Bilevel Optimization
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/stanfordasl/sensitivity_jax
공식 코드https://github.com/stanfordasl/sensitivity_torch
공식 코드https://github.com/syguan96/dynaboa
공식 코드Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
https://github.com/tsudalab/POPSL
공식 코드Preference-Optimized Pareto Set Learning for Blackbox Optimization
https://github.com/tum-pbs/prdp
공식 코드https://github.com/ucas-yanyang/lancbio
공식 코드LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace
https://github.com/ucr-optml/fedmsa
공식 코드Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
https://github.com/umass-ml4ed/c-bobcat
공식 코드Balancing Test Accuracy and Security in Computerized Adaptive Testing
https://github.com/vis-opt-group/iaptt-gm
공식 코드Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
https://github.com/weixuan-wang123/hbo
공식 코드HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
https://github.com/wronnyhuang/metapoison
공식 코드MetaPoison: Practical General-purpose Clean-label Data Poisoning
https://github.com/xfflzl/cim-cil
공식 코드Class-Incremental Exemplar Compression for Class-Incremental Learning
https://github.com/yazdanimehdi/fairbinn
공식 코드https://github.com/yingchi1998/bgl
공식 코드https://github.com/yining610/dynamic-decomposition
공식 코드https://github.com/ymhe12/GS-Phong
공식 코드GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis
https://github.com/yongjian16/IDGNN
공식 코드Efficient and Effective Implicit Dynamic Graph Neural Network
https://github.com/yuji-roh/fairbatch
공식 코드https://github.com/yxzwang/phenomnn
공식 코드From Hypergraph Energy Functions to Hypergraph Neural Networks
https://github.com/zalanborsos/bilevel_coresets
공식 코드Coresets via Bilevel Optimization for Continual Learning and Streaming
https://github.com/zhang-vislab/iros2024-lossdistillationweightedcd
공식 코드Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance
https://github.com/zhenzhang-ye/enhance_hypergradient
공식 코드Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
https://github.com/AsafShul/PoDD
https://github.com/ComputerVisionTalks/Talk-01
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/JinyuanLiu-CV/TarDAL
https://github.com/JonasGeiping/poisoning-gradient-matching
MetaPoison: Practical General-purpose Clean-label Data Poisoning
https://github.com/JunjieYang97/stocBiO
Bilevel Optimization: Convergence Analysis and Enhanced Design
https://github.com/KarhouTam/FL-bench
https://github.com/Kaushalya/metal
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
https://github.com/MindSpore-scientific-2/code-4/tree/main/nystrom-attention
Nystrom Method for Accurate and Scalable Implicit Differentiation
https://github.com/MindSpore-scientific-2/code-5/tree/main/nystrom-attention
Nystrom Method for Accurate and Scalable Implicit Differentiation
https://github.com/TsingZ0/FL-IoT
https://github.com/TsingZ0/PFL-Non-IID
https://github.com/TsingZ0/PFLlib
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.