Machines.

Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

1997

Approximating Posterior Distributions in Belief Networks Using Mixtures.

Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 10, 1997