19 citations · 52 across the 10 of their papers we have counts for
11 papers
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…
Federated Learning with Superquantile Aggregation for Heterogeneous Data
Krishna Pillutla, Yassine Laguel, Jérôme Malick +1
We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach…
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…
Device Heterogeneity in Federated Learning: A Superquantile Approach
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized…