5 citations · 7 across the 4 of their papers we have counts for
7 papers · 1 filter
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization
Shuchen Zhu, Boao Kong, Songtao Lu +2
This paper studies decentralized bilevel optimization, in which multiple agents collaborate to solve problems involving nested optimization structures with neighborhood communicati…
A Mathematics-Inspired Learning-to-Optimize Framework for Decentralized Optimization
Yutong He, Qiulin Shang, Xinmeng Huang +2
Most decentralized optimization algorithms are handcrafted. While endowed with strong theoretical guarantees, these algorithms generally target a broad class of problems, thereby n…
Distributed Bilevel Optimization with Communication Compression
Yutong He, Jie Hu, Xinmeng Huang +3
Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conve…
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
Boao Kong, Shuchen Zhu, Songtao Lu +2
Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine learning due to its versatility in handling nested structures. To address large-scale SBO, decen…
Understanding the Influence of Digraphs on Decentralized Optimization: Effective Metrics, Lower Bound, and Optimal Algorithm
Liyuan Liang, Xinmeng Huang, Ran Xin +1
This paper investigates the influence of directed networks on decentralized stochastic non-convex optimization associated with column-stochastic mixing matrices. Surprisingly, we f…
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Xinmeng Huang, Ping Li, Xiaoyun Li
Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potenti…