Our latest contribution to Bayesian optimization was presented by Taiwo Adebiyi at the Thirteenth International Conference on Learning Representations (ICLR 2025) in Singapore. As one of the premier conferences in deep learning, ICLR provided a globally recognized platform for the UQ Lab to showcase its research.
Our paper, Optimizing Posterior Samples for Bayesian Optimization by Rootfinding, authored by Taiwo Adebiyi, Dr. Bach Do, and Dr. Ruda Zhang, introduces a highly efficient global optimization algorithm—TS-roots—for Gaussian Process Thompson Sampling (TS) in Bayesian optimization. This is the first method to achieve exact TS, unlocking the full potential of TS’s theoretical advantages for significantly improved Bayesian optimization performance.
TS-roots stands out for its linear scalability with input dimensionality, substantial improvements in inner-loop optimization and competitive outer-loop performance, outperforming several well-known methods including GP-UCB and LogEI. With an average reviewer score of 7.0 placing it in the top 8% of all ICLR 2025 submissions, our paper received considerable attention at the conference.
Building on our strong presence at the NeurIPS 2024 BDU Workshop in Vancouver, ICLR 2025 provided an excellent platform to present the full development of TS-roots, including key algorithmic refinements and new experimental results. To learn more, you can check out the paper, presentation, and GitHub package.