Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure J. A. Lin, S. Ament, M. Balandat, E. Bakshy Bayesian Decision-making and Uncertainty Workshop at NeurIPS 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. A. Lin, S. Padhy, B. Mlodozeniec, J. Antorán, J. M. Hernández-Lobato Advances in Neural Information Processing Systems 2024
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
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
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)
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
Online Laplace Model Selection Revisited J. A. Lin, J. Antorán, J. M. Hernández-Lobato Advances in Approximate Bayesian Inference 2023 (Contributed Talk)
Function-Space Regularization for Deep Bayesian Classification J. A. Lin＊, J. Watson＊, P. Klink, J. Peters Advances in Approximate Bayesian Inference 2023
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
Neural Linear Models with Functional Gaussian Process Priors J. Watson＊, J. A. Lin＊, P. Klink, J. Peters Advances in Approximate Bayesian Inference 2020
