19 citations · 52 across the 10 of their papers we have counts for
8 papers · 1 filter
High-probability complexity guarantees for nonconvex minimax problems
Yassine Laguel, Yasa Syed, Necdet Serhat Aybat +1
Stochastic smooth nonconvex minimax problems are prevalent in machine learning, e.g., GAN training, fair classification, and distributionally robust learning. Stochastic gradient d…
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method
Yassine Laguel, Necdet Serhat Aybat, Mert Gürbüzbalaban
We consider stochastic strongly-convex-strongly-concave (SCSC) saddle point (SP) problems which frequently arise in applications ranging from distributionally robust learning to ga…
Push--Pull with Device Sampling
Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler +1
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communicat…
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…