ACS: An interactive framework for conformal selection
This work addresses the need for rigorous error control in adaptive data analysis for researchers and practitioners, representing an incremental advancement over existing conformal selection methods.
The paper tackles the problem of model-free selection with guaranteed error control by introducing adaptive conformal selection (ACS), an interactive framework that supports human-in-the-loop adaptive data analysis, and demonstrates its effectiveness through simulations and real-data applications in LLM deployment and drug discovery.
This paper presents adaptive conformal selection (ACS), an interactive framework for model-free selection with guaranteed error control. Building on conformal selection (Jin and Candès, 2023b), ACS generalizes the approach to support human-in-the-loop adaptive data analysis. Under the ACS framework, we can partially reuse the data to boost the selection power, make decisions on the fly while exploring the data, and incorporate new information or preferences as they arise. The key to ACS is a carefully designed principle that controls the information available for decision making, allowing the data analyst to explore the data adaptively while maintaining rigorous control of the false discovery rate (FDR). Based on the ACS framework, we provide concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating newly available labeled data. The effectiveness of ACS is demonstrated through extensive numerical simulations and real-data applications in large language model (LLM) deployment and drug discovery.