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Jihao Andreas Lin
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Latent Derivative Bayesian Last Layer Networks
J Watson*, JA Lin*, P Klink, J Pajarinen, J Peters
International Conference on Artificial Intelligence and Statistics, 2021
282021
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin
Advances in Neural Information Processing Systems, 2023
112023
Neural Linear Models with Functional Gaussian Process Priors
J Watson*, JA Lin*, P Klink, J Peters
Advances in Approximate Bayesian Inference, 2020
102020
Beyond Intuition, a Framework for Applying GPs to Real-World Data
K Tazi, JA Lin, R Viljoen, A Gardner, T John, H Ge, RE Turner
ICML Structured Probabilistic Inference & Generative Modeling Workshop, 2023
32023
Online Laplace Model Selection Revisited
JA Lin, J Antorán, JM Hernández-Lobato
Advances in Approximate Bayesian Inference, 2023
32023
Stochastic Gradient Descent for Gaussian Processes Done Right
JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ...
International Conference on Learning Representations, 2024
22024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
JA Lin, S Padhy, B Mlodozeniec, J Antorán, JM Hernández-Lobato
arXiv preprint arXiv:2405.18457, 2024
2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
JA Lin, S Padhy, B Mlodozeniec, JM Hernández-Lobato
Advances in Approximate Bayesian Inference, 2024
2024
Towards more interpretable and robust geospatial modelling with Gaussian Processes
K Tazi, JA Lin, AS Gardner, ST John, H Ge, RE Turner
AGU23, 2023
2023
Minimal Random Code Learning with Mean-KL Parameterization
JA Lin, G Flamich, JM Hernández-Lobato
ICML Neural Compression Workshop, 2023
2023
Function-Space Regularization for Deep Bayesian Classification
JA Lin*, J Watson*, P Klink, J Peters
Advances in Approximate Bayesian Inference, 2023
2023
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Articles 1–11