arXiv (All)
open access
Online Interval Selection with Binary Predictions: Optimal Consistency and a Continuous Robustness Tradeoff
We study online interval selection with binary predictions of membership in a final optimal solution. Requests arrive in arbitrary order; accepted requests may be revoked, but rejected or revoked requests cannot be recovered. With rewards proportional to interval length, the best consistency under f
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions#1035314
- AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling#259409
- Web Services ReliableMessaging v1.2 (2009-02-01)#600962
- Repeatable Requests Version 1.0 (2020-07-08)#600823
- WaveTLM: Reliable Time-Series Language Modeling through Task Compilation#1012973
Record · ID 1468965
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive