47 citations · 111 across the 25 of their papers we have counts for
4 papers · 1 filter
Informed Initialization for Bayesian Optimization and Active Learning
Carl Hvarfner, David Eriksson, Eytan Bakshy +1
Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the…
LILO: Bayesian Optimization with Natural Language Feedback
Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham +3
Many real-world optimization problems are guided by complex, subjective preferences that are difficult to express as explicit closed-form objectives. In response, we introduce Lang…
Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
Qing Feng, Samuel Daulton, Benjamin Letham +2
Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and lea…
Scalable Gaussian Processes with Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +3
Applying Gaussian processes (GPs) to very large datasets remains a challenge due to limited computational scalability. Matrix structures, such as the Kronecker product, can acceler…