| 2025 | ICLR | Identifying latent state transitions in non-linear dynamical systems. | aglar Hizli, agatay Yildiz, Matthias Bethge, S. T. John, Pekka Marttinen |
| 2024 | UAI | 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 |
| 2023 | ICML | Memory-Based Dual Gaussian Processes for Sequential Learning. | Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan |
| 2023 | ICML | Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. | Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietilinen, Pekka Marttinen |
| 2023 | ICML | Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. | Rui Li, S. T. John, Arno Solin |
| 2022 | AISTATS | Non-separable Spatio-temporal Graph Kernels via SPDEs. | Alexander Nikitin, S. T. John, Arno Solin, Samuel Kaski |
| 2020 | UAI | Amortized variance reduction for doubly stochastic objective. | Ayman Boustati, Sattar Vakili, James Hensman, S. T. John |
| 2019 | AISTATS | Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. | Andrs F. Lpez-Lopera, S. T. John, Nicolas Durrande |
| 2018 | ICML | Large-Scale Cox Process Inference using Variational Fourier Features. | S. T. John, James Hensman |