| Latent Derivative Bayesian Last Layer Networks J Watson*, JA Lin*, P Klink, J Pajarinen, J Peters International Conference on Artificial Intelligence and Statistics, 2021 | 27 | 2021 |
| Neural Linear Models with Functional Gaussian Process Priors J Watson*, JA Lin*, P Klink, J Peters Advances in Approximate Bayesian Inference, 2020 | 10 | 2020 |
| 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 | 9 | 2023 |
| 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 | 2 | 2023 |
| Online Laplace Model Selection Revisited JA Lin, J Antorán, JM Hernández-Lobato Advances in Approximate Bayesian Inference, 2023 | 2 | 2023 |
| 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 | | 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 |
Pascal KlinkAmazon RoboticsVerified email at amazon.de
