<ul><li>Preferential Bayesian Optimization (PBO) is a method to learn latent user utilities from preferential feedback over designs.</li><li>PBO relies on a statistical surrogate model, usually a Gaussian process, and an acquisition strategy to select the next candidate pair.</li><li>A new approach called Preferential Amortized Black-Box Optimization (PABBO) fully amortizes PBO by meta-learning both the surrogate and acquisition function.</li><li>PABBO outperforms Gaussian process-based strategies in terms of both computational speed and accuracy on synthetic and real-world datasets.</li></ul>

PABBO: Preferential Amortized Black-Box Optimization

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