Search Results for author: Youbang Sun

Found 6 papers, 0 papers with code

Improving LoRA in Privacy-preserving Federated Learning

no code implementations18 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.

Computational Efficiency Federated Learning +1

On the Stability Analysis of Open Federated Learning Systems

no code implementations25 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.

Federated Learning

On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraints

no code implementations29 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.

Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems

no code implementations24 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.

Distributed Optimization

Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics

no code implementations14 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.

Distributed Optimization

Can I trust you more? Model-Agnostic Hierarchical Explanations

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.

BIG-bench Machine Learning Negation

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