Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in
learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level …

We consider stochastic unconstrained bilevel optimization problems when only the first-
order gradient oracles are available. While numerous optimization methods have been …

Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization,
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …

Despite its flexibility to learn diverse inductive biases in machine learning programs, meta
learning (ie,\learning to learn) has long been recognized to suffer from poor scalability due …

We introduce contextual stochastic bilevel optimization (CSBO)--a stochastic bilevel
optimization framework with the lower-level problem minimizing an expectation conditioned …

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 …

Functional constrained optimization (FCO) has emerged as a powerful tool for solving
various machine learning problems. However, with the rapid increase in applications of …

The ability to decompose complex natural scenes into meaningful object-centric abstractions
lies at the core of human perception and reasoning. In the recent culmination of …

Recent works in learning-integrated optimization have shown promise in settings where the
optimization problem is only partially observed or where general-purpose optimizers …

Federated bilevel optimization (FBO) has shown great potential recently in machine learning
and edge computing due to the emerging nested optimization structure in meta-learning …