On penalty-based bilevel gradient descent method
Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization,
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …
Contextual stochastic bilevel optimization
We introduce contextual stochastic bilevel optimization (CSBO)--a stochastic bilevel
optimization framework with the lower-level problem minimizing an expectation conditioned …
optimization framework with the lower-level problem minimizing an expectation conditioned …
Projection-free methods for stochastic simple bilevel optimization with convex lower-level problem
J Cao, R Jiang, N Abolfazli… - Advances in …, 2024 - proceedings.neurips.cc
In this paper, we study a class of stochastic bilevel optimization problems, also known as
stochastic simple bilevel optimization, where we minimize a smooth stochastic objective …
stochastic simple bilevel optimization, where we minimize a smooth stochastic objective …
Slm: A smoothed first-order lagrangian method for structured constrained nonconvex optimization
S Lu - Advances in Neural Information Processing Systems, 2024 - proceedings.neurips.cc
Functional constrained optimization (FCO) has emerged as a powerful tool for solving
various machine learning problems. However, with the rapid increase in applications of …
various machine learning problems. However, with the rapid increase in applications of …
First-order penalty methods for bilevel optimization
In this paper, we study a class of unconstrained and constrained bilevel optimization
problems in which the lower level is a possibly nonsmooth convex optimization problem …
problems in which the lower level is a possibly nonsmooth convex optimization problem …
A conditional gradient-based method for simple bilevel optimization with convex lower-level problem
In this paper, we study a class of bilevel optimization problems, also known as simple bilevel
optimization, where we minimize a smooth objective function over the optimal solution set of …
optimization, where we minimize a smooth objective function over the optimal solution set of …
On penalty methods for nonconvex bilevel optimization and first-order stochastic approximation
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 …
objective functions are smooth but possibly nonconvex in both levels and the variables are …
Optimal algorithms for stochastic bilevel optimization under relaxed smoothness conditions
We consider stochastic bilevel optimization problems involving minimizing an upper-level
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …
On momentum-based gradient methods for bilevel optimization with nonconvex lower-level
F Huang - arXiv preprint arXiv:2303.03944, 2023 - arxiv.org
Bilevel optimization is a popular two-level hierarchical optimization, which has been widely
applied to many machine learning tasks such as hyperparameter learning, meta learning …
applied to many machine learning tasks such as hyperparameter learning, meta learning …
[PDF][PDF] PARL: A unified framework for policy alignment in reinforcement learning
We present a novel unified bilevel optimization-based framework, PARL, formulated to
address the recently highlighted critical issue of policy alignment in reinforcement learning …
address the recently highlighted critical issue of policy alignment in reinforcement learning …