Author

J St John

Finnish Center for Artificial Intelligence FCAI, Aalto University - Cited by 438 - machine learning - probabilistic modelling - Bayesian inference - Gaussian processes

Biography

  J St John is affiliated to Brent Integrated Diabetes Service, London North west Healthcare NHS Trust, UK. He is a recipient of many awards and grants for his valuable contributions and discoveries in major area of subject research. His international experience includes various programs, contributions and participation in different countries for diverse fields of study.  His research interests reflect in his wide range of publications in various national and international journals. 
Title
Cited by
Year
Many-body coarse-grained interactions using Gaussian approximation potentials
ST John, G CsányiThe Journal of Physical Chemistry B 121 (48), 10934-10949, 2017201
102
2017
A framework for interdomain and multioutput Gaussian processes
M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J HensmanarXiv preprint arXiv:2003.01115, 2020202
79
2020
Learning invariances using the marginal likelihood
M van der Wilk, M Bauer, ST John, J HensmanProceedings of the 32nd International Conference on Neural Information …, 2018201
73
2018
Large-scale Cox process inference using variational Fourier features
ST John, J HensmanInternational Conference on Machine Learning, 2362-2370, 2018201
31
2018
A tutorial on sparse Gaussian processes and variational inference
F Leibfried, V Dutordoir, ST John, N DurrandearXiv preprint arXiv:2012.13962, 2020202
29
2020
Spectroscopic method to measure the superfluid fraction of an ultracold atomic gas
ST John, Z Hadzibabic, NR CooperPhysical Review A 83 (2), 023610, 2011201
21
2011
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments
N BinTayyash, S Georgaka, ST John, S Ahmed, A Boukouvalas, ...Bioinformatics 37 (21), 3788-3795, 212
20
2021
Machine learning system
A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDEC FLORES, ...US Patent App. 16/753,580,
20
2020
GPflux: A Library for Deep Gaussian Processes
V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...PROBPROG2021, arXiv:2104.05674, 2021202
19
2021
Gaussian process modulated Cox processes under linear inequality constraints
AF López-Lopera, ST John, N DurrandeThe 22nd International Conference on Artificial Intelligence and Statistics …, 2019201
16
2019
Non-separable spatio-temporal graph kernels via SPDEs
AV Nikitin, ST John, A Solin, S KaskiInternational Conference on Artificial Intelligence and Statistics, 10640-10660, 2022202
9
2022
Variational Gaussian process models without matrix inverses
M van der Wilk, ST John, A Artemev, J HensmanSymposium on Advances in Approximate Bayesian Inference, 1-9, 2020202
7
2020
Fantasizing with dual GPs in Bayesian optimization and active learning
PE Chang, P Verma, ST John, V Picheny, H Moss, A SolinarXiv preprint arXiv:2211.01053, 2022202
4
2022
Scalable GAM using sparse variational Gaussian processes
V Adam, N Durrande, ST JohnarXiv preprint arXiv:1812.11106, 2018201
3
2018
Amortized variance reduction for doubly stochastic objective
A Boustati, S Vakili, J Hensman, ST JohnConference on Uncertainty in Artificial Intelligence, 61-70, 000
2
2020
Queer In AI: A Case Study in Community-Led Participatory AI
OO Queerinai, A Ovalle, A Subramonian, A Singh, C Voelcker, ...Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023202
1
2023
Targeted Causal Elicitation
N Ibrahim, ST John, Z Guo, S KaskiNeurIPS 2022 Workshop on Causality for Real-world Impact, 2022202
1
2022
Improving hyperparameter learning under approximate inference in Gaussian process models
R Li, ST John, A SolinarXiv preprint arXiv:2306.0420, 2023202
1
2023

Referred From: https://scholar.google.com/citations?user=Jf9j8GAAAAAJ&hl=en

Cited by
Citations 438
h-index 10
i10-index 10
Open Access Journals