Effective Design of Sequences with Limited Data

What is it about?

Experimental or labeled data plays a crucial role in training AI models. However, in fields like biology, obtaining labeled data can be particularly challenging due to the high cost and time required. To address this issue, this paper presents a method that leverages large amounts of unlabeled data available in the wild to enhance model robustness and generate new data or samples.

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Why is it important?

The volume of labeled data is significantly smaller than that of unlabeled data, primarily because labeling demands human effort. To address this gap, this paper proposes a theoretically validated framework designed to effectively utilize vast unlabeled resources for black-box optimization tasks. We further demonstrate its practicality and effectiveness through experiments conducted across various domains, including robotics and biology

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