Neil D. Lawrence

Patrick Pérez, Neil D. Lawrence, Jaco Vermaak, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

Conference on Biomimetic and Biohybrid Systems

Neil D. Lawrence, Tony J. Prescott, Carl Henrik Ek, 2015 .

Deep Gaussian Processes for Multi-fidelity Modeling pdf

Neil D. Lawrence, Javier González, Kurt Cutajar, 2019, ArXiv.

Reducing the variability in cDNA microarray image processing by Bayesian inference

Neil D. Lawrence, Mahesan Niranjan, Marta Milo, 2004, Bioinform..

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Neil D. Lawrence, David Luengo, Mauricio A. Álvarez, 2010, AISTATS.

Introduction to Dataset Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Advances in Intelligent Data Analysis XIV

Bernd Bischl, Neil D. Lawrence, Michèle Sebag, 2015, Lecture Notes in Computer Science.

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

Neil D. Lawrence, Neil D. Lawrence, 2005, J. Mach. Learn. Res..

A Variational B ayesian Committee of Neural Networks

Neil D. Lawrence, Mehdi Azzouzi, Neil D. Lawrence, 1999 .

Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2015 .

On the Training/Test Distributions Gap: A Data Representation Learning Framework

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Sparse B ayesian Learning: The Informative Vector Machine

Neil D. Lawrence, Ralf Herbrich, Matthias Seeger, 2002 .

Model-driven detection of clean speech patches in noise

Neil D. Lawrence, Martin Cooke, Jonathan Laidler, 2007, INTERSPEECH.

Topologically-constrained latent variable models

David J. Fleet, Neil D. Lawrence, Trevor Darrell, 2008, ICML '08.

puma User Guide

Neil D. Lawrence, Richard D. Pearson, Marta Milo, 2007 .

Advances in Neural Information Processing Systems 14

Neil D. Lawrence, Christopher M. Bishop, Michael J. Taylor, 2002 .

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

Neil D. Lawrence, Christopher A. Penfold, Paul E. Brown, 2012, PLoS Comput. Biol..

The Bigraphical Lasso

Neil D. Lawrence, John D. Lafferty, Shuheng Zhou, 2013, ICML.

Manifold Alignment Determination: finding correspondences across different data views pdf

Neil D. Lawrence, Carl Henrik Ek, Andreas C. Damianou, 2017, ArXiv.

Manifold Alignment Determination

Neil D. Lawrence, Carl Henrik Ek, Andreas Damianou, 2015 .

Bayesian processing of microarray images

Neil D. Lawrence, Mahesan Niranjan, Marta Milo, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Switched Latent Force Models for Movement Segmentation

Bernhard Schölkopf, Neil D. Lawrence, Jan Peters, 2010, NIPS.

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

Neil D. Lawrence, Alfredo A. Kalaitzis, Neil D. Lawrence, 2011, BMC Bioinformatics.

A variational approach to robust Bayesian interpolation

Neil D. Lawrence, Michael E. Tipping, N. Lawrence, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

When Training and Test Sets Are Different: Characterizing Learning Transfer

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

TFInfer: a tool for probabilistic inference of transcription factor activities

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2010, Bioinform..

Metrics for Probabilistic Geometries

Neil D. Lawrence, Søren Hauberg, Alfredo Vellido, 2014, UAI.

Human Motion Modelling through Dimensional Reduction with Gaussian Processes

Neil D. Lawrence, Carl Henrik Ek, Philip H. S. Torr, 2008 .

Editor's Note

Jiri Matas, Neil D. Lawrence, Max Welling, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Neil D. Lawrence, Zhenwen Dai, Carl Henrik Ek, 2016, ArXiv.

MEDITERRANEAN CONFERENCE ON INTELLIGENT SYSTEMS AND AUTOMATION

Neil D. Lawrence, Chris Melhuish, Carl Henrik Ek, 2009 .

A probabilistic model to integrate chip and microarray data

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006 .

Identifying Submodules of Cellular Regulatory Networks

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, CMSB.

Mixture Representations for Inference and Learning in Boltzmann Machines pdf

Neil D. Lawrence, Michael I. Jordan, Christopher M. Bishop, 1998, UAI.

Hierarchical G aussian Process Latent Variable Models

Neil D. Lawrence, Andrew J. Moore, Neil D. Lawrence, 2007, ICML 2007.

Introduction to Learning and Inference in Computational Systems Biology

Neil D. Lawrence, Neil D. Lawrence, 2010 .

Variational Auto-encoded Deep Gaussian Processes pdf

Neil D. Lawrence, Zhenwen Dai, Javier González, 2015, ICLR.

Latent Force Models: Introduction

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2013 .

An Introduction to Systems Biology from a Machine Learning Perspective II

Neil D. Lawrence, Antti Honkela, Michalis K. Titsias, 2009 .

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

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2005, Bioinform..

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

Neil D. Lawrence, Antti Honkela, Magnus Rattray, 2008, ECCB.

The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine

Neil D. Lawrence, Ralf Herbrich, Matthias Seeger, 2004 .

Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data

Neil D. Lawrence, Neil D. Lawrence, 2003, NIPS.

Transferring Knowledge across Learning Processes pdf

Neil D. Lawrence, Sebastian Flennerhag, Andreas C. Damianou, 2018, ICLR.

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

Neil D. Lawrence, Alfredo A. Kalaitzis, Neil D. Lawrence, 2012, ICML 2012.

Gaussian Processes for Big Data pdf

Neil D. Lawrence, James Hensman, Nicoló Fusi, 2013, UAI.

Gaussian Process Latent Variable Models for Fault Detection

Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming, 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients pdf

Neil D. Lawrence, Xi Shen, Guillaume Obozinski, 2020, ICLR.

A Sparse B ayesian Compression Scheme — The Informative Vector Machine

Neil D. Lawrence, Ralf Herbrich, 2001 .

Learning and Inference in Computational Systems Biology

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2010, Computational molecular biology.

Sparse Convolved Gaussian Processes for Multi-output Regression

Neil D. Lawrence, Mauricio A. Álvarez, Neil D. Lawrence, 2008, NIPS.

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

Neil D. Lawrence, Magnus Rattray, James Hensman, 2013, BMC Bioinformatics.

Manifold Relevance Determination pdf

Neil D. Lawrence, Carl Henrik Ek, Michalis K. Titsias, 2012, ICML.

Learning to learn with the informative vector machine

Neil D. Lawrence, John C. Platt, Neil D. Lawrence, 2004, ICML.

Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures

Neil D. Lawrence, Mahesan Niranjan, Joab R. Winkler, 2005, Deterministic and Statistical Methods in Machine Learning.

Modular Deep Probabilistic Programming

Neil D. Lawrence, Zhenwen Dai, Eric Meissner, 2018 .

A hybrid Maxent/HMM based ASR system

Neil D. Lawrence, Steve Renals, Yasser Hifny, 2005, INTERSPEECH.

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

Neil D. Lawrence, Antti Honkela, Magnus Rattray, 2012, BMC Systems Biology.

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

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2005, Bioinform..

iCub Visual Memory Inspector: Visualising the iCub's Thoughts

Neil D. Lawrence, Tony J. Prescott, Daniel Camilleri, 2016, Living Machines.

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

Neil D. Lawrence, Raoul-Gabriel Urma, Andrei Paleyes, 2020, ACM Comput. Surv..

Auto-Differentiating Linear Algebra pdf

Neil D. Lawrence, Zhenwen Dai, Matthias W. Seeger, 2017, ArXiv.

Ranking of gene regulators through differential equations and Gaussian processes

Neil D. Lawrence, Antti Honkela, Magnus Rattray, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

Optimising Synchronisation Times for Mobile Devices

Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop, 2001, NIPS.

TP1: Leveraging Complex Prior Knowledge in Learning

Neil D. Lawrence, Marc Dymetman, Neil D. Lawrence, 2008 .

Gaussian Process Regression for Binned Data pdf

Neil D. Lawrence, Mauricio A. Álvarez, Michael Thomas Smith, 2018, ArXiv.

Approximating Posterior Distributions in Belief Networks Using Mixtures

Neil D. Lawrence, Michael I. Jordan, Tommi S. Jaakkola, 1997, NIPS.

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

Neil D. Lawrence, Antti Honkela, Magnus Rattray, 2013, PLoS Comput. Biol..

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

Neil D. Lawrence, Neil D. Lawrence, 2010, J. Mach. Learn. Res..

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

Neil D. Lawrence, Matthias W. Seeger, Christopher K. I. Williams, 2003, AISTATS.

Theoretical Views on Dataset and Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Variational Inference in G aussian Processes via Probabilistic Point Assimilation

Neil D. Lawrence, Nathaniel J. King, N. Lawrence, 2005 .

Efficient inference for sparse latent variable models of transcriptional regulation

Neil D. Lawrence, Magnus Rattray, Zhenwen Dai, 2017, Bioinform..

Structured Variationally Auto-encoded Optimization

Neil D. Lawrence, Zhenwen Dai, Xiaoyu Lu, 2018, ICML.

WiFi-SLAM Using Gaussian Process Latent Variable Models

Neil D. Lawrence, Dieter Fox, Brian Ferris, 2007, IJCAI.

G aussian Process Models for Visualisation of High Dimensional Data

Neil D. Lawrence, N. Lawrence, 2004, NIPS 2004.

GLASSES: Relieving The Myopia Of Bayesian Optimisation

Neil D. Lawrence, Michael A. Osborne, Javier González, 2015, AISTATS.

Semi-supervised Learning via Gaussian Processes

Neil D. Lawrence, Michael I. Jordan, Neil D. Lawrence, 2004, NIPS.

Algorithms for Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

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

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2006, Bioinform..

Detecting periodicities with Gaussian processes

Neil D. Lawrence, Magnus Rattray, James Hensman, 2016, PeerJ Comput. Sci..

Variationally Auto-Encoded Deep G aussian Processes

Neil D. Lawrence, Zhenwen Dai, Andreas Damianou, 2016, International Conference on Learning Representations.

Gaussian Processes for Natural Language Processing

Neil D. Lawrence, Trevor Cohn, Daniel Preotiuc-Pietro, 2014, ACL.

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

Neil D. Lawrence, Michalis K. Titsias, Andreas C. Damianou, 2014, ArXiv.

Propagating uncertainty in microarray data analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2006, Briefings Bioinform..

International workshop on machine learning for multimodal interaction

Neil D. Lawrence, Grégory Rogez, Carl Henrik Ek, 2007 .

Local distance preservation in the GP-LVM through back constraints

Joaquin Quiñonero Candela, Neil D. Lawrence, Neil D. Lawrence, 2006, ICML.

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

Neil D. Lawrence, Simo Särkkä, Alexander Grigorievskiy, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

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

Neil D. Lawrence, Antti Honkela, Magnus Rattray, 2011, Bioinform..

Note Relevance Determination

Neil D. Lawrence, 2001, WIRN.

Acoustic space dimensionality selection and combination using the maximum entropy principle

Neil D. Lawrence, Steve Renals, Yasser H. Abdel-Haleem, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

Neil D. Lawrence, Tonatiuh Peña Centeno, Neil D. Lawrence, 2006, J. Mach. Learn. Res..

Data Readiness Levels pdf

Neil D. Lawrence, Neil D. Lawrence, 2017, ArXiv.

A Top-Down Approach for a Synthetic Autobiographical Memory System

Neil D. Lawrence, Tony J. Prescott, Carl Henrik Ek, 2015, Living Machines.

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Neil D. Lawrence, Zhenwen Dai, Aaron Klein, 2019, NeurIPS.

Gaussian Processes for Missing Species in Biochemical Systems

Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias, 2010, Learning and Inference in Computational Systems Biology.

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

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

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

Neil D. Lawrence, John A. Quinn, Martin Gordon Mubangizi, 2015, AALTD@PKDD/ECML.

Human Motion Modelling with Gaussian Processes

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2008 .

Differentially Private Regression with Gaussian Processes

Neil D. Lawrence, Mauricio A. Álvarez, Max Zwiessele, 2018, AISTATS.

Recurrent Gaussian Processes pdf

Neil D. Lawrence, Zhenwen Dai, Guilherme De A. Barreto, 2015, ICLR.

Semi-described and semi-supervised learning with Gaussian processes

Neil D. Lawrence, Andreas C. Damianou, Neil D. Lawrence, 2015, UAI.

Editor's Note

Jiri Matas, Neil D. Lawrence, Max Welling, 2012, IEEE Trans. Pattern Anal. Mach. Intell..

On Bayesian Transduction: Implications for the Covariate Shift Problem

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Notation and Symbols

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity

Neil D. Lawrence, Muhammad Arifur Rahman, 2016, BCB.

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

Neil D. Lawrence, Diego di Bernardo, Gennaro Gambardella, 2015, BMC Bioinformatics.

Latent Force Models

Neil D. Lawrence, David Luengo, Mauricio A. Álvarez, 2009, AISTATS.

Differentially Private Gaussian Processes pdf

Neil D. Lawrence, Max Zwiessele, Michael Thomas Smith, 2016, ArXiv.

An integrated probabilistic framework for robot perception, learning and memory

Neil D. Lawrence, Uriel Martinez-Hernandez, Tony J. Prescott, 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

Modelling transcriptional regulation using Gaussian Processes

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, NIPS.

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

Neil D. Lawrence, Carl Henrik Ek, Andreas C. Damianou, 2016, J. Mach. Learn. Res..

Gaussian Processes and the Null-Category Noise Model

Neil D. Lawrence, Michael I. Jordan, Neil D. Lawrence, 2006, Semi-Supervised Learning.

Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning

Neil D. Lawrence, Mahesan Niranjan, Joab R. Winkler, 2004 .

Fast Nonparametric Clustering of Structured Time-Series

Neil D. Lawrence, Magnus Rattray, James Hensman, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Computationally Efficient Convolved Multiple Output Gaussian Processes

Neil D. Lawrence, Mauricio A. Álvarez, Neil D. Lawrence, 2011, J. Mach. Learn. Res..

Deep Gaussian Processes pdf

Neil D. Lawrence, Andreas C. Damianou, Neil D. Lawrence, 2012, AISTATS.

A Brief Introduction to Bayesian Inference

Neil D. Lawrence, Magnus Rattray, Neil D. Lawrence, 2010, Learning and Inference in Computational Systems Biology.

Efficient Sampling for Gaussian Process Inference using Control Variables

Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias, 2008, NIPS.

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

Zhichao Li, Neil D. Lawrence, Zhenwen Dai, 2019, Appl. Soft Comput..

Gaussian Process Latent Variable Models for Human Pose Estimation

Neil D. Lawrence, Philip H. S. Torr, Carl Henrik Ek, 2007, MLMI.

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

Neil D. Lawrence, Bei Wang, Xf Yan, 2020, IEEE Transactions on Industrial Informatics.

Extensions of the Informative Vector Machine

Neil D. Lawrence, Michael I. Jordan, John C. Platt, 2004, Deterministic and Statistical Methods in Machine Learning.

Detecting periodicities with Gaussian processes

Neil D. Lawrence, Magnus Rattray, James Hensman, 2016, PeerJ Prepr..

Dataset Shift in Machine Learning

Neil D. Lawrence, Masashi Sugiyama, Anton Schwaighofer, 2009 .

Hybrid Discriminative-Generative Approach with Gaussian Processes

Neil D. Lawrence, James Hensman, Max Zwiessele, 2014, AISTATS.

Fast Sparse Gaussian Process Methods: The Informative Vector Machine

Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich, 2002, NIPS.

Prior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian Processes

Neil D. Lawrence, David Luengo, Michalis K. Titsias, 2009 .

The Structure of Neural Network Posteriors

Neil D. Lawrence, Mehdi Azzouzi, N. Lawrence, 2001 .

Topslam: Waddington Landscape Recovery for Single Cell Experiments

Neil D. Lawrence, Max Zwiessele, Neil D. Lawrence, 2016, bioRxiv.

Kernels for Vector-Valued Functions: a Review

Neil D. Lawrence, Lorenzo Rosasco, Mauricio A. Álvarez, 2011, Found. Trends Mach. Learn..

Overlapping Mixtures of Gaussian Processes for the Data Association Problem

Neil D. Lawrence, Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, 2011, Pattern Recognit..

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

Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti, 2006, Bioinform..

Preferential Bayesian Optimization

Neil D. Lawrence, Zhenwen Dai, Javier González, 2017, ICML.

Introduction to Gaussian Processes

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2013 .

G aussian Processes for Big Data with Stochastic Variational Inference

Neil D. Lawrence, James Hensman, Neil D. Lawrence, 2012 .

Missing Data in Kernel PCA

Neil D. Lawrence, Guido Sanguinetti, Neil D. Lawrence, 2006, ECML.

Variational Gaussian Process Dynamical Systems

Neil D. Lawrence, Michalis K. Titsias, Andreas C. Damianou, 2011, NIPS.

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

Neil D. Lawrence, John A. Quinn, Martin Gordon Mubangizi, 2015, AALTD@PKDD/ECML.

Linear Latent Force Models Using Gaussian Processes

Neil D. Lawrence, David Luengo, Mauricio A. Álvarez, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep recurrent Gaussian processes for outlier-robust system identification

Neil D. Lawrence, Guilherme A. Barreto, Andreas Damianou, 2017 .

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

Neil D. Lawrence, Boaz Lerner, B. Lerner, 2001, Neural Computing & Applications.

Gaussian Process Models with Parallelization and GPU acceleration pdf

Neil D. Lawrence, James Hensman, Zhenwen Dai, 2014, ArXiv.

Towards a Data Science Collaboratory

Bernd Bischl, Neil D. Lawrence, Balázs Kégl, 2015 .

Gaussian Processes in Practice

Neil D. Lawrence, Anton Schwaighofer, Joaquin Quinonero Candela, 2007 .

MOCAP Toolbox for MATLAB

Neil D. Lawrence, Neil D. Lawrence, N. Lawrence, 2005 .

The 3Ds of Machine Learning Systems Design

Neil D. Lawrence, N. Lawrence, 2018 .

Variational Information Distillation for Knowledge Transfer

Neil D. Lawrence, Zhenwen Dai, Sungsoo Ahn, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

puma: a Bioconductor package for propagating uncertainty in microarray analysis

Neil D. Lawrence, Magnus Rattray, Xuejun Liu, 2009, BMC Bioinformatics.

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

Neil D. Lawrence, Oliver Stegle, Nicoló Fusi, 2012, PLoS Comput. Biol..

Spike and Slab Gaussian Process Latent Variable Models pdf

Neil D. Lawrence, James Hensman, Zhenwen Dai, 2015, ArXiv.

Learning for Larger Datasets with the Gaussian Process Latent Variable Model

Neil D. Lawrence, Neil D. Lawrence, 2007, AISTATS.

Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis

Neil D. Lawrence, Michael E. Tipping, Neil D. Lawrence, 2005, Neurocomputing.

Residual Component Analysis pdf

Neil D. Lawrence, Alfredo A. Kalaitzis, Neil D. Lawrence, 2011, ICML 2011.

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

Neil D. Lawrence, Joris M. Mooij, Oliver Stegle, 2011, NIPS.

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

Neil D. Lawrence, Magnus Rattray, James Hensman, 2014, ArXiv.

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Bernhard Schölkopf, Neil D. Lawrence, Neil D. Lawrence, 2001, ICML.

Chained Gaussian Processes pdf

Aki Vehtari, Neil D. Lawrence, James Hensman, 2016, AISTATS.

Hierarchical Gaussian process latent variable models

Neil D. Lawrence, Andrew J. Moore, Neil D. Lawrence, 2007, ICML '07.

Ambiguity Modeling in Latent Spaces

Neil D. Lawrence, Philip H. S. Torr, Grégory Rogez, 2008, MLMI.

Living Together: Mind and Machine Intelligence pdf

Neil D. Lawrence, Neil D. Lawrence, 2017, ArXiv.

Advances in Neural Information Processing Systems Workshop on Learning from Multiple Sources

Neil D. Lawrence, Carl Henrik Ek, Philip Hs Torr, 2008 .

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

Neil D. Lawrence, Simo Särkkä, Mauricio A. Álvarez, 2017, IEEE Transactions on Automatic Control.

Fast Variational Inference for Gaussian Process Models Through KL-Correction

Neil D. Lawrence, Nathaniel John King, Neil D. Lawrence, 2006, ECML.

Batch Bayesian Optimization via Local Penalization

Neil D. Lawrence, Zhenwen Dai, Javier González, 2015, AISTATS.

The Emergence of Organizing Structure in Conceptual Representation

Neil D. Lawrence, Joshua B. Tenenbaum, Brenden M. Lake, 2018, Cognitive science.

Fast Variational Inference in the Conjugate Exponential Family

Neil D. Lawrence, Magnus Rattray, James Hensman, 2012, NIPS.

Discriminative Learning under Covariate Shift with a Single Optimization Problem

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Geometry of Covariate Shift with Applications to Active Learning

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

International Conference on Learning Representations Workshop track

Neil D. Lawrence, Zhenwen Dai, Carl Henrik Ek, 2016 .

Invited Talk Abstracts

Neil D. Lawrence, Yi Ma, Lawrence Carin, 2010, AAAI Fall Symposium: Manifold Learning and Its Applications.

Spectral Dimensionality Reduction via Maximum Entropy

Neil D. Lawrence, Neil D. Lawrence, 2011, AISTATS.

Differentially Private Regression and Classification with Sparse Gaussian Processes pdf

Neil D. Lawrence, Mauricio A. Alvarez, Michael Thomas Smith, 2019, ArXiv.

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes

Neil D. Lawrence, Michalis K. Titsias, Andreas C. Damianou, 2016, J. Mach. Learn. Res..

Warped linear mixed models for the genetic analysis of transformed phenotypes

Neil D. Lawrence, Christoph Lippert, Nicolo Fusi, 2014, Nature Communications.