Get Started
Research questionHow can quantile risk control meet predefined risk thresholds with finite-sample guarantees without excessive conservatism?Finite calibration data make it difficult to ensure that a quantile-based loss stays below a chosen threshold. Procedures with rigorous guarantees may be overly conservative, while tighter procedures may lack finite-sample validity.
Evaluation & Benchmarks
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Occupancy-based Quantile Risk ControlThe source studies conformal quantile risk control for machine-learning deployment, using calibration losses to control risk at threshold α with confidence 1−δ. It reports a finite-sample guarantee, convergence at rate O_p(n^-1/2), and benchmark experiments showing up to a 78.64% reduction in risk gap; specific deployment domains and model-access requirements are not given.research paper · Sep 2, 2026
Related questions
How can regression models produce tight, valid prediction intervals across confidence levels without retraining?How can extreme quantile treatment effects be estimated under heavy tails while respecting location invariance?How can deep imbalanced regression avoid underfitting scarce tail labels when uncertainty varies across instances?How can interval-based time-series classifiers speed up frequent inference without materially reducing accuracy?
Home
Topics
Search
Library