Abstract Number:
3231
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Andrew Nguyen (1), Reese Pathak (2), Anastasios Angelopoulos (2), Stephen Bates (3), Michael Jordan (4)
Institutions:
(1) University Of California Berkeley, N/A, (2) University of California, Berkeley, N/A, (3) MIT, N/A, (4) Univ of California-Berkeley, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Decision-making pipelines involve trading-off risks with rewards.
It is often desirable to determine how much risk can be tolerated based on measured quantities, using collected data.
In this work, we address this problem, and allow decision-makers to control risks at a data-dependent level.
We demonstrate that, when applied without modification, state-of-the art uncertainty quantification methods can lead to gross violations on real problem instances when the levels are data-dependent.
As a remedy, we propose methods that permit the data analyst to claim valid control.
Our methodology, which is based on uniform tail bounds, supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions.
To illustrate the benefits of our approach, we carry out numerical experiments on synthetic data and the large-scale vision dataset MS-COCO.
Keywords:
Conformal prediction, uncertainty quantification, distribution-free, bootstrap, empirical process theory, simultaneous inference| | | | |
Sponsors:
IMS
Tracks:
Statistical Methodology
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