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Yiyuan Yang

6 papers hereh-index 491 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author1
  • last author1

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.CV1
same name
  • Yiyuan Yang — 9 papers, h 5
  • Yiyuan Yang — 6 papers, h 2
  • Yiyuan Yang — 6 papers, h 3
  • Yiyuan Yang — 3 papers, h 10
  • Yiyuan Yang — 3 papers, h 2
  • Yiyuan Yang — 1 paper, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedFederated Low-Rank Adaptation for Foundation Models: A Survey

10 citations · 13 across the 6 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CV2025

VideoCoF: Unified Video Editing with Temporal Reasoner

Xiangpeng Yang, Ji Xie, Yiyuan Yang +4

Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temp…

cs.LG2025

Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach

Yiyuan Yang, Guodong Long, Qinghua Lu +2

Federated foundation models represent a new paradigm to jointly fine-tune pre-trained foundation models across clients. It is still a challenge to fine-tune foundation models for a…

cs.LG2025★ 10 cited

Federated Low-Rank Adaptation for Foundation Models: A Survey

Yiyuan Yang, Guodong Long, Qinghua Lu +3

Effectively leveraging private datasets remains a significant challenge in developing foundation models. Federated Learning (FL) has recently emerged as a collaborative framework t…

cs.LG2025

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach

Yiyuan Yang, Guodong Long, Tianyi Zhou +3

As foundation models gain prominence, Federated Foundation Models (FedFM) have emerged as a privacy-preserving approach to collaboratively fine-tune models in federated learning (F…

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