Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …

This paper presents a new approach and algorithm for solving a class of constrained Bi-
Level Optimization (BLO) problems in which the lower-level problem involves constraints …

We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and
bilevel optimization techniques to develop a general gradient-based framework for end-to …

We study the problem of efficiently computing the derivative of the fixed-point of a parametric
non-differentiable contraction map. This problem has wide applications in machine learning …

Two-level stochastic optimization formulations have become instrumental in a number of
machine learning contexts such as continual learning, neural architecture search …

In this work, we propose algorithms for solving a class of Bilevel Optimization (BLO)
problems, with applications in areas such as signal processing, networking and machine …

We develop new algorithms for Riemannian bilevel optimization. We focus in particular on
batch and stochastic gradient-based methods, with the explicit goal of avoiding second …

Algorithms for bilevel optimization often encounter Hessian computations, which are
prohibitive in high dimensions. While recent works offer first-order methods for …

Constrained bilevel optimization tackles nested structures present in constrained learning
tasks like constrained meta-learning, adversarial learning, and distributed bilevel …