no code implementations • 19 May 2024 • Youbang Sun, Shixiang Chen, Alfredo Garcia, Shahin Shahrampour
Many classical and modern machine learning algorithms require solving optimization tasks under orthogonal constraints.
no code implementations • 4 May 2024 • Youbang Sun, Tao Liu, P. R. Kumar, Shahin Shahrampour
We assume all agents make decisions under a policy with bounded rationality, which is enforced by the introduction of entropy regularization.
no code implementations • 18 Mar 2024 • Youbang Sun, Zitao Li, Yaliang Li, Bolin Ding
Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency.
no code implementations • 25 Sep 2022 • Youbang Sun, Heshan Fernando, Tianyi Chen, Shahin Shahrampour
We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process.
no code implementations • 29 May 2021 • Youbang Sun, Mahyar Fazlyab, Shahin Shahrampour
Our numerical experiments on strongly convex problems indicate that our framework certifies superior convergence rates compared to the existing rates for distributed GD.
no code implementations • 24 Nov 2020 • Youbang Sun, Shahin Shahrampour
Distributed optimization often requires finding the minimum of a global objective function written as a sum of local functions.
no code implementations • 14 Sep 2020 • Youbang Sun, Shahin Shahrampour
This work addresses distributed optimization, where a network of agents wants to minimize a global strongly convex objective function.
no code implementations • ICLR 2019 • Michael Tsang, Youbang Sun, Dongxu Ren, Yan Liu
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.