What is recorded here?
Penalizes cumulative ranked class probabilities beyond a chosen rank and calibrates the resulting nested randomized sets on held-out data to control marginal label coverage.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits conformal validity and APS cumulative scores; new rank-tail regularization is distinct. Finite-sample theorem assumes i.i.d. calibration/test triples and nested sets, with positivity for near-tight upper coverage. Marginal coverage is not per-input conditional coverage or guaranteed minimal set size.
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; Jitendra Malik; Michael I. Jordan
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
RAPS: regularized adaptive prediction sets for image classifiers
Penalizes cumulative ranked class probabilities beyond a chosen rank and calibrates the resulting nested randomized sets on held-out data to control marginal label coverage.
Scientific scope
Inherits conformal validity and APS cumulative scores; new rank-tail regularization is distinct. Finite-sample theorem assumes i.i.d. calibration/test triples and nested sets, with positivity for near-tight upper coverage. Marginal coverage is not per-input conditional coverage or guaranteed minimal set size.
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; Jitendra Malik; Michael I. Jordan
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.