A Fenchel-Young loss formulation turns data-driven inverse optimization into a differentiable problem solvable by gradient methods, with claimed theoretical guarantees and superior empirical performance on noisy data.
Research in International Business and Finance 64:101879
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A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization
A Fenchel-Young loss formulation turns data-driven inverse optimization into a differentiable problem solvable by gradient methods, with claimed theoretical guarantees and superior empirical performance on noisy data.