arXiv Machine Learning
Jul 24

Non-Stationary Functional Bilevel Optimization

arXiv:2601. 15363v2 Announce Type: replace-cross Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and perform suboptimally in online, non-stationary scenarios.

By Jason Bohne, Ieva Petrulionyte, Michael Arbel, Julien Mairal, Pawe{\l} Polak
arXiv Machine Learning
Jun 18

Stochastic Adaptive Gradient Descent Without Descent

arXiv:2509. 14969v2 Announce Type: replace Abstract: We introduce a new adaptive step-size strategy for convex optimization with stochastic gradient that exploits the local geometry of the objective function only by means of a first-order stochastic oracle and without any hyper-parameter tuning.

By Jean-Fran\c{c}ois Aujol, J\'er\'emie Bigot, Camille Castera
arXiv Machine Learning
Jun 19

Federated Bilevel Performative Prediction

arXiv:2606. 19734v1 Announce Type: new Abstract: Federated bilevel optimization is widely used for nested learning problems across distributed clients, such as federated hyperparameter tuning and meta-learning under privacy and communication constraints.

By Liangxin Qian, Chang Liu, Xuanyu Cao, Jun Zhao, Kwok-Yan Lam