Rongxing Du — accepted AI-conference papers

2026

DECENTRALIZED LEARNING WITH DYNAMICALLY REFINED EDGE WEIGHTS: A DATA-DEPENDENT FRAMEWORK

ICASSP 2026poster

This paper aims to accelerate decentralized optimization by strategically designing the edge weights used in the agent-to-agent message exchanges. We propose a Dynamic Directed Decentralized Gradient (D3GD) framework and show that the proposed data-dependent framework is a practical alternative to t…

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2024

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du +2

ICLR 2024poster

This work focuses on leveraging and selecting from vast, unlabeled, open data to *pre-fine-tune* a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms…

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