arXiv (All)
open access
Omniprediction for All Mixable Proper Losses and Beyond
Omniprediction concerns the task of making predictions that are simultaneously accurate with respect to different losses. Earlier results by [CV09] show that in an online learning setting, $O(\log(|\mathcal{F}||\mathcal{L}|))$ regret is achievable simultaneously against every predictor in a finite p
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
- When Does q-error Predict Plan Regret? Three Regimes of Cardinality-Estimation Error#282900
- Review: "The Disappointment Dilemma: Short- and Long-Term Learning From Negative Outcomes".#148125
- Dynamic Regret via Discounted-to-Dynamic Reduction with Applications to Curved Losses and Adam Optimizer#1131462
- Constant regret in general games via higher-order optimism#639658
- Fear as a Rational Filter: Asymmetric Risk, Irreversibility, and the Evolution of Action Suppression#697140
Record · ID 1534212
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