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Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures


Aug 09, 2021
Andrei Paleyes, Christian Cabrera, Neil D. Lawrence


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Solving Schrödinger Bridges via Maximum Likelihood


Jun 17, 2021
Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft

* 9 pages + appendix (total 28 pages) 

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Challenges in Deploying Machine Learning: a Survey of Case Studies


Nov 18, 2020
Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence

* The ML-Retrospectives, Surveys & Meta-Analyses Workshop, NeurIPS 2020 

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Empirical Bayes Transductive Meta-Learning with Synthetic Gradients


Apr 27, 2020
Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou

* ICLR 2020 

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Differentially Private Regression and Classification with Sparse Gaussian Processes


Sep 19, 2019
Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence

* 26 pages, 6 figures. Submitted to JMLR 4th January, 2019 (in review) 

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Variational Information Distillation for Knowledge Transfer


Apr 11, 2019
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai

* To appear at CVPR 2019 

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Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design


Mar 26, 2019
Neil D. Lawrence

* Paper presented at the Stu Hunter Research Conference held at the Villa Porro Pirelli in Induno Olona, Italy, from Sunday February 17th to Wednesday February 20th, 2019 

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Transferring Knowledge across Learning Processes


Dec 03, 2018
Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou

* 22 pages, 8 figures, 6 tables 

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Auto-Differentiating Linear Algebra


Oct 31, 2018
Matthias Seeger, Asmus Hetzel, Zhenwen Dai, Eric Meissner, Neil D. Lawrence


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


Aug 13, 2018
Simo SÀrkkÀ, Mauricio A. Álvarez, Neil D. Lawrence


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The Emergence of Organizing Structure in Conceptual Representation


Nov 22, 2017
Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum

* In press at Cognitive Science 

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Differentially Private Gaussian Processes


May 30, 2017
Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence

* 9 pages + 4 supplementary material pages, 6 plots grouped into 5 figures, submitted to NIPS 2017 

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Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes


May 27, 2017
Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence


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Living Together: Mind and Machine Intelligence


May 22, 2017
Neil D. Lawrence


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Data Readiness Levels


May 05, 2017
Neil D. Lawrence


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Preferential Bayesian Optimization


Apr 12, 2017
Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

* 10 pages, 6 figures 

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Manifold Alignment Determination: finding correspondences across different data views


Jan 12, 2017
Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

* NIPS workshop on Multi-Modal Machine Learning, 2015 

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Chained Gaussian Processes


Apr 18, 2016
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

* Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain 

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


Apr 17, 2016
Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

* 49 pages including appendix 

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Recurrent Gaussian Processes


Feb 24, 2016
CĂ©sar Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

* Published as a conference paper at ICLR 2016. 12 pages, 3 figures 

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GLASSES: Relieving The Myopia Of Bayesian Optimisation


Oct 21, 2015
Javier GonzĂĄlez, Michael Osborne, Neil D. Lawrence

* 12 pages, 9 figures 

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Batch Bayesian Optimization via Local Penalization


Oct 15, 2015
Javier GonzĂĄlez, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

* 11 pages, 10 figures 

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Semi-described and semi-supervised learning with Gaussian processes


Sep 03, 2015
Andreas Damianou, Neil D. Lawrence

* Published in the proceedings for Uncertainty in Artificial Intelligence (UAI), 2015 

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Bayesian Optimization for Synthetic Gene Design


May 07, 2015
Javier GonzĂĄlez, Joseph Longworth, David C. James, Neil D. Lawrence

* 6 pages, 3 figures. NIPS 2014, Workshop in Bayesian Optimization 

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Nested Variational Compression in Deep Gaussian Processes


Dec 03, 2014
James Hensman, Neil D. Lawrence


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Metrics for Probabilistic Geometries


Nov 27, 2014
Alessandra Tosi, SĂžren Hauberg, Alfredo Vellido, Neil D. Lawrence

* UAI 2014 

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Variational Inference for Uncertainty on the Inputs of Gaussian Process Models


Sep 08, 2014
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

* 51 pages (of which 10 is Appendix), 19 figures 

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Fast nonparametric clustering of structured time-series


Apr 14, 2014
James Hensman, Magnus Rattray, Neil D. Lawrence

* Accepted for publication in special edition of TPAMI on Bayesian Nonparametrics 

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Gaussian Processes for Big Data


Sep 26, 2013
James Hensman, Nicolo Fusi, Neil D. Lawrence

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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