ST John
ST John
Finnish Center for Artificial Intelligence FCAI, Aalto University
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Cited by
Cited by
Many-body coarse-grained interactions using Gaussian approximation potentials
ST John, G Csányi
The Journal of Physical Chemistry B 121 (48), 10934-10949, 2017
A framework for interdomain and multioutput Gaussian processes
M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman
arXiv preprint arXiv:2003.01115, 2020
Learning invariances using the marginal likelihood
M van der Wilk, M Bauer, ST John, J Hensman
Proceedings of the 32nd International Conference on Neural Information …, 2018
A tutorial on sparse Gaussian processes and variational inference
F Leibfried, V Dutordoir, ST John, N Durrande
arXiv preprint arXiv:2012.13962, 2020
Large-scale Cox process inference using variational Fourier features
ST John, J Hensman
International Conference on Machine Learning, 2362-2370, 2018
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, 2021
GPflux: A Library for Deep Gaussian Processes
V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...
PROBPROG2021, arXiv:2104.05674, 2021
Machine learning system
A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDEC FLORES, ...
US Patent App. 16/753,580, 2020
Spectroscopic method to measure the superfluid fraction of an ultracold atomic gas
ST John, Z Hadzibabic, NR Cooper
Physical Review A 83 (2), 023610, 2011
Gaussian process modulated Cox processes under linear inequality constraints
AF López-Lopera, ST John, N Durrande
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Non-separable spatio-temporal graph kernels via SPDEs
AV Nikitin, ST John, A Solin, S Kaski
International Conference on Artificial Intelligence and Statistics, 10640-10660, 2022
Variational Gaussian process models without matrix inverses
M van der Wilk, ST John, A Artemev, J Hensman
Symposium on Advances in Approximate Bayesian Inference, 1-9, 2020
Fantasizing with dual GPs in Bayesian optimization and active learning
PE Chang, P Verma, ST John, V Picheny, H Moss, A Solin
arXiv preprint arXiv:2211.01053, 2022
Amortized variance reduction for doubly stochastic objective
A Boustati, S Vakili, J Hensman, ST John
Conference on Uncertainty in Artificial Intelligence, 61-70, 2020
Scalable GAM using sparse variational Gaussian processes
V Adam, N Durrande, ST John
arXiv preprint arXiv:1812.11106, 2018
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 …, 2023
Memory-Based Dual Gaussian Processes for Sequential Learning
PE Chang, P Verma, ST John, A Solin, ME Khan
International Conference on Machine Learning, 4035-4054, 2023
Improving hyperparameter learning under approximate inference in Gaussian process models
R Li, ST John, A Solin
arXiv preprint arXiv:2306.04201, 2023
Targeted Causal Elicitation
N Ibrahim, ST John, Z Guo, S Kaski
NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions
Ç Hızlı, ST John, A Juuti, T Saarinen, K Pietiläinen, P Marttinen
arXiv preprint arXiv:2209.04142, 2022
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