Large-Scale Cox Process Inference using Variational Fourier Features. - S. T. John; James Hensman (ICML, 2018)

Learning Invariances using the Marginal Likelihood. - Mark van der Wilk; Matthias Bauer 001; S. T. John; James Hensman (NeurIPS, 2018)

Scalable GAM using sparse variational Gaussian processes. - Vincent Adam; Nicolas Durrande; S. T. John (CoRR, 2018)

Variational Gaussian Process Models without Matrix Inverses. - Mark van der Wilk; S. T. John; Artem Artemev; James Hensman (AABI, 2019)

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. - Andrés F. López-Lopera; S. T. John; Nicolas Durrande (AISTATS, 2019)

Amortized variance reduction for doubly stochastic objective. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (UAI, 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 objectives. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (CoRR, 2020)

A Tutorial on Sparse Gaussian Processes and Variational Inference. - Felix Leibfried; Vincent Dutordoir; S. T. John; Nicolas Durrande (CoRR, 2020)

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)

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-separable Spatio-temporal Graph Kernels via SPDEs. - Alexander V. Nikitin; S. T. John; Arno Solin; Samuel Kaski (AISTATS, 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)

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)

Towards Improved Learning in Gaussian Processes: The Best of Two Worlds. - Rui Li; S. T. John; Arno Solin (CoRR, 2022)

Memory-Based Dual Gaussian Processes for Sequential Learning. - Paul Edmund Chang; Prakhar Verma; S. T. John; Arno Solin; Mohammad Emtiyaz Khan (ICML, 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 (ICML, 2023)

Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. - Rui Li; S. T. John; Arno Solin (ICML, 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)

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)

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)

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)

Learning relevant contextual variables within Bayesian optimization. - Julien Martinelli; Ayush Bharti; Armi Tiihonen; S. T. John; Louis Filstroff; Sabina J. Sloman; Patrick Rinke; Samuel Kaski (UAI, 2024)

Identifying latent state transitions in non-linear dynamical systems. - Çaglar Hizli; Çagatay Yildiz; Matthias Bethge; S. T. John; Pekka Marttinen (ICLR, 2025)