Neil D. Lawrence
31 papers · 1999–2022 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (50) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
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Topic Pioneer
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Keyword Trendsetter Combo
(5)
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Deep Specialist
(17)
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Keyword Champion
(5)
π§
Keyword Pioneer
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Conference Pioneer
π₯
Unstoppable Β· Bronze
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Century Club Β· Bronze
ποΈ
Keyword Collector
(140)
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Renaissance Researcher
(6)
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Trend Setter
Top co-authors
Research topics
Machine Learning
(29)
Artificial Intelligence
(20)
Optimization & Theory
(20)
Bayesian & Probabilistic Β· Machine Learning
(18)
Bayesian & Probabilistic Β· Artificial Intelligence
(17)
Core Methods
(17)
Deep Learning
(7)
Learning Types
(7)
Models
(5)
Healthcare & Medicine
(3)
Mathematics & Optimization
(3)
Application Areas
(3)
Learning Paradigms
(3)
Research
(3)
Data Science & Analytics
(2)
Keywords
gaussian process
(19)
variational inference
(9)
gaussian processes
(5)
dimensionality reduction
(5)
latent variable model
(5)
bayesian inference
(5)
manifold learning
(3)
graphical model
(3)
approximate inference
(3)
convolution process
(3)
graphical lasso
(3)
dynamical system
(3)
transfer learning
(3)
sparse approximation
(3)
gaussian process latent variable model
(3)
marginal likelihood
(3)
latent function
(3)
probabilistic model
(2)
gaussian process prior
(2)
differential privacy
(2)
Papers
Generalised GPLVM with Stochastic Variational Inference
AISTATS 2022
Modeling the Machine Learning Multiverse
NeurIPS 2022
Two-way Sparse Network Inference for Count Data
AISTATS 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
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
JMLR 2012
Fast Variational Inference in the Conjugate Exponential Family
NeurIPS 2012
Computationally Efficient Convolved Multiple Output Gaussian Processes
JMLR 2011
Efficient inference in matrix-variate Gaussian models with \iid observation noise
NeurIPS 2011
Variational Gaussian Process Dynamical Systems
NeurIPS 2011
Bayesian Gaussian Process Latent Variable Model
AISTATS 2010
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
AISTATS 2010
Switched Latent Force Models for Movement Segmentation
NeurIPS 2010
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
NeurIPS 2008
Efficient Sampling for Gaussian Process Inference using Control Variables
NeurIPS 2008
Sparse Convolved Gaussian Processes for Multi-output Regression
NeurIPS 2008
Modelling transcriptional regulation using Gaussian Processes
NeurIPS 2006
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
JMLR 2006
Latent Force Models
AISTATS 2005
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
AISTATS 1999