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math.OC2022
Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
R. Tyrell Rockafellar and collaborators introduced, in a series of works, new regression modeling methods based on the notion of superquantile (or conditional value-at-risk). These…
math.OC2022
Superquantile-based learning: a direct approach using gradient-based optimization
Yassine Laguel, Jérôme Malick, Zaid Harchaoui
We consider a formulation of supervised learning that endows models with robustness to distributional shifts from training to testing. The formulation hinges upon the superquantile…