3 papers
cs.LG2025
Graph Random Features for Scalable Gaussian Processes
Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski +3
We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input space…
cs.LG2025
Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle +3
Successive Halving is a popular algorithm for hyperparameter optimization which allocates exponentially more resources to promising candidates. However, the algorithm typically rel…
cs.LG2025
Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
Jihao Andreas Lin
Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale g…