Neil D. Lawrence

223 papers A* 17A 26B 3C 2Journal 123Unranked 44

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Machine Learning Systems: A Survey from a Data-Oriented Perspective. ACM Comput. Surv. Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence
2026	J	`jnl`	On the Value of Tokeniser Pretraining in Physics Foundation Models. CoRR Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana, Michael McCabe, Neil D. Lawrence, Shirley Ho, Miles D. Cranmer
2026	J	`jnl`	Optimising for Energy Efficiency and Performance in Machine Learning. CoRR Emile Dos Santos Ferreira, Andrei Paleyes, Neil D. Lawrence
2026	J	`jnl`	The Origin of the Inaccessible Game. CoRR Neil D. Lawrence
2025	J	`jnl`	Beyond Quantification: Navigating Uncertainty in Professional AI Systems. CoRR Sylvie Delacroix, Diana Robinson, Umang Bhatt, Jacopo Domenicucci, Jessica Montgomery, Gaël Varoquaux, Carl Henrik Ek, Vincent Fortuin, Yulan He, Tom Diethe, Neill W. Campbell, Mennatallah El-Assady, Søren Hauberg, Ivana Dusparic, Neil D. Lawrence
2025	J	`jnl`	LLM Performance for Code Generation on Noisy Tasks. CoRR Radzim Sendyka, Christian Cabrera, Andrei Paleyes, Diana Robinson, Neil D. Lawrence
2025	J	`jnl`	Prompt Variability Effects On LLM Code Generation. CoRR Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence
2025	J	`jnl`	Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout. CoRR Aditya Ravuri, Neil D. Lawrence
2025	J	`jnl`	Requirements Are All You Need: The Final Frontier for End-User Software Engineering. ACM Trans. Softw. Eng. Methodol. Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen
2025	J	`jnl`	Revisiting Deep AC-OPF. CoRR Oluwatomisin I. Dada, Neil D. Lawrence
2025	J	`jnl`	The Human Visual System Can Inspire New Interaction Paradigms for LLMs. CoRR Diana Robinson, Neil D. Lawrence
2025	J	`jnl`	The Inaccessible Game. CoRR Neil D. Lawrence
2025	—	`conf`	The Systems Engineering Approach in Times of Large Language Models. HICSS Christian Cabrera, Viviana Bastidas, Jennifer Schooling, Neil D. Lawrence
2025	J	`jnl`	Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements. CoRR Aditya Ravuri, Neil D. Lawrence
2024	B	`conf`	Can causality accelerate experimentation in software systems? CAIN Andrei Paleyes, Han-Bo Li, Neil D. Lawrence
2024	J	`jnl`	On Feature Learning for Titi Monkey Activity Detection. CoRR Aditya Ravuri, Jen Muir, Neil D. Lawrence
2024	J	`jnl`	Requirements are All You Need: The Final Frontier for End-User Software Engineering. CoRR Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen
2024	J	`jnl`	Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
2024	—	`conf`	Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
2024	J	`jnl`	Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
2024	J	`jnl`	The Systems Engineering Approach in Times of Large Language Models. CoRR Christian Cabrera, Viviana Bastidas, Jennifer Schooling, Neil D. Lawrence
2024	J	`jnl`	Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. CoRR Aditya Ravuri, Neil D. Lawrence
2023	J	`jnl`	AI for Science: An Emerging Agenda. CoRR Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2023	—	`conf`	Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models. PKDD/ECML Workshops (1) Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
2023	J	`jnl`	Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
2023	J	`jnl`	Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
2023	J	`jnl`	Causal fault localisation in dataflow systems. CoRR Andrei Paleyes, Neil D. Lawrence
2023	J	`jnl`	Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
2023	J	`jnl`	Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence
2023	B	`conf`	Dataflow graphs as complete causal graphs. CAIN Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
2023	J	`jnl`	Dataflow graphs as complete causal graphs. CoRR Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
2023	J	`jnl`	Dimensionality Reduction as Probabilistic Inference. CoRR Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
2023	—	`conf`	Emukit: A Python toolkit for decision making under uncertainty. SciPy Andrei Paleyes, Maren Mahsereci, Neil D. Lawrence
2023	J	`jnl`	Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence
2022	J	`jnl`	An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2022	B	`conf`	An empirical evaluation of flow based programming in the machine learning deployment context. CAIN Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2022	A	`conf`	Generalised GPLVM with Stochastic Variational Inference. AISTATS Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Ice Core Dating using Probabilistic Programming. CoRR Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
2022	J	`jnl`	Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2022	A*	`conf`	Modeling the Machine Learning Multiverse. NeurIPS Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence
2022	J	`jnl`	Modeling the Machine Learning Multiverse. CoRR Samuel J. Bell, Onno Pepijn Kampman, Jesse Dodge, Neil D. Lawrence
2022	—	`conf`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
2022	J	`jnl`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
2022	J	`jnl`	Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data. CoRR Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo
2022	J	`jnl`	The Effect of Task Ordering in Continual Learning. CoRR Samuel J. Bell, Neil D. Lawrence
2022	A	`conf`	Two-way Sparse Network Inference for Count Data. AISTATS Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo
2021	J	`jnl`	Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
2021	J	`jnl`	Behavioral Experiments for Understanding Catastrophic Forgetting. CoRR Samuel J. Bell, Neil D. Lawrence
2021	—	`conf`	Benchmarking Real-Time Reinforcement Learning. Pre-Registration Workshop @ NeurIPS Pierre Thodoroff, Wenyu Li, Neil D. Lawrence
2021	J	`jnl`	Differentially Private Regression and Classification with Sparse Gaussian Processes. J. Mach. Learn. Res. Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
2021	J	`jnl`	Emulation of physical processes with Emukit. CoRR Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González
2021	J	`jnl`	Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. CoRR Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
2021	J	`jnl`	Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment. CoRR Corinna Cortes, Neil D. Lawrence
2021	J	`jnl`	Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. J. Mach. Learn. Res. Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
2021	J	`jnl`	Solving Schrödinger Bridges via Maximum Likelihood. CoRR Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft
2021	J	`jnl`	Solving Schrödinger Bridges via Maximum Likelihood. Entropy Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence
2020	J	`jnl`	Challenges in Deploying Machine Learning: a Survey of Case Studies. CoRR Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
2020	J	`jnl`	Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes. IEEE Trans. Ind. Informatics Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan
2020	A*	`conf`	Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. ICLR Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou
2020	J	`jnl`	Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. CoRR Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou
2019	J	`jnl`	A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant. Appl. Soft Comput. Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan
2019	J	`jnl`	Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design. CoRR Neil D. Lawrence
2019	J	`jnl`	Deep Gaussian Processes for Multi-fidelity Modeling. CoRR Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González
2019	J	`jnl`	Differentially Private Regression and Classification with Sparse Gaussian Processes. CoRR Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
2019	J	`jnl`	Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Trans. Autom. Control. Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
2019	A*	`conf`	Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González
2019	J	`jnl`	Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González
2019	—	`conf`	Transferring Knowledge across Learning Processes. ICLR Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou
2019	—	`conf`	Usability of Probabilistic Programming Languages. PPIG 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
2019	A*	`conf`	Variational Information Distillation for Knowledge Transfer. CVPR Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai
2019	J	`jnl`	Variational Information Distillation for Knowledge Transfer. CoRR Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai
2018	A	`conf`	Differentially Private Regression with Gaussian Processes. AISTATS Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence
2018	J	`jnl`	Gaussian Process Regression for Binned Data. CoRR Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
2018	J	`jnl`	Intrinsic Gaussian processes on complex constrained domains. CoRR Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson
2018	A*	`conf`	Structured Variationally Auto-encoded Optimization. ICML Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence
2018	J	`jnl`	Transferring Knowledge across Learning Processes. CoRR Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou
2017	J	`jnl`	Auto-Differentiating Linear Algebra. CoRR Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence
2017	J	`jnl`	Data Readiness Levels. CoRR Neil D. Lawrence
2017	—	`conf`	Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
2017	J	`jnl`	Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. CoRR Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
2017	J	`jnl`	Efficient inference for sparse latent variable models of transcriptional regulation. Bioinform. Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray
2017	J	`jnl`	Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. CoRR Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
2017	J	`jnl`	Living Together: Mind and Machine Intelligence. CoRR Neil D. Lawrence
2017	J	`jnl`	Manifold Alignment Determination: finding correspondences across different data views. CoRR Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
2017	—	`conf`	Parallelizable sparse inverse formulation Gaussian processes (SpInGP). MLSP Alexander Grigorievskiy, Neil D. Lawrence, Simo Särkkä
2017	A*	`conf`	Preferential Bayesian Optimization. ICML Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence
2016	—	`conf`	A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity. BCB Muhammad Arifur Rahman, Neil D. Lawrence
2016	—	`conf`	An integrated probabilistic framework for robot perception, learning and memory. ROBIO Uriel Martinez-Hernandez, Andreas C. Damianou, Daniel Camilleri, Luke W. Boorman, Neil D. Lawrence, Tony J. Prescott
2016	A	`conf`	Batch Bayesian Optimization via Local Penalization. AISTATS Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence
2016	A	`conf`	Chained Gaussian Processes. AISTATS Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
2016	J	`jnl`	Chained Gaussian Processes. CoRR Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
2016	J	`jnl`	Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
2016	J	`jnl`	Detecting periodicities with Gaussian processes. PeerJ Prepr. Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
2016	J	`jnl`	Differentially Private Gaussian Processes. CoRR Michael T. Smith, Max Zwiessele, Neil D. Lawrence
2016	A	`conf`	GLASSES: Relieving The Myopia Of Bayesian Optimisation. AISTATS Javier González, Michael A. Osborne, Neil D. Lawrence
2016	J	`jnl`	Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. CoRR Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
2016	A*	`conf`	Recurrent Gaussian Processes. ICLR (Poster) César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence
2016	J	`jnl`	The Emergence of Organizing Structure in Conceptual Representation. CoRR Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum
2016	J	`jnl`	Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. CoRR Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence
2016	A*	`conf`	Variational Auto-encoded Deep Gaussian Processes. ICLR (Poster) Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence
2016	J	`jnl`	Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. J. Mach. Learn. Res. Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
2016	—	`conf`	iCub Visual Memory Inspector: Visualising the iCub's Thoughts. Living Machines Daniel Camilleri, Andreas C. Damianou, Harry Jackson, Neil D. Lawrence, Tony J. Prescott
2015	—	`conf`	A Top-Down Approach for a Synthetic Autobiographical Memory System. Living Machines Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott
2015	J	`jnl`	A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data. BMC Bioinform. Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego di Bernardo
2015	—	`ed.`	Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada NIPS Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett
2015	J	`jnl`	Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans. Pattern Anal. Mach. Intell. James Hensman, Magnus Rattray, Neil D. Lawrence
2015	—	`conf`	Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes. AALTD@PKDD/ECML (Revised Selected Papers) Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence
2015	—	`conf`	Monitoring Short Term Changes of Malaria Incidence in Uganda with Gaussian Processes. AALTD@PKDD/ECML Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence
2015	A	`conf`	Semi-described and semi-supervised learning with Gaussian processes. UAI Andreas C. Damianou, Neil D. Lawrence
2015	J	`jnl`	Semi-described and semi-supervised learning with Gaussian processes. CoRR Andreas C. Damianou, Neil D. Lawrence
2015	J	`jnl`	Spike and Slab Gaussian Process Latent Variable Models. CoRR Zhenwen Dai, James Hensman, Neil D. Lawrence
2014	—	`ed.`	Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada NIPS Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger
2014	J	`jnl`	Fast variational inference for nonparametric clustering of structured time-series. CoRR James Hensman, Magnus Rattray, Neil D. Lawrence
2014	J	`jnl`	Gaussian Process Models with Parallelization and GPU acceleration. CoRR Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence
2014	—	`conf`	Gaussian Processes for Natural Language Processing. ACL (Tutorial Abstracts) Trevor Cohn, Daniel Preotiuc-Pietro, Neil D. Lawrence
2014	A	`conf`	Hybrid Discriminative-Generative Approach with Gaussian Processes. AISTATS Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence
2014	J	`jnl`	Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data. PLoS Comput. Biol. Ciira Wa Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray
2014	A	`conf`	Metrics for Probabilistic Geometries. UAI Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
2014	J	`jnl`	Metrics for Probabilistic Geometries. CoRR Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
2014	A	`conf`	Tilted Variational Bayes. AISTATS James Hensman, Max Zwiessele, Neil D. Lawrence
2014	J	`jnl`	Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
2013	A	`conf`	Deep Gaussian Processes. AISTATS Andreas C. Damianou, Neil D. Lawrence
2013	J	`jnl`	Detecting regulatory gene-environment interactions with unmeasured environmental factors. Bioinform. Nicoló Fusi, Christoph Lippert, Karsten M. Borgwardt, Neil D. Lawrence, Oliver Stegle
2013	A	`conf`	Gaussian Processes for Big Data. UAI James Hensman, Nicoló Fusi, Neil D. Lawrence
2013	J	`jnl`	Gaussian Processes for Big Data. CoRR James Hensman, Nicoló Fusi, Neil D. Lawrence
2013	J	`jnl`	Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinform. James Hensman, Neil D. Lawrence, Magnus Rattray
2013	J	`jnl`	Linear Latent Force Models Using Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
2013	J	`jnl`	Mixture Representations for Inference and Learning in Boltzmann Machines CoRR Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
2013	J	`jnl`	Preface: Intelligent interactive data visualization. Data Min. Knowl. Discov. Barbara Hammer, Daniel A. Keim, Neil D. Lawrence, Guy Lebanon
2013	—	`conf`	The Bigraphical Lasso. ICML (3) Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou
2012	J	`jnl`	A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models. J. Mach. Learn. Res. Neil D. Lawrence
2012	J	`jnl`	Deep Gaussian Processes CoRR Andreas C. Damianou, Neil D. Lawrence
2012	J	`jnl`	Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling
2012	J	`jnl`	Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling
2012	J	`jnl`	Fast Variational Inference in the Conjugate Exponential Family CoRR James Hensman, Magnus Rattray, Neil D. Lawrence
2012	—	`conf`	Fast Variational Inference in the Conjugate Exponential Family. NIPS James Hensman, Magnus Rattray, Neil D. Lawrence
2012	J	`jnl`	Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison. BMC Syst. Biol. Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray
2012	J	`jnl`	Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies. PLoS Comput. Biol. Nicoló Fusi, Oliver Stegle, Neil D. Lawrence
2012	J	`jnl`	Kernels for Vector-Valued Functions: A Review. Found. Trends Mach. Learn. Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence
2012	J	`jnl`	Manifold Relevance Determination CoRR Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence
2012	A*	`conf`	Manifold Relevance Determination. ICML Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence
2012	J	`jnl`	Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition. PLoS Comput. Biol. Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman
2012	J	`jnl`	Overlapping Mixtures of Gaussian Processes for the data association problem. Pattern Recognit. Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence
2012	A	`conf`	Preface. AISTATS Neil D. Lawrence, Mark A. Girolami
2012	A	`ed.`	Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012 AISTATS Neil D. Lawrence, Mark A. Girolami
2012	J	`jnl`	Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models CoRR Alfredo A. Kalaitzis, Neil D. Lawrence
2012	A*	`conf`	Residual Components Analysis. ICML Alfredo A. Kalaitzis, Neil D. Lawrence
2011	J	`jnl`	A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression. BMC Bioinform. Alfredo A. Kalaitzis, Neil D. Lawrence
2011	J	`jnl`	Computationally Efficient Convolved Multiple Output Gaussian Processes. J. Mach. Learn. Res. Mauricio A. Álvarez, Neil D. Lawrence
2011	—	`conf`	Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt
2011	J	`jnl`	Kernels for Vector-Valued Functions: a Review CoRR Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence
2011	J	`jnl`	Linear Latent Force Models using Gaussian Processes CoRR Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
2011	J	`jnl`	Overlapping Mixtures of Gaussian Processes for the Data Association Problem CoRR Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence
2011	J	`jnl`	Residual Component Analysis CoRR Alfredo A. Kalaitzis, Neil D. Lawrence
2011	A	`conf`	Spectral Dimensionality Reduction via Maximum Entropy. AISTATS Neil D. Lawrence
2011	J	`jnl`	Variational Gaussian Process Dynamical Systems CoRR Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
2011	—	`conf`	Variational Gaussian Process Dynamical Systems. NIPS Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
2011	J	`jnl`	tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor. Bioinform. Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence
2010	—	`ch.`	A Brief Introduction to Bayesian Inference. Learning and Inference in Computational Systems Biology Neil D. Lawrence, Magnus Rattray
2010	J	`jnl`	A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction CoRR Neil D. Lawrence
2010	A	`conf`	Bayesian Gaussian Process Latent Variable Model. AISTATS Michalis K. Titsias, Neil D. Lawrence
2010	A	`conf`	Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. AISTATS Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence
2010	—	`ch.`	Gaussian Processes for Missing Species in Biochemical Systems. Learning and Inference in Computational Systems Biology Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias
2010	—	`conf`	Invited Talk Abstracts. AAAI Fall Symposium: Manifold Learning and Its Applications Yi Ma, Fei Sha, Lawrence Carin, Gilad Lerman, Neil D. Lawrence
2010	—	`book`	Learning and Inference in Computational Systems Biology. Neil D. Lawrence, Mark A. Girolami, Magnus Rattray, Guido Sanguinetti
2010	J	`jnl`	Model-based method for transcription factor target identification with limited data. Proc. Natl. Acad. Sci. USA Antti Honkela, Charles Girardot, Eleanor Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray
2010	—	`conf`	Switched Latent Force Models for Movement Segmentation. NIPS Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence
2010	J	`jnl`	TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinform. Hafiz Muhammad Shahzad Asif, Matthew D. Rolfe, Jeffrey Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti
2009	A	`conf`	Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery. BMVC John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence
2009	A	`conf`	Latent Force Models. AISTATS Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
2009	A*	`conf`	Non-linear matrix factorization with Gaussian processes. ICML Neil D. Lawrence, Raquel Urtasun
2009	J	`jnl`	puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinform. Richard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray
2008	—	`conf`	Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS Ben Calderhead, Mark A. Girolami, Neil D. Lawrence
2008	—	`conf`	Ambiguity Modeling in Latent Spaces. MLMI Carl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence
2008	—	`conf`	Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray
2008	C	`conf`	Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. ECCB Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence
2008	—	`conf`	Sparse Convolved Gaussian Processes for Multi-output Regression. NIPS Mauricio A. Álvarez, Neil D. Lawrence
2008	A*	`conf`	Topologically-constrained latent variable models. ICML Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence
2007	C	`conf`	Gaussian Process Latent Variable Models for Fault Detection. CIDM Luka Eciolaza, Muhammad Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming
2007	—	`conf`	Gaussian Process Latent Variable Models for Human Pose Estimation. MLMI Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence
2007	—	`ed.`	Gaussian Processes in Practice, Bletchley Park, Bletchley, UK, June 12-13, 2006 Gaussian Processes in Practice Neil D. Lawrence, Anton Schwaighofer, Joaquin Quiñonero Candela
2007	A*	`conf`	Hierarchical Gaussian process latent variable models. ICML Neil D. Lawrence, Andrew J. Moore
2007	A	`conf`	Learning for Larger Datasets with the Gaussian Process Latent Variable Model. AISTATS Neil D. Lawrence
2007	A	`conf`	Model-driven detection of clean speech patches in noise. INTERSPEECH Jonathan Laidler, Martin Cooke, Neil D. Lawrence
2007	—	`conf`	Modeling Human Locomotion with Topologically Constrained Latent Variable Models. Workshop on Human Motion Raquel Urtasun, David J. Fleet, Neil D. Lawrence
2007	A*	`conf`	WiFi-SLAM Using Gaussian Process Latent Variable Models. IJCAI Brian Ferris, Dieter Fox, Neil D. Lawrence
2006	J	`jnl`	A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinform. Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence
2006	—	`conf`	Fast Variational Inference for Gaussian Process Models Through KL-Correction. ECML Nathaniel John King, Neil D. Lawrence
2006	—	`ch.`	Gaussian Processes and the Null-Category Noise Model. Semi-Supervised Learning Neil D. Lawrence, Michael I. Jordan
2006	—	`conf`	Identifying Submodules of Cellular Regulatory Networks. CMSB Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence
2006	A*	`conf`	Local distance preservation in the GP-LVM through back constraints. ICML Neil D. Lawrence, Joaquin Quiñonero Candela
2006	—	`conf`	Missing Data in Kernel PCA. ECML Guido Sanguinetti, Neil D. Lawrence
2006	—	`conf`	Modelling transcriptional regulation using Gaussian Processes. NIPS Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray
2006	J	`jnl`	Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. J. Mach. Learn. Res. Tonatiuh Peña Centeno, Neil D. Lawrence
2006	J	`jnl`	Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinform. Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray
2006	J	`jnl`	Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinform. Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray
2006	J	`jnl`	Propagating uncertainty in microarray data analysis. Briefings Bioinform. Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence
2005	A	`conf`	A hybrid Maxent/HMM based ASR system. INTERSPEECH Yasser Hifny, Steve Renals, Neil D. Lawrence
2005	J	`jnl`	A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. Bioinform. Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray
2005	J	`jnl`	Accounting for probe-level noise in principal component analysis of microarray data. Bioinform. Guido Sanguinetti, Marta Milo, Magnus Rattray, Neil D. Lawrence
2005	—	`ed.`	Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures Deterministic and Statistical Methods in Machine Learning Joab R. Winkler, Mahesan Niranjan, Neil D. Lawrence
2005	J	`jnl`	Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. J. Mach. Learn. Res. Neil D. Lawrence
2005	J	`jnl`	Variational inference for Student- Neurocomputing Michael E. Tipping, Neil D. Lawrence
2004	—	`conf`	Acoustic space dimensionality selection and combination using the maximum entropy principle. ICASSP (5) Yasser H. Abdel-Haleem, Steve Renals, Neil D. Lawrence
2004	—	`conf`	Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning Neil D. Lawrence, John C. Platt, Michael I. Jordan
2004	A*	`conf`	Learning to learn with the informative vector machine. ICML Neil D. Lawrence, John C. Platt
2004	J	`jnl`	Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinform. Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier
2004	—	`conf`	Semi-supervised Learning via Gaussian Processes. NIPS Neil D. Lawrence, Michael I. Jordan
2003	—	`conf`	A variational approach to robust Bayesian interpolation. NNSP Michael E. Tipping, Neil D. Lawrence
2003	—	`conf`	Bayesian processing of microarray images. NNSP Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier
2003	A	`conf`	Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence
2003	—	`conf`	Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. NIPS Neil D. Lawrence
2003	—	`conf`	Variational Inference for Visual Tracking. CVPR (1) Jaco Vermaak, Neil D. Lawrence, Patrick Pérez
2002	—	`conf`	Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich
2001	J	`jnl`	A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics. Neural Comput. Appl. Boaz Lerner, Neil D. Lawrence
2001	A*	`conf`	Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. ICML Neil D. Lawrence, Bernhard Schölkopf
2001	—	`conf`	Note Relevance Determination. WIRN Neil D. Lawrence
2001	—	`conf`	Optimising Synchronisation Times for Mobile Devices. NIPS Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor
2001	A	`conf`	Probabilistic Modelling of Replica Divergence. HotOS Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop
2001	A	`conf`	Variational Learning for Multi-Layer Networks of Linear Threshold Units. AISTATS Neil D. Lawrence
1998	A	`conf`	Mixture Representations for Inference and Learning in Boltzmann Machines. UAI Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
1997	—	`conf`	Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan