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FOLDS Seminar: ACS: An interactive framework for machine-assisted selection with model-free guarantees
November 6 at 12:00 PM - 1:00 PM
Zoom link: https://upenn.zoom.us/j/98220304722
In this talk, I will introduce 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.
The talk is based on https://arxiv.org/pdf/2507.15825.
Zhimei Ren
Assistant professor in the Department of Statistics and Data Science at the Wharton School
Zhimei Ren is an assistant professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. From 2021-2023, she was a postdoctoral researcher in the Statistics Department at the University of Chicago, advised by Professor Rina Foygel Barber. She obtained her Ph.D. in Statistics from Stanford University, advised by Professor Emmanuel Candès. Her research interests lie broadly in multiple hypothesis testing, distribution-free inference, causal inference, and data-driven decision-making.