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.
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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.