The AI Policy Frontline: Driving Evidence-Based Solutions, gathers leading researchers, policymakers, government officials, and industry experts to address some of the most critical challenges and opportunities at the intersection of Artificial Intelligence and public policy today.
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Thomas Garbay
Alumni
Publications
Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization
Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different models to address each part, mak… (see more)ing them well-suited for problems with inherent hierarchical structure. However, typical H-GP models do not fully take advantage of this structure, only sending information up or down the hierarchy. This one-way coupling limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), an efficient H-GP framework that establishes bidirectional information exchange between parent and child models in H-GPs for online training. BIF retains the modular structure of hierarchical models - the parent combines subtask knowledge from children GPs - while introducing top-down feedback to continually refine children models during online learning. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. BIF outperforms conventional H-GP Bayesian Optimization methods, achieving up to 85% and 5x higher