The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
On the utility of prediction sets in human-ai teams
3 Pith papers cite this work. Polarity classification is still indexing.
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MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
PLACO is a multi-stage framework that extends Bayesian combination of human and model labels to achieve cost-effective high performance in human-AI teams.
citing papers explorer
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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Medical Model Synthesis Architectures: A Case Study
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
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PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams
PLACO is a multi-stage framework that extends Bayesian combination of human and model labels to achieve cost-effective high performance in human-AI teams.