Bilevel optimization, which captures the inherent nested structure of machine learning
problems, is gaining popularity in many recent applications. Existing works on bilevel …

We consider stochastic bilevel optimization problems involving minimizing an upper-level
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …

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 …

Bilevel optimization has recently regained interest owing to its applications in emerging
machine learning fields such as hyperparameter optimization, meta-learning, and …

Bilevel optimization has recently regained interest owing to its applications in emerging
machine learning fields such as hyperparameter optimization, metalearning, and …

Bilevel optimization has seen an increasing presence in various domains of applications. In
this work, we propose a framework for solving bilevel optimization problems where variables …

Stochastic bilevel optimization, which captures the inherent nested structure of machine
learning problems, is gaining popularity in many recent applications. Existing works on …

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 …

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 …