Convex Surrogate

Convex surrogates are simpler, often computationally tractable approximations of complex, non-convex objective functions used in various machine learning tasks, aiming to find solutions close to the optimal solution of the original problem. Current research focuses on balancing the trade-off between the dimensionality of the surrogate and its consistency with the original problem, exploring different model architectures like polyhedral surrogates and iteratively reweighted least squares algorithms. These efforts are significant because they improve the efficiency and scalability of solving challenging optimization problems in areas such as multiclass classification, matrix completion, and structured prediction, leading to more practical and effective machine learning models.