Loading
Life sciences · Preprint
arXiv · August 7, 2026
Raises a question worth testing. It does not answer one.
This preprint introduces OCPQ, an online conformal prediction algorithm that operates without feedback from deployed predictions, instead choosing between outputting prediction sets or querying labels. The authors provide theoretical guarantees (O(T^2/3) expected regret, coverage at least β−O(T^−1/3)) and report experimental validation on real-world datasets, but do not provide empirical comparison to existing methods or deployment outcomes in safety-critical settings.
Theoretical algorithm development with experimental validation. Intervention: Online Conformal Prediction with Queries (OCPQ) algorithm adapting label-efficient forecasting to a partial monitoring game in which prediction actions return no observation and query actions reveal the true label..
OCPQ achieves O(T^2/3) expected regret for any non-i.i.d. oblivious data stream of length T Expected coverage is at least β−O(T^−1/3) for user-defined β OCPQ queries only an expected T^−1/3 fraction of rounds while providing coverage comparable to bandit-based OCP methods
Application to safety-critical domains (e.g. medical) not demonstrated or validated
The source did not state who this applies to in practice.
This is a theoretical algorithmic contribution with experimental validation on a novel problem formulation, lacking empirical comparison to clinical or safety-critical deployment outcomes.
As stated by the source record.
Quoted from the source exactly as published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provides theoretically principled uncertainty quantification for arbitrary black-box classifiers and non-i.i.d. data streams by constructing prediction sets that are guaranteed to contain the true label at a user-specified frequency. OCP usually updates prediction sets using feedback from previously deployed predictions. We instead study an OCP setting beyond feedback: on each round, the learner can either output a prediction set or query the correct label, but not both. Thus, no deployed prediction is ever evaluated directly. We reduce this problem to a partial monitoring game in which prediction actions return no observation and a separate query action reveals the label. The reward function is constructed in a way that encourages the learner to output small prediction sets while ensuring that the correct label is covered with a sufficiently high probability. To solve this game, we develop OCP with queries (OCPQ) by adapting the label efficient forecaster of Cesa-Bianchi, Lugosi, and Stoltz (2004) to our setting. For any black box classifier and any (non-i.i.d.) oblivious data stream of length $T$, OCPQ has $O(T^{2/3})$ expected regret and expected coverage at least $β-O(T^{-1/3})$ for a user-defined $β$, while querying only an expected $T^{-1/3}$ fraction of rounds. This provides coverage comparable to bandit-based OCP methods while requiring no feedback from deployed prediction sets. Experiments on real-world datasets further demonstrate the effectiveness of our approach.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.