← Authors

Neil D. Lawrence

29 papers · 2006–2022 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (11) πŸ—ΊοΈ Taxonomy Completionist (19) πŸƒ Academic Marathon (16) 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) 🌟 Keyword Trendsetter Combo (6) πŸ”¬ Deep Specialist (14) πŸ† Keyword Champion (5) πŸ’Ž Century Club (29) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (5) πŸ—ƒοΈ Keyword Collector (76)

Conferences

AISTATS (9) NIPS (9) JMLR (6) ICML (3) CVPR (1) ICLR (1)

Top co-authors

Andreas Damianou (6) James Hensman (4) Max Zwiessele (3) Zhenwen Dai (3) Michalis K. Titsias (3) Magnus Rattray (3) Mauricio A. Álvarez (3) Mauricio Alvarez (3) Javier Gonzalez (2) Michalis Titsias (2)

Keywords

gaussian process (19) variational inference (9) latent variable model (5) dimensionality reduction (5) bayesian inference (5) gaussian processes (5) graphical model (3) dynamical system (3) marginal likelihood (3) convolution process (3) approximate inference (3) sparse approximation (3) latent function (3) transfer learning (2) probabilistic model (2) manifold learning (2) graphical lasso (2) bayesian optimization (2) differential privacy (2) gaussian process prior (2)

Papers

Two-way Sparse Network Inference for Count Data AISTATS 2022
Generalised GPLVM with Stochastic Variational Inference AISTATS 2022
Modeling the Machine Learning Multiverse NIPS 2022
Differentially Private Regression and Classification with Sparse Gaussian Processes JMLR 2021
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis JMLR 2021
Transferring Knowledge across Learning Processes ICLR 2019
Variational Information Distillation for Knowledge Transfer CVPR 2019
Differentially Private Regression with Gaussian Processes AISTATS 2018
Structured Variationally Auto-encoded Optimization ICML 2018
Preferential Bayesian Optimization ICML 2017
Chained Gaussian Processes AISTATS 2016
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes JMLR 2016
Hybrid Discriminative-Generative Approach with Gaussian Processes AISTATS 2014
Tilted Variational Bayes AISTATS 2014
Deep Gaussian Processes AISTATS 2013
The Bigraphical Lasso ICML 2013
Fast Variational Inference in the Conjugate Exponential Family NIPS 2012
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models JMLR 2012
Computationally Efficient Convolved Multiple Output Gaussian Processes JMLR 2011
Variational Gaussian Process Dynamical Systems NIPS 2011
Efficient inference in matrix-variate Gaussian models with \iid observation noise NIPS 2011
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels AISTATS 2010
Switched Latent Force Models for Movement Segmentation NIPS 2010
Bayesian Gaussian Process Latent Variable Model AISTATS 2010
Efficient Sampling for Gaussian Process Inference using Control Variables NIPS 2008
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes NIPS 2008
Sparse Convolved Gaussian Processes for Multi-output Regression NIPS 2008
Modelling transcriptional regulation using Gaussian Processes NIPS 2006
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis JMLR 2006