3 papers
cs.LG2026
Knowledge Gradient for Preference Learning
Kaiwen Wu, Jacob R. Gardner
The knowledge gradient is a popular acquisition function in Bayesian optimization (BO) for optimizing black-box objectives with noisy function evaluations. Many practical settings,…
cs.LG2025
Mixed Likelihood Variational Gaussian Processes
Kaiwen Wu, Craig Sanders, Benjamin Letham +1
Gaussian processes (GPs) are powerful models for human-in-the-loop experiments due to their flexibility and well-calibrated uncertainty. However, GPs modeling human responses typic…
cs.LG2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…