Neil D. Lawrence

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 84

Top Venues

Publications

Andrei Paleyes, Han-Bo Li, Neil D. Lawrence
Can causality accelerate experimentation in software systems? CAIN (2024)
Aditya Ravuri, Neil D. Lawrence
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. CoRR (2024)
Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR (2024)
Aditya Ravuri, Jen Muir, Neil D. Lawrence
On Feature Learning for Titi Monkey Activity Detection. CoRR (2024)
Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen
Requirements are All You Need: The Final Frontier for End-User Software Engineering. CoRR (2024)
Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR (2024)
Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
Dimensionality Reduction as Probabilistic Inference. CoRR (2023)
Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR (2023)
Andrei Paleyes, Neil D. Lawrence
Causal fault localisation in dataflow systems. CoRR (2023)
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
AI for Science: An Emerging Agenda. CoRR (2023)
Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR (2023)
Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
Dataflow graphs as complete causal graphs. CAIN (2023)
Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
Dataflow graphs as complete causal graphs. CoRR (2023)
Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence
Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy 25 (2) (2023)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33 (1) (2023)
Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. 55 (6) (2023)
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports 12 (9) (2022)
Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB (2022)
Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo
Two-way Sparse Network Inference for Count Data. AISTATS (2022)
Samuel J. Bell, Onno Pepijn Kampman, Jesse Dodge, Neil D. Lawrence
Modeling the Machine Learning Multiverse. CoRR (2022)
Samuel J. Bell, Neil D. Lawrence
The Effect of Task Ordering in Continual Learning. CoRR (2022)
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR (2022)
Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR (2022)
Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence
Modeling the Machine Learning Multiverse. NeurIPS (2022)
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
Ice Core Dating using Probabilistic Programming. CoRR (2022)
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Generalised GPLVM with Stochastic Variational Inference. AISTATS (2022)
Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
An empirical evaluation of flow based programming in the machine learning deployment context. CAIN (2022)
Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR (2022)
Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data. CoRR (2022)
Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft
Solving Schrödinger Bridges via Maximum Likelihood. CoRR (2021)
Corinna Cortes, Neil D. Lawrence
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment. CoRR (2021)
Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence
Solving Schrödinger Bridges via Maximum Likelihood. Entropy 23 (9) (2021)
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier Gonzalez
Emulation of physical processes with Emukit. CoRR (2021)
Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. J. Mach. Learn. Res. 22 (2021)
Pierre Thodoroff, Wenyu Li, Neil D. Lawrence
Benchmarking Real-Time Reinforcement Learning. Pre-Registration Workshop @ NeurIPS (2021)
Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
Differentially Private Regression and Classification with Sparse Gaussian Processes. J. Mach. Learn. Res. 22 (2021)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR (2021)
Andrei Paleyes, Christian Cabrera, Neil D. Lawrence
Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures. CoRR (2021)
Samuel J. Bell, Neil D. Lawrence
Behavioral Experiments for Understanding Catastrophic Forgetting. CoRR (2021)
Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan
Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes. IEEE Trans. Ind. Informatics 16 (6) (2020)
Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. ICLR (2020)
Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. CoRR (2020)
Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
Challenges in Deploying Machine Learning: a Survey of Case Studies. CoRR (2020)
Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González
Deep Gaussian Processes for Multi-fidelity Modeling. CoRR (2019)
Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou
Transferring Knowledge across Learning Processes. ICLR (2019)
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS (2019)
Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Trans. Autom. Control. 64 (7) (2019)
Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai
Variational Information Distillation for Knowledge Transfer. CVPR (2019)
Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai
Variational Information Distillation for Knowledge Transfer. CoRR (2019)
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR (2019)
Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan
A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant. Appl. Soft Comput. 82 (2019)
Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
Differentially Private Regression and Classification with Sparse Gaussian Processes. CoRR (2019)
Neil D. Lawrence
Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design. CoRR (2019)
Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence
Gaussian Process Regression for Binned Data. CoRR (2018)
Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou
Transferring Knowledge across Learning Processes. CoRR (2018)
Michael Thomas Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence
Differentially Private Regression with Gaussian Processes. AISTATS (2018)
Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson
Intrinsic Gaussian processes on complex constrained domains. CoRR (2018)
Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence
Structured Variationally Auto-encoded Optimization. ICML (2018)
Alexander Grigorievskiy, Neil D. Lawrence, Simo Särkkä
Parallelizable sparse inverse formulation Gaussian processes (SpInGP). MLSP (2017)
Neil D. Lawrence
Living Together: Mind and Machine Intelligence. CoRR (2017)
Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. CoRR (2017)
Neil D. Lawrence
Data Readiness Levels. CoRR (2017)
Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence
Preferential Bayesian Optimization. ICML (2017)
Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray
Efficient inference for sparse latent variable models of transcriptional regulation. Bioinform. 33 (23) (2017)
Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. CoRR (2017)
Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
Manifold Alignment Determination: finding correspondences across different data views. CoRR (2017)
Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS (2017)
Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence
Auto-Differentiating Linear Algebra. CoRR (2017)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. 2 (2016)
Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. CoRR (2016)
Daniel Camilleri, Andreas C. Damianou, Harry Jackson, Neil D. Lawrence, Tony J. Prescott
iCub Visual Memory Inspector: Visualising the iCub's Thoughts. Living Machines (2016)
Muhammad Arifur Rahman, Neil D. Lawrence
A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity. BCB (2016)
Uriel Martinez-Hernandez, Andreas C. Damianou, Daniel Camilleri, Luke W. Boorman, Neil D. Lawrence, Tony J. Prescott
An integrated probabilistic framework for robot perception, learning and memory. ROBIO (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. AISTATS (2016)
Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence
Differentially Private Gaussian Processes. CoRR (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. CoRR (2016)
Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. CoRR (2016)
Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum
The Emergence of Organizing Structure in Conceptual Representation. CoRR (2016)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. J. Mach. Learn. Res. 17 (2016)
César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence
Recurrent Gaussian Processes. ICLR (Poster) (2016)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Prepr. 4 (2016)
Javier González, Michael A. Osborne, Neil D. Lawrence
GLASSES: Relieving The Myopia Of Bayesian Optimisation. AISTATS (2016)
Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence
Batch Bayesian Optimization via Local Penalization. AISTATS (2016)
Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence
Variational Auto-encoded Deep Gaussian Processes. ICLR (Poster) (2016)
Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego di Bernardo
A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data. BMC Bioinform. 16 (2015)
Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott
A Top-Down Approach for a Synthetic Autobiographical Memory System. Living Machines (2015)
James Hensman, Magnus Rattray, Neil D. Lawrence
Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence
Monitoring Short Term Changes of Malaria Incidence in Uganda with Gaussian Processes. AALTD@PKDD/ECML (2015)
Andreas C. Damianou, Neil D. Lawrence
Semi-described and semi-supervised learning with Gaussian processes. UAI (2015)
Ricardo Andrade Pacheco, Martin Gordon Mubangizi, John A. Quinn, Neil D. Lawrence
Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes. AALTD@PKDD/ECML (Revised Selected Papers) (2015)
Zhenwen Dai, James Hensman, Neil D. Lawrence
Spike and Slab Gaussian Process Latent Variable Models. CoRR (2015)
Andreas C. Damianou, Neil D. Lawrence
Semi-described and semi-supervised learning with Gaussian processes. CoRR (2015)
James Hensman, Max Zwiessele, Neil D. Lawrence
Tilted Variational Bayes. AISTATS (2014)
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
Metrics for Probabilistic Geometries. CoRR (2014)
Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence
Gaussian Process Models with Parallelization and GPU acceleration. CoRR (2014)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR (2014)
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
Metrics for Probabilistic Geometries. UAI (2014)
Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence
Hybrid Discriminative-Generative Approach with Gaussian Processes. AISTATS (2014)
Ciira Wa Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data. PLoS Comput. Biol. 10 (5) (2014)
Trevor Cohn, Daniel Preotiuc-Pietro, Neil D. Lawrence
Gaussian Processes for Natural Language Processing. ACL (Tutorial Abstracts) (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Neil D. Lawrence