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Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
Zhen Lin · Shubhendu Trivedi · Cao Xiao · Jimeng Sun

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #130

Many real-world multi-label prediction problems involve set-valued predictions that must satisfy specific requirements dictated by downstream usage. We focus on a typical scenario where such requirements, separately encoding value and cost, compete with each other. For instance, a hospital might expect a smart diagnosis system to capture as many severe, often co-morbid, diseases as possible (the value), while maintaining strict control over incorrect predictions (the cost). We present a general pipeline, dubbed as FavMac, to maximize the value while controlling the cost in such scenarios. FavMac can be combined with almost any multi-label classifier, affording distribution-free theoretical guarantees on cost control. Moreover, unlike prior works, FavMac can handle real-world large-scale applications via a carefully designed online update mechanism, which is of independent interest. Our methodological and theoretical contributions are supported by experiments on several healthcare tasks and synthetic datasets - FavMac furnishes higher value compared with several variants and baselines while maintaining strict cost control.

Author Information

Zhen Lin (University of Illinois at Urbana-Champaign)
Shubhendu Trivedi (Massachusetts Institute of Technology)
Cao Xiao (Relativity)
Jimeng Sun (University of Illinois at Urbana - Champaign)

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