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ORCIDS. T. John
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2020 – today
- 2023
[c9]Paul Edmund Chang, Prakhar Verma
, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. ICML 2023: 4035-4054- [c8]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: 13050-13084 - [c7]Rui Li, S. T. John, Arno Solin:
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. ICML 2023: 19595-19615 - [i18]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 abs/2305.14120 (2023) - [i17]Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan:
Memory-Based Dual Gaussian Processes for Sequential Learning. CoRR abs/2306.03566 (2023) - [i16]Rui Li, S. T. John, Arno Solin:
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. CoRR abs/2306.04201 (2023) - [i15]Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen:
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. CoRR abs/2306.09656 (2023) - [i14]Daolang Huang, Manuel Haussmann, Ulpu Remes, S. T. John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi:
Practical Equivariances via Relational Conditional Neural Processes. CoRR abs/2306.10915 (2023) - [i13]Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. Gardner, S. T. John, Hong Ge, Richard E. Turner:
Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR abs/2307.03093 (2023) - 2022
- [c6]Alexander V. Nikitin, S. T. John, Arno Solin, Samuel Kaski:
Non-separable Spatio-temporal Graph Kernels via SPDEs. AISTATS 2022: 10640-10660 - [i12]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 abs/2209.04142 (2022) - [i11]Paul E. Chang, Prakhar Verma, S. T. John, Victor Picheny, Henry B. Moss, Arno Solin:
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning. CoRR abs/2211.01053 (2022) - [i10]Rui Li, S. T. John, Arno Solin:
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds. CoRR abs/2211.06260 (2022) - 2021
- [j1]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): 3788-3795 (2021) - [i9]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 abs/2104.05674 (2021) - [i8]Alexander V. Nikitin, S. T. John, Arno Solin, Samuel Kaski:
Non-separable Spatio-temporal Graph Kernels via SPDEs. CoRR abs/2111.08524 (2021) - 2020
- [c5]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objective. UAI 2020: 61-70 - [i7]Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman:
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR abs/2003.01115 (2020) - [i6]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objectives. CoRR abs/2003.04125 (2020) - [i5]Felix Leibfried, Vincent Dutordoir, S. T. John, Nicolas Durrande:
A Tutorial on Sparse Gaussian Processes and Variational Inference. CoRR abs/2012.13962 (2020)
2010 – 2019
- 2019
- [c4]Mark van der Wilk, S. T. John, Artem Artemev, James Hensman:
Variational Gaussian Process Models without Matrix Inverses. AABI 2019: 1-9 - [c3]Andrés F. López-Lopera, S. T. John, Nicolas Durrande:
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. AISTATS 2019: 1997-2006 - [i4]Andrés F. López-Lopera, S. T. John, Nicolas Durrande:
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints. CoRR abs/1902.10974 (2019) - 2018
- [c2]S. T. John, James Hensman:
Large-Scale Cox Process Inference using Variational Fourier Features. ICML 2018: 2367-2375 - [c1]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. NeurIPS 2018: 9960-9970 - [i3]S. T. John, James Hensman:
Large-Scale Cox Process Inference using Variational Fourier Features. CoRR abs/1804.01016 (2018) - [i2]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. CoRR abs/1808.05563 (2018) - [i1]Vincent Adam, Nicolas Durrande, S. T. John:
Scalable GAM using sparse variational Gaussian processes. CoRR abs/1812.11106 (2018)
Coauthor Index
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