What is recorded here?
Adds a finite-sample worst-case correction to held-out empirical loss and selects conservativeness to control expected loss on a future exchangeable example.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Expectation is over calibration and test randomness, unlike high-probability calibration-conditional RCPS. Requires exchangeable, non-increasing, right-continuous losses with stated upper bound and feasible endpoint; arbitrary non-monotone losses are explicitly not covered. Near-tight lower bound has additional assumptions.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Anastasios N. Angelopoulos; Stephen Bates; Adam Fisch; Lihua Lei; Tal Schuster
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Conformal Risk Control for monotone bounded prediction losses
Adds a finite-sample worst-case correction to held-out empirical loss and selects conservativeness to control expected loss on a future exchangeable example.
Scientific scope
Expectation is over calibration and test randomness, unlike high-probability calibration-conditional RCPS. Requires exchangeable, non-increasing, right-continuous losses with stated upper bound and feasible endpoint; arbitrary non-monotone losses are explicitly not covered. Near-tight lower bound has additional assumptions.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Anastasios N. Angelopoulos; Stephen Bates; Adam Fisch; Lihua Lei; Tal Schuster
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.