Approximating Posterior Distributions in Belief Networks Using Mixtures. - Christopher M. Bishop; Neil D. Lawrence; Tommi S. Jaakkola; Michael I. Jordan (NIPS, 1997)

Mixture Representations for Inference and Learning in Boltzmann Machines. - Neil D. Lawrence; Christopher M. Bishop; Michael I. Jordan (UAI, 1998)

Variational Learning for Multi-Layer Networks of Linear Threshold Units. - Neil D. Lawrence (AISTATS, 2001)

Probabilistic Modelling of Replica Divergence. - Antony I. T. Rowstron; Neil D. Lawrence; Christopher M. Bishop (HotOS, 2001)

Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. - Neil D. Lawrence; Bernhard Schölkopf (ICML, 2001)

Optimising Synchronisation Times for Mobile Devices. - Neil D. Lawrence; Antony I. T. Rowstron; Christopher M. Bishop; M. J. Taylor (NIPS, 2001)

Note Relevance Determination. - Neil D. Lawrence (WIRN, 2001)

A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics. - Boaz Lerner; Neil D. Lawrence (Neural Comput. Appl., 2001)

Fast Sparse Gaussian Process Methods: The Informative Vector Machine. - Neil D. Lawrence; Matthias W. Seeger; Ralf Herbrich (NIPS, 2002)

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. - Matthias W. Seeger; Christopher K. I. Williams; Neil D. Lawrence (AISTATS, 2003)

Variational Inference for Visual Tracking. - Jaco Vermaak; Neil D. Lawrence; Patrick Pérez (CVPR (1), 2003)

Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. - Neil D. Lawrence (NIPS, 2003)

Bayesian processing of microarray images. - Neil D. Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier (NNSP, 2003)

A variational approach to robust Bayesian interpolation. - Michael E. Tipping; Neil D. Lawrence (NNSP, 2003)

Extensions of the Informative Vector Machine. - Neil D. Lawrence; John C. Platt; Michael I. Jordan (Deterministic and Statistical Methods in Machine Learning, 2004)

Acoustic space dimensionality selection and combination using the maximum entropy principle. - Yasser H. Abdel-Haleem; Steve Renals; Neil D. Lawrence (ICASSP (5), 2004)

Learning to learn with the informative vector machine. - Neil D. Lawrence; John C. Platt (ICML, 2004)

Semi-supervised Learning via Gaussian Processes. - Neil D. Lawrence; Michael I. Jordan (NIPS, 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)

A hybrid Maxent/HMM based ASR system. - Yasser Hifny; Steve Renals; Neil D. Lawrence (INTERSPEECH, 2005)

A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. - Xuejun Liu; Marta Milo; Neil D. Lawrence; Magnus Rattray (Bioinform., 2005)

Accounting for probe-level noise in principal component analysis of microarray data. - Guido Sanguinetti; Marta Milo; Magnus Rattray; Neil D. Lawrence (Bioinform., 2005)

Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. - Michael E. Tipping; Neil D. Lawrence (Neurocomputing, 2005)

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. - Neil D. Lawrence (J. Mach. Learn. Res., 2005)

Gaussian Processes and the Null-Category Noise Model. - Neil D. Lawrence; Michael I. Jordan (Semi-Supervised Learning, 2006)

Identifying Submodules of Cellular Regulatory Networks. - Guido Sanguinetti; Magnus Rattray; Neil D. Lawrence (CMSB, 2006)

Fast Variational Inference for Gaussian Process Models Through KL-Correction. - Nathaniel John King; Neil D. Lawrence (ECML, 2006)

Missing Data in Kernel PCA. - Guido Sanguinetti; Neil D. Lawrence (ECML, 2006)

Local distance preservation in the GP-LVM through back constraints. - Neil D. Lawrence; Joaquin Quiñonero Candela (ICML, 2006)

Modelling transcriptional regulation using Gaussian Processes. - Neil D. Lawrence; Guido Sanguinetti; Magnus Rattray (NIPS, 2006)

Propagating uncertainty in microarray data analysis. - Magnus Rattray; Xuejun Liu; Guido Sanguinetti; Marta Milo; Neil D. Lawrence (Briefings Bioinform., 2006)

Probe-level measurement error improves accuracy in detecting differential gene expression. - Xuejun Liu; Marta Milo; Neil D. Lawrence; Magnus Rattray (Bioinform., 2006)

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. - Guido Sanguinetti; Neil D. Lawrence; Magnus Rattray (Bioinform., 2006)

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. - Guido Sanguinetti; Magnus Rattray; Neil D. Lawrence (Bioinform., 2006)

Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. - Tonatiuh Peña Centeno; Neil D. Lawrence (J. Mach. Learn. Res., 2006)

Gaussian Process Latent Variable Models for Fault Detection. - Luka Eciolaza; Muhammad Alkarouri; Neil D. Lawrence; Visakan Kadirkamanathan; Peter J. Fleming (CIDM, 2007)

Modeling Human Locomotion with Topologically Constrained Latent Variable Models. - Raquel Urtasun; David J. Fleet; Neil D. Lawrence (Workshop on Human Motion, 2007)

Hierarchical Gaussian process latent variable models. - Neil D. Lawrence; Andrew J. Moore (ICML, 2007)

WiFi-SLAM Using Gaussian Process Latent Variable Models. - Brian Ferris; Dieter Fox; Neil D. Lawrence (IJCAI, 2007)

Model-driven detection of clean speech patches in noise. - Jonathan Laidler; Martin Cooke; Neil D. Lawrence (INTERSPEECH, 2007)

Gaussian Process Latent Variable Models for Human Pose Estimation. - Carl Henrik Ek; Philip H. S. Torr; Neil D. Lawrence (MLMI, 2007)

Learning for Larger Datasets with the Gaussian Process Latent Variable Model. - Neil D. Lawrence (AISTATS, 2007)

Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. - Pei Gao; Antti Honkela; Magnus Rattray; Neil D. Lawrence (ECCB, 2008)

Topologically-constrained latent variable models. - Raquel Urtasun; David J. Fleet; Andreas Geiger 001; Jovan Popovic; Trevor Darrell; Neil D. Lawrence (ICML, 2008)

Ambiguity Modeling in Latent Spaces. - Carl Henrik Ek; Jonathan Rihan; Philip H. S. Torr; Grégory Rogez; Neil D. Lawrence (MLMI, 2008)

Sparse Convolved Gaussian Processes for Multi-output Regression. - Mauricio A. Álvarez; Neil D. Lawrence (NIPS, 2008)

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. - Ben Calderhead; Mark A. Girolami; Neil D. Lawrence (NIPS, 2008)

Efficient Sampling for Gaussian Process Inference using Control Variables. - Michalis K. Titsias; Neil D. Lawrence; Magnus Rattray (NIPS, 2008)

Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery. - John Darby; Baihua Li; Nicholas Costen; David J. Fleet; Neil D. Lawrence (BMVC, 2009)

Non-linear matrix factorization with Gaussian processes. - Neil D. Lawrence; Raquel Urtasun (ICML, 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)

Latent Force Models. - Mauricio A. Álvarez; David Luengo; Neil D. Lawrence (AISTATS, 2009)

A Brief Introduction to Bayesian Inference. - Neil D. Lawrence; Magnus Rattray (Learning and Inference in Computational Systems Biology, 2010)

Gaussian Processes for Missing Species in Biochemical Systems. - Neil D. Lawrence; Magnus Rattray; Pei Gao; Michalis K. Titsias (Learning and Inference in Computational Systems Biology, 2010)

Invited Talk Abstracts. - Yi Ma; Fei Sha; Lawrence Carin; Gilad Lerman; Neil D. Lawrence (AAAI Fall Symposium: Manifold Learning and Its Applications, 2010)

Switched Latent Force Models for Movement Segmentation. - Mauricio A. Álvarez; Jan Peters 001; Bernhard Schölkopf; Neil D. Lawrence (NIPS, 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)

A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction - Neil D. Lawrence (CoRR, 2010)

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. - Mauricio A. Álvarez; David Luengo; Michalis K. Titsias; Neil D. Lawrence (AISTATS, 2010)

Bayesian Gaussian Process Latent Variable Model. - Michalis K. Titsias; Neil D. Lawrence (AISTATS, 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)

Variational Gaussian Process Dynamical Systems. - Andreas C. Damianou; Michalis K. Titsias; Neil D. Lawrence (NIPS, 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 (NIPS, 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)

A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression. - Alfredo A. Kalaitzis; Neil D. Lawrence (BMC Bioinform., 2011)

Residual Component Analysis - Alfredo A. Kalaitzis; Neil D. Lawrence (CoRR, 2011)

Computationally Efficient Convolved Multiple Output Gaussian Processes. - Mauricio A. Álvarez; Neil D. Lawrence (J. Mach. Learn. Res., 2011)

Spectral Dimensionality Reduction via Maximum Entropy. - Neil D. Lawrence (AISTATS, 2011)

Manifold Relevance Determination. - Andreas C. Damianou; Carl Henrik Ek; Michalis K. Titsias; Neil D. Lawrence (ICML, 2012)

Residual Components Analysis. - Alfredo A. Kalaitzis; Neil D. Lawrence (ICML, 2012)

Fast Variational Inference in the Conjugate Exponential Family. - James Hensman; Magnus Rattray; Neil D. Lawrence (NIPS, 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)

Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models - Alfredo A. Kalaitzis; Neil D. Lawrence (CoRR, 2012)

Kernels for Vector-Valued Functions: A Review. - Mauricio A. Álvarez; Lorenzo Rosasco; Neil D. Lawrence (Found. Trends Mach. Learn., 2012)

A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models. - Neil D. Lawrence (J. Mach. Learn. Res., 2012)

Preface. - Neil D. Lawrence; Mark A. Girolami (AISTATS, 2012)

Editor's Note. - Ramin Zabih; Sing Bing Kang; Neil D. Lawrence; Jiri Matas; Max Welling (IEEE Trans. Pattern Anal. Mach. Intell., 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)

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)

Overlapping Mixtures of Gaussian Processes for the data association problem. - Miguel Lázaro-Gredilla; Steven Van Vaerenbergh; Neil D. Lawrence (Pattern Recognit., 2012)

Deep Gaussian Processes. - Andreas C. Damianou; Neil D. Lawrence (AISTATS, 2013)

The Bigraphical Lasso. - Alfredo A. Kalaitzis; John D. Lafferty; Neil D. Lawrence; Shuheng Zhou 002 (ICML (3), 2013)

Gaussian Processes for Big Data. - James Hensman; Nicoló Fusi; Neil D. Lawrence (UAI, 2013)

Detecting regulatory gene-environment interactions with unmeasured environmental factors. - Nicoló Fusi; Christoph Lippert; Karsten M. Borgwardt; Neil D. Lawrence; Oliver Stegle (Bioinform., 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)

Preface: Intelligent interactive data visualization. - Barbara Hammer; Daniel A. Keim; Neil D. Lawrence; Guy Lebanon (Data Min. Knowl. Discov., 2013)

Linear Latent Force Models Using Gaussian Processes. - Mauricio A. Álvarez; David Luengo; Neil D. Lawrence (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Gaussian Processes for Natural Language Processing. - Trevor Cohn; Daniel Preotiuc-Pietro; Neil D. Lawrence (ACL (Tutorial Abstracts), 2014)

Tilted Variational Bayes. - James Hensman; Max Zwiessele; Neil D. Lawrence (AISTATS, 2014)

Hybrid Discriminative-Generative Approach with Gaussian Processes. - Ricardo Andrade Pacheco; James Hensman; Max Zwiessele; Neil D. Lawrence (AISTATS, 2014)

Metrics for Probabilistic Geometries. - Alessandra Tosi; Søren Hauberg; Alfredo Vellido; Neil D. Lawrence (UAI, 2014)

Gaussian Process Models with Parallelization and GPU acceleration. - Zhenwen Dai; Andreas C. Damianou; James Hensman; 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)

Fast variational inference for nonparametric clustering of structured time-series. - James Hensman; Magnus Rattray; Neil D. Lawrence (CoRR, 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)

A Top-Down Approach for a Synthetic Autobiographical Memory System. - Andreas C. Damianou; Carl Henrik Ek; Luke Boorman; Neil D. Lawrence; Tony J. Prescott (Living Machines, 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 (AALTD@PKDD/ECML, 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 (AALTD@PKDD/ECML (Revised Selected Papers), 2015)

Semi-described and semi-supervised learning with Gaussian processes. - Andreas C. Damianou; Neil D. Lawrence (UAI, 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)

Spike and Slab Gaussian Process Latent Variable Models. - Zhenwen Dai; James Hensman; Neil D. Lawrence (CoRR, 2015)

Fast Nonparametric Clustering of Structured Time-Series. - James Hensman; Magnus Rattray; Neil D. Lawrence (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Batch Bayesian Optimization via Local Penalization. - Javier González 002; Zhenwen Dai; Philipp Hennig; Neil D. Lawrence (AISTATS, 2016)

GLASSES: Relieving The Myopia Of Bayesian Optimisation. - Javier González 002; Michael A. Osborne; Neil D. Lawrence (AISTATS, 2016)

Chained Gaussian Processes. - Alan D. Saul; James Hensman; Aki Vehtari; Neil D. Lawrence (AISTATS, 2016)

A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity. - Muhammad Arifur Rahman; Neil D. Lawrence (BCB, 2016)

iCub Visual Memory Inspector: Visualising the iCub's Thoughts. - Daniel Camilleri; Andreas C. Damianou; Harry Jackson; Neil D. Lawrence; Tony J. Prescott (Living Machines, 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 (ROBIO, 2016)

Variational Auto-encoded Deep Gaussian Processes. - Zhenwen Dai; Andreas C. Damianou; Javier González 002; Neil D. Lawrence (ICLR (Poster), 2016)

The Emergence of Organizing Structure in Conceptual Representation. - Brenden M. Lake; Neil D. Lawrence; Joshua B. Tenenbaum (CoRR, 2016)

Recurrent Gaussian Processes. - César Lincoln C. Mattos; Zhenwen Dai; Andreas C. Damianou; Jeremy Forth; Guilherme A. Barreto; Neil D. Lawrence (ICLR (Poster), 2016)

Differentially Private Gaussian Processes. - Michael T. Smith 003; Max Zwiessele; Neil D. Lawrence (CoRR, 2016)

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. - Fariba Yousefi; Zhenwen Dai; Carl Henrik Ek; Neil D. Lawrence (CoRR, 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)

Detecting periodicities with Gaussian processes. - Nicolas Durrande; James Hensman; Magnus Rattray; Neil D. Lawrence (PeerJ Prepr., 2016)

Preferential Bayesian Optimization. - Javier González 002; Zhenwen Dai; Andreas C. Damianou; Neil D. Lawrence (ICML, 2017)

Parallelizable sparse inverse formulation Gaussian processes (SpInGP). - Alexander Grigorievskiy; Neil D. Lawrence; Simo Särkkä (MLSP, 2017)

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. - Zhenwen Dai; Mauricio A. Álvarez; Neil D. Lawrence (NIPS, 2017)

Efficient inference for sparse latent variable models of transcriptional regulation. - Zhenwen Dai; Mudassar Iqbal; Neil D. Lawrence; Magnus Rattray (Bioinform., 2017)

Manifold Alignment Determination: finding correspondences across different data views. - Andreas C. Damianou; Neil D. Lawrence; Carl Henrik Ek (CoRR, 2017)

Data Readiness Levels. - Neil D. Lawrence (CoRR, 2017)

Living Together: Mind and Machine Intelligence. - Neil D. Lawrence (CoRR, 2017)

Auto-Differentiating Linear Algebra. - Matthias W. Seeger; Asmus Hetzel; Zhenwen Dai; Neil D. Lawrence (CoRR, 2017)

Differentially Private Regression with Gaussian Processes. - Michael T. Smith 003; Mauricio A. Álvarez; Max Zwiessele; Neil D. Lawrence (AISTATS, 2018)

Structured Variationally Auto-encoded Optimization. - Xiaoyu Lu; Javier González 002; Zhenwen Dai; Neil D. Lawrence (ICML, 2018)

Intrinsic Gaussian processes on complex constrained domains. - Mu Niu; Pokman Cheung; Lizhen Lin; Zhenwen Dai; Neil D. Lawrence; David B. Dunson (CoRR, 2018)

Gaussian Process Regression for Binned Data. - Michael Thomas Smith; Mauricio A. Álvarez; Neil D. Lawrence (CoRR, 2018)

Variational Information Distillation for Knowledge Transfer. - Sungsoo Ahn; Shell Xu Hu; Andreas C. Damianou; Neil D. Lawrence; Zhenwen Dai (CVPR, 2019)

Transferring Knowledge across Learning Processes. - Sebastian Flennerhag; Pablo Garcia Moreno; Neil D. Lawrence; Andreas C. Damianou (ICLR, 2019)

Meta-Surrogate Benchmarking for Hyperparameter Optimization. - Aaron Klein; Zhenwen Dai; Frank Hutter; Neil D. Lawrence; Javier González 002 (NeurIPS, 2019)

Usability of Probabilistic Programming Languages. - Alan F. Blackwell; Luke Church; Martin Erwig; James Geddes; Andy Gordon 001; Maria I. Gorinova 001; Atilim Gunes Baydin; Bradley Gram-Hansen; Tobias Kohn; Neil D. Lawrence; Vikash Mansinghka 001; Brooks Paige; et al. (PPIG, 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)

Deep Gaussian Processes for Multi-fidelity Modeling. - Kurt Cutajar; Mark Pullin; Andreas C. Damianou; Neil D. Lawrence; Javier González 002 (CoRR, 2019)

Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design. - Neil D. Lawrence (CoRR, 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)

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. - Shell Xu Hu; Pablo Garcia Moreno; Yang Xiao 009; Xi Shen 001; Guillaume Obozinski; Neil D. Lawrence; Andreas C. Damianou (ICLR, 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)

Benchmarking Real-Time Reinforcement Learning. - Pierre Thodoroff; Wenyu Li; Neil D. Lawrence (Pre-Registration Workshop @ NeurIPS, 2021)

Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. - Andrei Paleyes; Christian Cabrera 001; Neil D. Lawrence (CoRR, 2021)

Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment. - Corinna Cortes; Neil D. Lawrence (CoRR, 2021)

Behavioral Experiments for Understanding Catastrophic Forgetting. - Samuel J. Bell; Neil D. Lawrence (CoRR, 2021)

Emulation of physical processes with Emukit. - Andrei Paleyes; Mark Pullin; Maren Mahsereci; Cliff McCollum; Neil D. Lawrence; Javier González 002 (CoRR, 2021)

Solving Schrödinger Bridges via Maximum Likelihood. - Francisco Vargas 001; Pierre Thodoroff; Austen Lamacraft; Neil D. Lawrence (Entropy, 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)

Differentially Private Regression and Classification with Sparse Gaussian Processes. - Michael Thomas Smith; Mauricio A. Álvarez; Neil D. Lawrence (J. Mach. Learn. Res., 2021)

Generalised GPLVM with Stochastic Variational Inference. - Vidhi Lalchand; Aditya Ravuri; Neil D. Lawrence (AISTATS, 2022)

Two-way Sparse Network Inference for Count Data. - Sijia Li; Martín López-García; Neil D. Lawrence; Luisa Cutillo (AISTATS, 2022)

An empirical evaluation of flow based programming in the machine learning deployment context. - Andrei Paleyes; Christian Cabrera 001; Neil D. Lawrence (CAIN, 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 (MLCB, 2022)

Modeling the Machine Learning Multiverse. - Samuel J. Bell; Onno Kampman; Jesse Dodge; Neil D. Lawrence (NeurIPS, 2022)

Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. - Vidhi Lalchand; Aditya Ravuri; 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)

The Effect of Task Ordering in Continual Learning. - Samuel J. Bell; Neil D. Lawrence (CoRR, 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 001 (CoRR, 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, 2022)

Dataflow graphs as complete causal graphs. - Andrei Paleyes; Siyuan Guo; Bernhard Schölkopf; Neil D. Lawrence (CAIN, 2023)

Causal fault localisation in dataflow systems. - Andrei Paleyes; Neil David Lawrence (EuroMLSys@EuroSys, 2023)

Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models. - Bogdan Ficiu; Neil D. Lawrence; Andrei Paleyes (PKDD/ECML Workshops (1), 2023)

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. - Christian Cabrera 001; Andrei Paleyes; Pierre Thodoroff; 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)

Dimensionality Reduction as Probabilistic Inference. - Aditya Ravuri; Francisco Vargas 001; Vidhi Lalchand; Neil D. Lawrence (CoRR, 2023)

Challenges in Deploying Machine Learning: A Survey of Case Studies. - Andrei Paleyes; Raoul-Gabriel Urma; Neil D. Lawrence (ACM Comput. Surv., 2023)

Bayesian learning via neural Schrödinger-Föllmer flows. - Francisco Vargas 001; Andrius Ovsianas; David Fernandes; Mark Girolami; Neil D. Lawrence; Nikolas Nüsken (Stat. Comput., 2023)

Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. - Francisco Vargas 001; Pierre Thodoroff; Austen Lamacraft; Neil D. Lawrence (Entropy, 2023)

Can causality accelerate experimentation in software systems? - Andrei Paleyes; Han-Bo Li; Neil D. Lawrence (CAIN, 2024)

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. - Christian Cabrera 001; Andrei Paleyes; Neil D. Lawrence (SATrends, 2024)

Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. - Sarah Zhao; Aditya Ravuri; Vidhi Lalchand; 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)

On Feature Learning for Titi Monkey Activity Detection. - Aditya Ravuri; Jen Muir; Neil D. Lawrence (CoRR, 2024)

The Systems Engineering Approach in Times of Large Language Models. - Christian Cabrera 001; Viviana Bastidas; Jennifer Schooling; Neil D. Lawrence (CoRR, 2024)

The Human Visual System Can Inspire New Interaction Paradigms for LLMs. - Diana Robinson; Neil D. Lawrence (CoRR, 2025)

LLM Performance for Code Generation on Noisy Tasks. - Radzim Sendyka; Christian Cabrera 001; Andrei Paleyes; Diana Robinson; Neil D. Lawrence (CoRR, 2025)

Prompt Variability Effects On LLM Code Generation. - Andrei Paleyes; Radzim Sendyka; Diana Robinson; Christian Cabrera 001; Neil D. Lawrence (CoRR, 2025)

Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout. - Aditya Ravuri; Neil D. Lawrence (CoRR, 2025)

Requirements Are All You Need: The Final Frontier for End-User Software Engineering. - Diana Robinson; Christian Cabrera 001; Andrew D. Gordon 001; Neil D. Lawrence; Lars Mennen (ACM Trans. Softw. Eng. Methodol., 2025)

Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements. - Aditya Ravuri; Neil D. Lawrence (CoRR, 2025)