3 papers
cs.LG2025
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
Jiajun He, Yuanqi Du, Francisco Vargas +5
We consider the sampling problem, where the aim is to draw samples from a distribution whose density is known only up to a normalization constant. Recent breakthroughs in generativ…
cs.LG2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec +1
Gaussian processes are a versatile probabilistic machine learning model whose effectiveness often depends on good hyperparameters, which are typically learned by maximising the mar…
cs.LG2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec +2
Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear syst…