4 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
FADAS: Towards Federated Adaptive Asynchronous Optimization
Yujia Wang, Shiqiang Wang, Songtao Lu +1
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable…
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…
Ontology Revision based on Pre-trained Language Models
Qiu Ji, Guilin Qi, Yuxin Ye +4
Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and on…
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
Paz Fink Shustin, Shashanka Ubaru, Małgorzata J. Zimoń +4
Learning data representations under uncertainty is an important task that emerges in numerous scientific computing and data analysis applications. However, uncertainty quantificati…