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 …

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 …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

Coreset is a small set that provides a data summary for a large dataset, such that training
solely on the small set achieves competitive performance compared with a large dataset. In …

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 …

In this paper, we revisit the bilevel optimization problem, in which the upper-level objective
function is generally nonconvex and the lower-level objective function is strongly convex …

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 …

In this work, we study first-order algorithms for solving Bilevel Optimization (BO) where the
objective functions are smooth but possibly nonconvex in both levels and the variables are …

In appropriate frameworks, automatic differentiation is transparent to the user, at the cost of
being a significant computational burden when the number of operations is large. For …