S. T. John

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 22

Top Topics

Gaussian Process

Top Venues

Publications

Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. ICML (2023)
Rui Li, S. T. John, Arno Solin
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. ICML (2023)
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, Richard E. Turner
Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR (2023)
Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Memory-Based Dual Gaussian Processes for Sequential Learning. CoRR (2023)
Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Memory-Based Dual Gaussian Processes for Sequential Learning. ICML (2023)
Julien Martinelli, Ayush Bharti, S. T. John, Armi Tiihonen, Sabina Sloman, Louis Filstroff, Samuel Kaski
Cost-aware learning of relevant contextual variables within Bayesian optimization. CoRR (2023)
Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen
Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions. CoRR (2022)
Alexander Nikitin, S. T. John, Arno Solin, Samuel Kaski
Non-separable Spatio-temporal Graph Kernels via SPDEs. CoRR (2021)
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John
GPflux: A Library for Deep Gaussian Processes. CoRR (2021)
Nuha Bintayyash, Sokratia Georgaka, S. T. John, Sumon Ahmed, Alexis Boukouvalas, James Hensman, Magnus Rattray
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinform. 37 (21) (2021)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objectives. CoRR (2020)
Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande
A Tutorial on Sparse Gaussian Processes and Variational Inference. CoRR (2020)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objective. UAI (2020)
Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR (2020)
Andrés F. López-Lopera, S. T. John, Nicolas Durrande
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. CoRR (2019)
Andrés F. López-Lopera, S. T. John, Nicolas Durrande
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. AISTATS (2019)
Mark van der Wilk, S. T. John, Artem Artemev, James Hensman
Variational Gaussian Process Models without Matrix Inverses. AABI (2019)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. NeurIPS (2018)
S. T. John, James Hensman
Large-Scale Cox Process Inference using Variational Fourier Features. CoRR (2018)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. CoRR (2018)
Vincent Adam, Nicolas Durrande, S. T. John
Scalable GAM using sparse variational Gaussian processes. CoRR (2018)
S. T. John, James Hensman
Large-Scale Cox Process Inference using Variational Fourier Features. ICML (2018)