Noiseless inverse optimization admits tight high-probability O(d/T) generalization bounds on the induced action set that extend to regret and match adversarial upper bounds.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
RASP-Tuner matches or beats GP-UCB and CMA-ES regret on seven of nine synthetic non-stationary tasks while running 8-12 times faster per step.
citing papers explorer
-
Tight Generalization Bounds for Noiseless Inverse Optimization
Noiseless inverse optimization admits tight high-probability O(d/T) generalization bounds on the induced action set that extend to regret and match adversarial upper bounds.
-
RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments
RASP-Tuner matches or beats GP-UCB and CMA-ES regret on seven of nine synthetic non-stationary tasks while running 8-12 times faster per step.