arXiv (OAI Expanded)
open access
Efficient Online Inverse Optimization with $O(d)$ Regret
We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bound of this order was obtained recently by Dewasurendra, settling a question of Gollapudi et al.\ and of Oki and Sakaue, but by an improper rule that
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
- Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise#411038
- Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights#1063189
- Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret#998552
- Lower Bounds for Linear-Oracle Online Learning#1142976
- Lipschitz Bandits with Arbitrary Feedback Delays#612467
Record · ID 1071228
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