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ORCIDNeil D. Lawrence
Neil David Lawrence
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- affiliation: University of Cambridge, UK
- affiliation: University of Sheffield, Department of Computer Science
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2020 – today
- 2024
[c90]Andrei Paleyes
, Han-Bo Li, Neil D. Lawrence:
Can causality accelerate experimentation in software systems? CAIN 2024: 280-281- [c89]Christian Cabrera, Andrei Paleyes, Neil D. Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends 2024: 5-9 - [c88]Christian Cabrera, Andrei Paleyes, Neil David Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. SATrends@ICSE 2024: 5-9 - [i65]Christian Cabrera, Andrei Paleyes, Neil D. Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR abs/2401.11370 (2024) - [i64]Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence:
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR abs/2405.03879 (2024) - [i63]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 abs/2405.13708 (2024) - [i62]Aditya Ravuri, Neil D. Lawrence:
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. CoRR abs/2405.17412 (2024) - [i61]Aditya Ravuri, Jen Muir, Neil D. Lawrence:
On Feature Learning for Titi Monkey Activity Detection. CoRR abs/2407.01452 (2024) - 2023
- [j44]Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence:
Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. 55(6): 114:1-114:29 (2023) - [j43]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): 289 (2023) - [j42]Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33(1): 3 (2023) - [c87]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CAIN 2023: 7-12 - [c86]Andrei Paleyes, Neil David Lawrence:
Causal fault localisation in dataflow systems. EuroMLSys@EuroSys 2023: 140-147 - [i60]Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence:
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR abs/2302.04810 (2023) - [i59]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
AI for Science: An Emerging Agenda. CoRR abs/2303.04217 (2023) - [i58]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CoRR abs/2303.09552 (2023) - [i57]Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence:
Dimensionality Reduction as Probabilistic Inference. CoRR abs/2304.07658 (2023) - [i56]Andrei Paleyes, Neil D. Lawrence:
Causal fault localisation in dataflow systems. CoRR abs/2304.11987 (2023) - [i55]Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes:
Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR abs/2311.15691 (2023) - 2022
- [c85]Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised GPLVM with Stochastic Variational Inference. AISTATS 2022: 7841-7864 - [c84]Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo:
Two-way Sparse Network Inference for Count Data. AISTATS 2022: 10924-10938 - [c83]Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An empirical evaluation of flow based programming in the machine learning deployment context. CAIN 2022: 54-64 - [c82]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: 46-60 - [c81]Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence:
Modeling the Machine Learning Multiverse. NeurIPS 2022 - [i54]Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR abs/2202.12979 (2022) - [i53]Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo:
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data. CoRR abs/2203.07912 (2022) - [i52]Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR abs/2204.12781 (2022) - [i51]Samuel J. Bell, Neil D. Lawrence:
The Effect of Task Ordering in Continual Learning. CoRR abs/2205.13323 (2022) - [i50]Samuel J. Bell, Onno Pepijn Kampman, Jesse Dodge, Neil D. Lawrence:
Modeling the Machine Learning Multiverse. CoRR abs/2206.05985 (2022) - [i49]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 abs/2209.06716 (2022) - [i48]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 abs/2210.16568 (2022) - [i47]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): 150-199 (2022) - 2021
- [j41]Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence:
Solving Schrödinger Bridges via Maximum Likelihood. Entropy 23(9): 1134 (2021) - [j40]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: 86:1-86:51 (2021) - [j39]Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence:
Differentially Private Regression and Classification with Sparse Gaussian Processes. J. Mach. Learn. Res. 22: 188:1-188:41 (2021) - [c80]Pierre Thodoroff, Wenyu Li, Neil D. Lawrence:
Benchmarking Real-Time Reinforcement Learning. Pre-Registration Workshop @ NeurIPS 2021: 26-41 - [i46]Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft:
Solving Schrödinger Bridges via Maximum Likelihood. CoRR abs/2106.02081 (2021) - [i45]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 abs/2108.04105 (2021) - [i44]Corinna Cortes, Neil D. Lawrence:
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment. CoRR abs/2109.09774 (2021) - [i43]Samuel J. Bell, Neil D. Lawrence:
Behavioral Experiments for Understanding Catastrophic Forgetting. CoRR abs/2110.10570 (2021) - [i42]Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González:
Emulation of physical processes with Emukit. CoRR abs/2110.13293 (2021) - [i41]Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR abs/2111.10510 (2021) - 2020
- [j38]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): 3651-3661 (2020) - [c79]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 - [i40]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 abs/2004.12696 (2020) - [i39]Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence:
Challenges in Deploying Machine Learning: a Survey of Case Studies. CoRR abs/2011.09926 (2020)
2010 – 2019
- 2019
- [j37]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) - [j36]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): 2953-2960 (2019) - [c78]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CVPR 2019: 9163-9171 - [c77]Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou:
Transferring Knowledge across Learning Processes. ICLR 2019 - [c76]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS 2019: 6267-6277 - [c75]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:
Usability of Probabilistic Programming Languages. PPIG 2019 - [i38]Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González:
Deep Gaussian Processes for Multi-fidelity Modeling. CoRR abs/1903.07320 (2019) - [i37]Neil D. Lawrence:
Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design. CoRR abs/1903.11241 (2019) - [i36]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CoRR abs/1904.05835 (2019) - [i35]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR abs/1905.12982 (2019) - [i34]Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence:
Differentially Private Regression and Classification with Sparse Gaussian Processes. CoRR abs/1909.09147 (2019) - 2018
- [c74]Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence:
Differentially Private Regression with Gaussian Processes. AISTATS 2018: 1195-1203 - [c73]Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence:
Structured Variationally Auto-encoded Optimization. ICML 2018: 3273-3281 - [i33]Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson:
Intrinsic Gaussian processes on complex constrained domains. CoRR abs/1801.01061 (2018) - [i32]Michael Thomas Smith, Mauricio A. Álvarez, Neil D. Lawrence:
Gaussian Process Regression for Binned Data. CoRR abs/1809.02010 (2018) - [i31]Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou:
Transferring Knowledge across Learning Processes. CoRR abs/1812.01054 (2018) - 2017
- [j35]Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray:
Efficient inference for sparse latent variable models of transcriptional regulation. Bioinform. 33(23): 3776-3783 (2017) - [c72]Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence:
Preferential Bayesian Optimization. ICML 2017: 1282-1291 - [c71]Alexander Grigorievskiy, Neil D. Lawrence, Simo Särkkä:
Parallelizable sparse inverse formulation Gaussian processes (SpInGP). MLSP 2017: 1-6 - [c70]Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence:
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS 2017: 5131-5139 - [i30]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Manifold Alignment Determination: finding correspondences across different data views. CoRR abs/1701.03449 (2017) - [i29]Neil D. Lawrence:
Data Readiness Levels. CoRR abs/1705.02245 (2017) - [i28]Neil D. Lawrence:
Living Together: Mind and Machine Intelligence. CoRR abs/1705.07996 (2017) - [i27]Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence:
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. CoRR abs/1705.09862 (2017) - [i26]Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence:
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. CoRR abs/1709.05409 (2017) - [i25]Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence:
Auto-Differentiating Linear Algebra. CoRR abs/1710.08717 (2017) - 2016
- [j34]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: 42:1-42:62 (2016) - [j33]Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence:
Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. 2: e50 (2016) - [c69]Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence:
Batch Bayesian Optimization via Local Penalization. AISTATS 2016: 648-657 - [c68]Javier González, Michael A. Osborne, Neil D. Lawrence:
GLASSES: Relieving The Myopia Of Bayesian Optimisation. AISTATS 2016: 790-799 - [c67]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. AISTATS 2016: 1431-1440 - [c66]Muhammad Arifur Rahman, Neil D. Lawrence:
A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity. BCB 2016: 495-496 - [c65]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: 48-57 - [c64]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: 1796-1801 - [c63]Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence:
Variational Auto-encoded Deep Gaussian Processes. ICLR (Poster) 2016 - [c62]César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence:
Recurrent Gaussian Processes. ICLR (Poster) 2016 - [i24]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. CoRR abs/1604.04939 (2016) - [i23]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. CoRR abs/1604.05263 (2016) - [i22]Michael T. Smith, Max Zwiessele, Neil D. Lawrence:
Differentially Private Gaussian Processes. CoRR abs/1606.00720 (2016) - [i21]Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence:
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. CoRR abs/1607.00067 (2016) - [i20]Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum:
The Emergence of Organizing Structure in Conceptual Representation. CoRR abs/1611.09384 (2016) - [i19]Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence:
Detecting periodicities with Gaussian processes. PeerJ Prepr. 4: e1743 (2016) - 2015
- [j32]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: 279:1-279:9 (2015) - [j31]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 383-393 (2015) - [c61]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: 280-292 - [c60]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 - [c59]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: 95-110 - [c58]Andreas C. Damianou, Neil D. Lawrence:
Semi-described and semi-supervised learning with Gaussian processes. UAI 2015: 228-237 - [e6]Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett:
Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada. 2015 [contents] - [i18]Zhenwen Dai, James Hensman, Neil D. Lawrence:
Spike and Slab Gaussian Process Latent Variable Models. CoRR abs/1505.02434 (2015) - [i17]Andreas C. Damianou, Neil D. Lawrence:
Semi-described and semi-supervised learning with Gaussian processes. CoRR abs/1509.01168 (2015) - 2014
- [j30]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) - [c57]Trevor Cohn, Daniel Preotiuc-Pietro, Neil D. Lawrence:
Gaussian Processes for Natural Language Processing. ACL (Tutorial Abstracts) 2014: 1-3 - [c56]Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence:
Hybrid Discriminative-Generative Approach with Gaussian Processes. AISTATS 2014: 47-56 - [c55]James Hensman, Max Zwiessele, Neil D. Lawrence:
Tilted Variational Bayes. AISTATS 2014: 356-364 - [c54]Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence:
Metrics for Probabilistic Geometries. UAI 2014: 800-808 - [e5]Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 [contents] - [i16]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast variational inference for nonparametric clustering of structured time-series. CoRR abs/1401.1605 (2014) - [i15]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR abs/1409.2287 (2014) - [i14]Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence:
Gaussian Process Models with Parallelization and GPU acceleration. CoRR abs/1410.4984 (2014) - [i13]Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence:
Metrics for Probabilistic Geometries. CoRR abs/1411.7432 (2014) - 2013
- [j29]Nicoló Fusi, Christoph Lippert, Karsten M. Borgwardt, Neil D. Lawrence, Oliver Stegle:
Detecting regulatory gene-environment interactions with unmeasured environmental factors. Bioinform. 29(11): 1382-1389 (2013) - [j28]James Hensman, Neil D. Lawrence, Magnus Rattray:
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinform. 14: 252 (2013) - [j27]Barbara Hammer, Daniel A. Keim, Neil D. Lawrence, Guy Lebanon:
Preface: Intelligent interactive data visualization. Data Min. Knowl. Discov. 27(1): 1-3 (2013) - [j26]Mauricio A. Álvarez, David Luengo, Neil D. Lawrence:
Linear Latent Force Models Using Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 35(11): 2693-2705 (2013) - [c53]Andreas C. Damianou, Neil D. Lawrence:
Deep Gaussian Processes. AISTATS 2013: 207-215 - [c52]Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou:
The Bigraphical Lasso. ICML (3) 2013: 1229-1237 - [c51]James Hensman, Nicoló Fusi, Neil D. Lawrence:
Gaussian Processes for Big Data. UAI 2013 - [i12]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines. CoRR abs/1301.7393 (2013) - [i11]James Hensman, Nicoló Fusi, Neil D. Lawrence:
Gaussian Processes for Big Data. CoRR abs/1309.6835 (2013) - 2012
- [j25]Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray:
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison. BMC Syst. Biol. 6: 53 (2012) - [j24]Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence:
Kernels for Vector-Valued Functions: A Review. Found. Trends Mach. Learn. 4(3): 195-266 (2012) - [j23]Neil D. Lawrence:
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models. J. Mach. Learn. Res. 13: 1609-1638 (2012) - [j22]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 209-210 (2012) - [j21]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(5): 833 (2012) - [j20]Nicoló Fusi, Oliver Stegle, Neil D. Lawrence:
Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies. PLoS Comput. Biol. 8(1) (2012) - [j19]Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman:
Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition. PLoS Comput. Biol. 8(5) (2012) - [j18]Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence:
Overlapping Mixtures of Gaussian Processes for the data association problem. Pattern Recognit. 45(4): 1386-1395 (2012) - [c50]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. ICML 2012 - [c49]Alfredo A. Kalaitzis, Neil D. Lawrence:
Residual Components Analysis. ICML 2012 - [c48]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Variational Inference in the Conjugate Exponential Family. NIPS 2012: 2897-2905 - [c47]Neil D. Lawrence, Mark A. Girolami:
Preface. AISTATS 2012 - [e4]Neil D. Lawrence, Mark A. Girolami:
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012. JMLR Proceedings 22, JMLR.org 2012 [contents] - [i10]Alfredo A. Kalaitzis, Neil D. Lawrence:
Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models. CoRR abs/1206.4560 (2012) - [i9]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. CoRR abs/1206.4610 (2012) - [i8]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Variational Inference in the Conjugate Exponential Family. CoRR abs/1206.5162 (2012) - [i7]Andreas C. Damianou, Neil D. Lawrence:
Deep Gaussian Processes. CoRR abs/1211.0358 (2012) - 2011
- [j17]Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence:
tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor. Bioinform. 27(7): 1026-1027 (2011) - [j16]Alfredo A. Kalaitzis, Neil D. Lawrence:
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression. BMC Bioinform. 12: 180 (2011) - [j15]Mauricio A. Álvarez, Neil D. Lawrence:
Computationally Efficient Convolved Multiple Output Gaussian Processes. J. Mach. Learn. Res. 12: 1459-1500 (2011) - [c46]Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt:
Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS 2011: 630-638 - [c45]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. NIPS 2011: 2510-2518 - [c44]Neil D. Lawrence:
Spectral Dimensionality Reduction via Maximum Entropy. AISTATS 2011: 51-59 - [i6]Alfredo A. Kalaitzis, Neil D. Lawrence:
Residual Component Analysis. CoRR abs/1106.4333 (2011) - [i5]Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence:
Kernels for Vector-Valued Functions: a Review. CoRR abs/1106.6251 (2011) - [i4]Mauricio A. Álvarez, David Luengo, Neil D. Lawrence:
Linear Latent Force Models using Gaussian Processes. CoRR abs/1107.2699 (2011) - [i3]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. CoRR abs/1107.4985 (2011) - [i2]Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence:
Overlapping Mixtures of Gaussian Processes for the Data Association Problem. CoRR abs/1108.3372 (2011) - 2010
- [j14]Hafiz Muhammad Shahzad Asif, Matthew D. Rolfe, Jeffrey Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti:
TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinform. 26(20): 2635-2636 (2010) - [j13]Antti Honkela, Charles Girardot, Eleanor Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray:
Model-based method for transcription factor target identification with limited data. Proc. Natl. Acad. Sci. USA 107(17): 7793-7798 (2010) - [c43]Yi Ma, Fei Sha, Lawrence Carin, Gilad Lerman, Neil D. Lawrence:
Invited Talk Abstracts. AAAI Fall Symposium: Manifold Learning and Its Applications 2010 - [c42]Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence:
Switched Latent Force Models for Movement Segmentation. NIPS 2010: 55-63 - [c41]Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence:
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. AISTATS 2010: 25-32 - [c40]Michalis K. Titsias, Neil D. Lawrence:
Bayesian Gaussian Process Latent Variable Model. AISTATS 2010: 844-851 - [p3]Neil D. Lawrence, Magnus Rattray:
A Brief Introduction to Bayesian Inference. Learning and Inference in Computational Systems Biology 2010: 97-116 - [p2]Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias:
Gaussian Processes for Missing Species in Biochemical Systems. Learning and Inference in Computational Systems Biology 2010: 231-252 - [e3]Neil D. Lawrence, Mark A. Girolami, Magnus Rattray, Guido Sanguinetti:
Learning and Inference in Computational Systems Biology. Computational molecular biology, MIT Press 2010, ISBN 978-0-262-01386-4 [contents] - [i1]Neil D. Lawrence:
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction. CoRR abs/1010.4830 (2010)
2000 – 2009
- 2009
- [j12]Richard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray:
puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinform. 10 (2009) - [c39]John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence:
Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery. BMVC 2009: 1-11 - [c38]Neil D. Lawrence, Raquel Urtasun:
Non-linear matrix factorization with Gaussian processes. ICML 2009: 601-608 - [c37]Mauricio A. Álvarez, David Luengo, Neil D. Lawrence:
Latent Force Models. AISTATS 2009: 9-16 - 2008
- [c36]Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence:
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. ECCB 2008: 70-75 - [c35]Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence:
Topologically-constrained latent variable models. ICML 2008: 1080-1087 - [c34]Carl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence:
Ambiguity Modeling in Latent Spaces. MLMI 2008: 62-73 - [c33]Mauricio A. Álvarez, Neil D. Lawrence:
Sparse Convolved Gaussian Processes for Multi-output Regression. NIPS 2008: 57-64 - [c32]Ben Calderhead, Mark A. Girolami, Neil D. Lawrence:
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS 2008: 217-224 - [c31]Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray:
Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS 2008: 1681-1688 - 2007
- [c30]Luka Eciolaza, Muhammad Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming:
Gaussian Process Latent Variable Models for Fault Detection. CIDM 2007: 287-292 - [c29]Raquel Urtasun, David J. Fleet, Neil D. Lawrence:
Modeling Human Locomotion with Topologically Constrained Latent Variable Models. Workshop on Human Motion 2007: 104-118 - [c28]Neil D. Lawrence, Andrew J. Moore:
Hierarchical Gaussian process latent variable models. ICML 2007: 481-488 - [c27]Brian Ferris, Dieter Fox, Neil D. Lawrence:
WiFi-SLAM Using Gaussian Process Latent Variable Models. IJCAI 2007: 2480-2485 - [c26]Jonathan Laidler, Martin Cooke, Neil D. Lawrence:
Model-driven detection of clean speech patches in noise. INTERSPEECH 2007: 922-925 - [c25]Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence:
Gaussian Process Latent Variable Models for Human Pose Estimation. MLMI 2007: 132-143 - [c24]Neil D. Lawrence:
Learning for Larger Datasets with the Gaussian Process Latent Variable Model. AISTATS 2007: 243-250 - [e2]Neil D. Lawrence, Anton Schwaighofer, Joaquin Quiñonero Candela:
Gaussian Processes in Practice, Bletchley Park, Bletchley, UK, June 12-13, 2006. JMLR Proceedings 1, JMLR.org 2007 [contents] - 2006
- [j11]Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence:
Propagating uncertainty in microarray data analysis. Briefings Bioinform. 7(1): 37-47 (2006) - [j10]Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence:
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinform. 22(14): 1753-1759 (2006) - [j9]Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray:
Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinform. 22(17): 2107-2113 (2006) - [j8]Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray:
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinform. 22(22): 2775-2781 (2006) - [j7]Tonatiuh Peña Centeno, Neil D. Lawrence:
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. J. Mach. Learn. Res. 7: 455-491 (2006) - [c23]Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence:
Identifying Submodules of Cellular Regulatory Networks. CMSB 2006: 155-168 - [c22]Nathaniel John King, Neil D. Lawrence:
Fast Variational Inference for Gaussian Process Models Through KL-Correction. ECML 2006: 270-281 - [c21]Guido Sanguinetti, Neil D. Lawrence:
Missing Data in Kernel PCA. ECML 2006: 751-758 - [c20]Neil D. Lawrence, Joaquin Quiñonero Candela:
Local distance preservation in the GP-LVM through back constraints. ICML 2006: 513-520 - [c19]Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray:
Modelling transcriptional regulation using Gaussian Processes. NIPS 2006: 785-792 - [p1]Neil D. Lawrence, Michael I. Jordan:
Gaussian Processes and the Null-Category Noise Model. Semi-Supervised Learning 2006: 136-150 - 2005
- [j6]Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray:
A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. Bioinform. 21(18): 3637-3644 (2005) - [j5]Guido Sanguinetti, Marta Milo, Magnus Rattray, Neil D. Lawrence:
Accounting for probe-level noise in principal component analysis of microarray data. Bioinform. 21(19): 3748-3754 (2005) - [j4]Michael E. Tipping, Neil D. Lawrence:
Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing 69(1-3): 123-141 (2005) - [j3]Neil D. Lawrence:
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. J. Mach. Learn. Res. 6: 1783-1816 (2005) - [c18]Yasser Hifny, Steve Renals, Neil D. Lawrence:
A hybrid Maxent/HMM based ASR system. INTERSPEECH 2005: 3017-3020 - [e1]Joab R. Winkler, Mahesan Niranjan, Neil D. Lawrence:
Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures. Lecture Notes in Computer Science 3635, Springer 2005, ISBN 3-540-29073-7 [contents] - 2004
- [j2]Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier:
Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinform. 20(4): 518-526 (2004) - [c17]Neil D. Lawrence, John C. Platt, Michael I. Jordan:
Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning 2004: 56-87 - [c16]Yasser H. Abdel-Haleem, Steve Renals, Neil D. Lawrence:
Acoustic space dimensionality selection and combination using the maximum entropy principle. ICASSP (5) 2004: 637-640 - [c15]Neil D. Lawrence, John C. Platt:
Learning to learn with the informative vector machine. ICML 2004 - [c14]Neil D. Lawrence, Michael I. Jordan:
Semi-supervised Learning via Gaussian Processes. NIPS 2004: 753-760 - 2003
- [c13]Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence:
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS 2003: 254-261 - [c12]Jaco Vermaak, Neil D. Lawrence, Patrick Pérez:
Variational Inference for Visual Tracking. CVPR (1) 2003: 773-780 - [c11]Neil D. Lawrence:
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. NIPS 2003: 329-336 - [c10]Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier:
Bayesian processing of microarray images. NNSP 2003: 71-80 - [c9]Michael E. Tipping, Neil D. Lawrence:
A variational approach to robust Bayesian interpolation. NNSP 2003: 229-238 - 2002
- [c8]Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich:
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616 - 2001
- [j1]Boaz Lerner, Neil D. Lawrence:
A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics. Neural Comput. Appl. 10(1): 39-47 (2001) - [c7]Neil D. Lawrence:
Variational Learning for Multi-Layer Networks of Linear Threshold Units. AISTATS 2001: 168-175 - [c6]Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop:
Probabilistic Modelling of Replica Divergence. HotOS 2001: 55-60 - [c5]Neil D. Lawrence, Bernhard Schölkopf:
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. ICML 2001: 306-313 - [c4]Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor:
Optimising Synchronisation Times for Mobile Devices. NIPS 2001: 1401-1408 - [c3]Neil D. Lawrence:
Note Relevance Determination. WIRN 2001: 128-133
1990 – 1999
- 1998
- [c2]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327 - 1997
- [c1]Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan:
Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997: 416-422
Coauthor Index
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