S. T. John

According to our database, S. T. John authored at least 22 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Beyond Intuition, a Framework for Applying GPs to Real-World Data.

Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. Gardner, S. T. John, Hong Ge, Richard E. Turner

CoRR, 2023

Practical Equivariances via Relational Conditional Neural Processes.

Daolang Huang, Manuel Haussmann, Ulpu Remes, S. T. John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi

CoRR, 2023

Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions.

Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen

CoRR, 2023

Cost-aware learning of relevant contextual variables within Bayesian optimization.

Julien Martinelli, Ayush Bharti, S. T. John, Armi Tiihonen, Sabina Sloman, Louis Filstroff, Samuel Kaski

CoRR, 2023

Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models.

Rui Li, S. T. John, Arno Solin

Proceedings of the International Conference on Machine Learning, 2023

Causal Modeling of Policy Interventions From Treatment-Outcome Sequences.

Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen

Proceedings of the International Conference on Machine Learning, 2023

Memory-Based Dual Gaussian Processes for Sequential Learning.

Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan

Proceedings of the International Conference on Machine Learning, 2023

2022

Towards Improved Learning in Gaussian Processes: The Best of Two Worlds.

Rui Li, S. T. John, Arno Solin

CoRR, 2022

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning.

Paul E. Chang, Prakhar Verma, S. T. John, Victor Picheny, Henry B. Moss, Arno Solin

CoRR, 2022

Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions.

Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen

CoRR, 2022

Non-separable Spatio-temporal Graph Kernels via SPDEs.

Alexander V. Nikitin, S. T. John, Arno Solin, Samuel Kaski

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

GPflux: A Library for Deep Gaussian Processes.

Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John

CoRR, 2021

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.

Nuha Bintayyash, Sokratia Georgaka, S. T. John, Sumon Ahmed, Alexis Boukouvalas, James Hensman, Magnus Rattray

Bioinform., 2021

2020

A Tutorial on Sparse Gaussian Processes and Variational Inference.

Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande

CoRR, 2020

Amortized variance reduction for doubly stochastic objectives.

Ayman Boustati, Sattar Vakili, James Hensman, S. T. John

CoRR, 2020

A Framework for Interdomain and Multioutput Gaussian Processes.

Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman

CoRR, 2020

Amortized variance reduction for doubly stochastic objective.

Ayman Boustati, Sattar Vakili, James Hensman, S. T. John

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints.

Andrés F. López-Lopera, S. T. John, Nicolas Durrande

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Variational Gaussian Process Models without Matrix Inverses.

Mark van der Wilk, S. T. John, Artem Artemev, James Hensman

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Scalable GAM using sparse variational Gaussian processes.

Vincent Adam, Nicolas Durrande, S. T. John

CoRR, 2018

Learning Invariances using the Marginal Likelihood.

Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Large-Scale Cox Process Inference using Variational Fourier Features.

S. T. John, James Hensman

Proceedings of the 35th International Conference on Machine Learning, 2018