Bilevel Optimization
96 papers with code • 0 benchmarks • 0 datasets
Bilevel Optimization is a branch of optimization, which contains a nested optimization problem within the constraints of the outer optimization problem. The outer optimization task is usually referred as the upper level task, and the nested inner optimization task is referred as the lower level task. The lower level problem appears as a constraint, such that only an optimal solution to the lower level optimization problem is a possible feasible candidate to the upper level optimization problem.
Source: Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization
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Latest papers with no code
Data Augmentation Policy Search for Long-Term Forecasting
Data augmentation serves as a popular regularization technique to combat overfitting challenges in neural networks.
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization
This paper presents a new algorithm member for accelerating first-order methods for bilevel optimization, namely the \emph{(Perturbed) Restarted Accelerated Fully First-order methods for Bilevel Approximation}, abbreviated as \texttt{(P)RAF${}^2$BA}.
BiLO: Bilevel Local Operator Learning for PDE inverse problems
At the lower level, we train a neural network to locally approximate the PDE solution operator in the neighborhood of a given set of PDE parameters, which enables an accurate approximation of the descent direction for the upper level optimization problem.
Functional Bilevel Optimization for Machine Learning
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space.
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available.
Whiteness-based bilevel learning of regularization parameters in imaging
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise.
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades.
Generalizing Reward Modeling for Out-of-Distribution Preference Learning
During meta-training, a bilevel optimization algorithm is utilized to learn a reward model capable of guiding policy learning to align with human preferences across various distributions.
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems.
An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem
In this paper, we focus on simple bilevel optimization problems, where we minimize a convex smooth objective function over the optimal solution set of another convex smooth constrained optimization problem.
