Publications

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
J. A. Lin, S. Padhy, B. Mlodozeniec, J. Antorán, J. M. Hernández-Lobato
Preprint

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Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
J. A. Lin, S. Padhy, B. Mlodozeniec, J. M. Hernández-Lobato
Advances in Approximate Bayesian Inference 2024

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Stochastic Gradient Descent for Gaussian Processes Done Right
J. A. Lin^＊, S. Padhy^＊, J. Antorán^＊, A. Tripp, A. Terenin, C. Szepesvári, J. M. Hernández-Lobato, D. Janz
International Conference on Learning Representations 2024

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Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
J. A. Lin^＊, J. Antorán^＊, S. Padhy^＊, D. Janz, J. M. Hernández-Lobato, A. Terenin
Advances in Neural Information Processing Systems 2023 (Oral Presentation)

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Minimal Random Code Learning with Mean-KL Parameterization
J. A. Lin, G. Flamich, J. M. Hernández-Lobato
Neural Compression Workshop at ICML 2023

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Beyond Intuition, a Framework for Applying GPs to Real-World Data
K. Tazi, J. A. Lin, R. Viljoen, A. Gardner, T. John, H. Ge, R. E. Turner
Structured Probabilistic Inference & Generative Modeling Workshop at ICML 2023

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Online Laplace Model Selection Revisited
J. A. Lin, J. Antorán, J. M. Hernández-Lobato
Advances in Approximate Bayesian Inference 2023 (Contributed Talk)

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Function-Space Regularization for Deep Bayesian Classification
J. A. Lin^＊, J. Watson^＊, P. Klink, J. Peters
Advances in Approximate Bayesian Inference 2023

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Latent Derivative Bayesian Last Layer Networks
J. Watson^＊, J. A. Lin^＊, P. Klink, J. Pajarinen, J. Peters
International Conference on Artificial Intelligence and Statistics 2021

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Neural Linear Models with Functional Gaussian Process Priors
J. Watson^＊, J. A. Lin^＊, P. Klink, J. Peters
Advances in Approximate Bayesian Inference 2020

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