| 2024 | CAIN | Can causality accelerate experimentation in software systems? | Andrei Paleyes, Han-Bo Li, Neil D. Lawrence |
| 2024 | ICSE | Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. | Christian Cabrera, Andrei Paleyes, Neil D. Lawrence |
| 2023 | CAIN | Dataflow graphs as complete causal graphs. | Andrei Paleyes, Siyuan Guo, Bernhard Schlkopf, Neil D. Lawrence |
| 2022 | AISTATS | Generalised GPLVM with Stochastic Variational Inference. | Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence |
| 2022 | AISTATS | Two-way Sparse Network Inference for Count Data. | Sijia Li, Martn Lpez-Garca, Neil D. Lawrence, Luisa Cutillo |
| 2022 | CAIN | An empirical evaluation of flow based programming in the machine learning deployment context. | Andrei Paleyes, Christian Cabrera, Neil D. Lawrence |
| 2020 | ICLR | Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. | Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou |
| 2019 | CVPR | Variational Information Distillation for Knowledge Transfer. | Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai |
| 2019 | ICLR | Transferring Knowledge across Learning Processes. | Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou |
| 2018 | AISTATS | Differentially Private Regression with Gaussian Processes. | Michael T. Smith, Mauricio A. lvarez, Max Zwiessele, Neil D. Lawrence |
| 2018 | ICML | Structured Variationally Auto-encoded Optimization. | Xiaoyu Lu, Javier Gonzlez, Zhenwen Dai, Neil D. Lawrence |
| 2017 | ICML | Preferential Bayesian Optimization. | Javier Gonzlez, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence |
| 2016 | AISTATS | Batch Bayesian Optimization via Local Penalization. | Javier Gonzlez, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence |
| 2016 | AISTATS | GLASSES: Relieving The Myopia Of Bayesian Optimisation. | Javier Gonzlez, Michael A. Osborne, Neil D. Lawrence |
| 2016 | AISTATS | Chained Gaussian Processes. | Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence |
| 2015 | UAI | Semi-described and semi-supervised learning with Gaussian processes. | Andreas C. Damianou, Neil D. Lawrence |
| 2014 | ACL | Gaussian Processes for Natural Language Processing. | Trevor Cohn, Daniel Preotiuc-Pietro, Neil D. Lawrence |
| 2014 | AISTATS | Tilted Variational Bayes. | James Hensman, Max Zwiessele, Neil D. Lawrence |
| 2014 | AISTATS | Hybrid Discriminative-Generative Approach with Gaussian Processes. | Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence |
| 2014 | UAI | Metrics for Probabilistic Geometries. | Alessandra Tosi, Sren Hauberg, Alfredo Vellido, Neil D. Lawrence |
| 2013 | AISTATS | Deep Gaussian Processes. | Andreas C. Damianou, Neil D. Lawrence |
| 2013 | ICML | The Bigraphical Lasso. | Alfredo A. Kalaitzis, John D. Lafferty, Neil D. Lawrence, Shuheng Zhou |
| 2013 | UAI | Gaussian Processes for Big Data. | James Hensman, Nicol Fusi, Neil D. Lawrence |
| 2012 | ICML | Manifold Relevance Determination. | Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence |
| 2012 | ICML | Residual Components Analysis. | Alfredo A. Kalaitzis, Neil D. Lawrence |
| 2009 | BMVC | Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery. | John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence |
| 2009 | ICML | Non-linear matrix factorization with Gaussian processes. | Neil D. Lawrence, Raquel Urtasun |
| 2008 | ECCB | Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. | Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence |
| 2008 | ICML | Topologically-constrained latent variable models. | Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence |
| 2007 | CIDM | Gaussian Process Latent Variable Models for Fault Detection. | Luka Eciolaza, Muhammad Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming |
| 2007 | ICML | Hierarchical Gaussian process latent variable models. | Neil D. Lawrence, Andrew J. Moore |
| 2007 | IJCAI | WiFi-SLAM Using Gaussian Process Latent Variable Models. | Brian Ferris, Dieter Fox, Neil D. Lawrence |
| 2007 | Interspeech | Model-driven detection of clean speech patches in noise. | Jonathan Laidler, Martin Cooke, Neil D. Lawrence |
| 2006 | ICML | Local distance preservation in the GP-LVM through back constraints. | Neil D. Lawrence, Joaquin Quionero Candela |
| 2005 | Interspeech | A hybrid Maxent/HMM based ASR system. | Yasser Hifny, Steve Renals, Neil D. Lawrence |
| 2004 | ICASSP | Acoustic space dimensionality selection and combination using the maximum entropy principle. | Yasser H. Abdel-Haleem, Steve Renals, Neil D. Lawrence |
| 2004 | ICML | Learning to learn with the informative vector machine. | Neil D. Lawrence, John C. Platt |
| 2003 | AISTATS | Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. | Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence |
| 2003 | CVPR | Variational Inference for Visual Tracking. | Jaco Vermaak, Neil D. Lawrence, Patrick Prez |
| 2001 | AISTATS | Variational Learning for Multi-Layer Networks of Linear Threshold Units. | Neil D. Lawrence |
| 2001 | HotOS | Probabilistic Modelling of Replica Divergence. | Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop |
| 2001 | ICML | Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. | Neil D. Lawrence, Bernhard Schlkopf |
| 1998 | UAI | Mixture Representations for Inference and Learning in Boltzmann Machines. | Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan |