Bilevel Optimization

76 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

Most implemented papers

Adaptive Personalized Federated Learning

MLOPTPSU/FedTorch 30 Mar 2020

Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.

OptNet: Differentiable Optimization as a Layer in Neural Networks

Kyubyong/sudoku ICML 2017

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks.

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

asteroidhouse/self-tuning-networks ICLR 2019

Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.

Second-Order Sensitivity Analysis for Bilevel Optimization

stanfordasl/second-ordersensitivityanalysisforbileveloptimization 4 May 2022

Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with respect to its parameters; this IFT gradient is then used in a first-order optimization method for the upper problem.

Truncated Back-propagation for Bilevel Optimization

lucfra/FAR-HO 25 Oct 2018

Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks.

Penalty Method for Inversion-Free Deep Bilevel Optimization

jihunhamm/bilevel-penalty 8 Nov 2019

We present results on data denoising, few-shot learning, and training-data poisoning problems in a large-scale setting.

MetaPoison: Practical General-purpose Clean-label Data Poisoning

wronnyhuang/metapoison NeurIPS 2020

Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models.

Learning Data Augmentation with Online Bilevel Optimization for Image Classification

ElementAI/bilevel_augment 25 Jun 2020

Data augmentation is a key practice in machine learning for improving generalization performance.

Adversarial Immunization for Certifiable Robustness on Graphs

TaoShuchang/AdvImmune 19 Jul 2020

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.

BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning

dut-media-lab/BOML 28 Sep 2020

learning to learn) has recently emerged as a promising paradigm for a variety of applications.