> For the complete documentation index, see [llms.txt](https://lichangbin.gitbook.io/paper_notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lichangbin.gitbook.io/paper_notes/oct/meta-learning-acquisition-functions-for-transfer-learning-in-bayesian-optimization.md). # Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization ## Motivation NOTE: read this paper again in details. It looks a novel direction **Transferring knowledge across tasks to improve data-efficiency** is one of the open key challenges in the field of **global black-box optimization**. (how about not black box?) limitation of current method: Readily available algorithms are typically designed to be universal optimizers and, therefore, often **suboptimal for specific tasks**. **Method in the paper:** We propose **a novel transfer learning method to obtain customized optimizers** within the well-established framework of **Bayesian optimization**, allowing our algorithm to utilize the proven generalization capabilities of Gaussian processes surrogate model. **Using reinforcement learning to meta-train an acquisition function (AF)** on a set of related tasks, the proposed method learns to **extract implicit structural information** and to exploit it for improved data-efficiency. ## Goal : Global Black-Box Optimization problem ![](/files/-MKaYQ9gO2Xre24ZAR9E) Usually Bayesian Optimization (BO) is used for the problem * Probabilistic surrogate model (e.g., GP) to interpolate between data points * Sampling strategy (acquisition function, AF) based on surrogate model Transfer learning is used to increase the data-efficiency by transferring knowledge across task instances ## MetaBO Retain the proven structure of BO, keep the powerful GP surrogate model ![](/files/-MKaZP15KwjD4CSi60L4) Replace the AF part with neural AF to obtain task-specific AFs by transfer learning ![](/files/-MKaZeuWlzmP3mxV6Mq-) Train neural AFs using RL: so no need for gradients of $$f\in \mathcal{F}$$ ## Reference * * * *