Lawrence, Neil D.

64 publications

ICLRW 2025 The Human Visual System Can Inspire New Interaction Paradigms for LLMs Diana Robinson, Neil D Lawrence

NeurIPSW 2024 Enhancing Patient Stratification and Interpretability Through Class-Contrastive and Feature Attribution Techniques Sharday Olowu, Neil D Lawrence, Soumya Banerjee

ICLRW 2024 Scalable Amortized GPLVMs for Single Cell Transcriptomics Data Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D Lawrence

ICMLW 2023 Dimensionality Reduction as Probabilistic Inference Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D Lawrence

AISTATS 2022 Generalised GPLVM with Stochastic Variational Inference Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence

AISTATS 2022 Two-Way Sparse Network Inference for Count Data Sijia Li, Martı́n López-Garcı́a, Neil D. Lawrence, Luisa Cutillo

NeurIPS 2022 Modeling the Machine Learning Multiverse Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence

JMLR 2021 Differentially Private Regression and Classification with Sparse Gaussian Processes Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence

JMLR 2021 Multi-View Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

ICLR 2020 Empirical Bayes Transductive Meta-Learning with Synthetic Gradients Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou

ICLR 2019 Transferring Knowledge Across Learning Processes Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou

AISTATS 2018 Differentially Private Regression with Gaussian Processes Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence

ICML 2018 Structured Variationally Auto-Encoded Optimization Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence

ICML 2017 Preferential Bayesian Optimization Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

AISTATS 2016 Batch Bayesian Optimization via Local Penalization Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

AISTATS 2016 Chained Gaussian Processes Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

AISTATS 2016 GLASSES: Relieving the Myopia of Bayesian Optimisation Javier González, Michael A. Osborne, Neil D. Lawrence

ICLR 2016 Recurrent Gaussian Processes César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

ICLR 2016 Variational Auto-Encoded Deep Gaussian Processes Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence

JMLR 2016 Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

UAI 2015 Semi-Described and Semi-Supervised Learning with Gaussian Processes Andreas C. Damianou, Neil D. Lawrence

AISTATS 2014 Hybrid Discriminative-Generative Approach with Gaussian Processes Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence

UAI 2014 Metrics for Probabilistic Geometries Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence

AISTATS 2014 Tilted Variational Bayes James Hensman, Max Zwiessele, Neil D. Lawrence

AISTATS 2013 Deep Gaussian Processes Andreas C. Damianou, Neil D. Lawrence

UAI 2013 Gaussian Processes for Big Data James Hensman, Nicoló Fusi, Neil D. Lawrence

ICML 2013 The Bigraphical Lasso Alfredo Kalaitzis, John Lafferty, Neil D. Lawrence, Shuheng Zhou

JMLR 2012 A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models Neil D. Lawrence

NeurIPS 2012 Fast Variational Inference in the Conjugate Exponential Family James Hensman, Magnus Rattray, Neil D. Lawrence

FnTML 2012 Kernels for Vector-Valued Functions: A Review Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence

ICML 2012 Manifold Relevance Determination Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence

AISTATS 2012 Preface Neil D. Lawrence, Mark Girolami

ICML 2012 Residual Components Analysis Alfredo A. Kalaitzis, Neil D. Lawrence

JMLR 2011 Computationally Efficient Convolved Multiple Output Gaussian Processes Mauricio A. Álvarez, Neil D. Lawrence

NeurIPS 2011 Efficient Inference in Matrix-Variate Gaussian Models with \iid Observation Noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten Borgwardt

NeurIPS 2011 Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence

AISTATS 2010 Bayesian Gaussian Process Latent Variable Model Michalis Titsias, Neil D. Lawrence

AISTATS 2010 Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels Mauricio Álvarez, David Luengo, Michalis Titsias, Neil D. Lawrence

NeurIPS 2010 Switched Latent Force Models for Movement Segmentation Mauricio Alvarez, Jan R. Peters, Neil D. Lawrence, Bernhard Schölkopf

AISTATS 2009 Latent Force Models Mauricio Álvarez, David Luengo, Neil D. Lawrence

ICML 2009 Non-Linear Matrix Factorization with Gaussian Processes Neil D. Lawrence, Raquel Urtasun

NeurIPS 2008 Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes Ben Calderhead, Mark Girolami, Neil D. Lawrence

NeurIPS 2008 Efficient Sampling for Gaussian Process Inference Using Control Variables Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias

NeurIPS 2008 Sparse Convolved Gaussian Processes for Multi-Output Regression Mauricio Alvarez, Neil D. Lawrence

ICML 2008 Topologically-Constrained Latent Variable Models Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence

ICML 2007 Hierarchical Gaussian Process Latent Variable Models Neil D. Lawrence, Andrew J. Moore

AISTATS 2007 Learning for Larger Datasets with the Gaussian Process Latent Variable Model Neil D. Lawrence

IJCAI 2007 WiFi-SLAM Using Gaussian Process Latent Variable Models Brian Ferris, Dieter Fox, Neil D. Lawrence

ECML-PKDD 2006 Fast Variational Inference for Gaussian Process Models Through KL-Correction Nathaniel John King, Neil D. Lawrence

ICML 2006 Local Distance Preservation in the GP-LVM Through Back Constraints Neil D. Lawrence, Joaquin Quiñonero Candela

ECML-PKDD 2006 Missing Data in Kernel PCA Guido Sanguinetti, Neil D. Lawrence

NeurIPS 2006 Modelling Transcriptional Regulation Using Gaussian Processes Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray

JMLR 2006 Optimising Kernel Parameters and Regularisation Coefficients for Non-Linear Discriminant Analysis Tonatiuh Peña Centeno, Neil D. Lawrence

ICML 2004 Learning to Learn with the Informative Vector Machine Neil D. Lawrence, John C. Platt

NeurIPS 2004 Semi-Supervised Learning via Gaussian Processes Neil D. Lawrence, Michael I. Jordan

AISTATS 2003 Fast Forward Selection to Speed up Sparse Gaussian Process Regression Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence

NeurIPS 2003 Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data Neil D. Lawrence

CVPR 2003 Variational Inference for Visual Tracking Jaco Vermaak, Neil D. Lawrence, Patrick Pérez

NeurIPS 2002 Fast Sparse Gaussian Process Methods: The Informative Vector Machine Neil D. Lawrence, Matthias Seeger, Ralf Herbrich

ICML 2001 Estimating a Kernel Fisher Discriminant in the Presence of Label Noise Neil D. Lawrence, Bernhard Schölkopf

NeurIPS 2001 Optimising Synchronisation Times for Mobile Devices Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, Michael J. Taylor

AISTATS 2001 Variational Learning for Multi-Layer Networks of Linear Threshold Units Neil D. Lawrence

UAI 1998 Mixture Representations for Inference and Learning in Boltzmann Machines Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan

NeurIPS 1997 Approximating Posterior Distributions in Belief Networks Using Mixtures Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan