Surrogate Regret

Surrogate regret quantifies the suboptimality of using a surrogate loss function—a computationally tractable approximation—to optimize a true, often non-convex, loss function in machine learning tasks like classification and structured prediction. Current research focuses on establishing tight bounds on this regret, particularly investigating the relationship between the properties of the surrogate loss (e.g., strong properness) and the achievable convergence rate. This work leverages frameworks like the exploit-the-surrogate-gap approach and embedding techniques to analyze and design consistent surrogate losses, leading to improved algorithms and a deeper understanding of the trade-offs between computational efficiency and performance. Ultimately, advancements in understanding surrogate regret contribute to more efficient and effective machine learning models across various applications.