19 citations · 21 across the 5 of their papers we have counts for
6 papers
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…
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…
Chance constrained problems: a bilevel convex optimization perspective
Yassine Laguel, Jérôme Malick, Wim Ackooij
Chance constraints are a valuable tool for the design of safe decisions in uncertain environments; they are used to model satisfaction of a constraint with a target probability. Ho…
On the Convexity of Level-sets of Probability Functions
Yassine Laguel, Wim van Ackooij, Jérôme Malick +1
In decision-making problems under uncertainty, probabilistic constraints are a valuable tool to express safety of decisions. They result from taking the probability measure of a gi…
First-order Optimization for Superquantile-based Supervised Learning
Yassine Laguel, Jérôme Malick, Zaid Harchaoui
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training dist…
Randomized Progressive Hedging methods for Multi-stage Stochastic Programming
Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko +2
Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario…