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Li Shen

4 papers hereh-index 331 citations6 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG4
same name
  • Li Shen — 71 papers, h 29
  • Li Shen — 22 papers, h 12
  • Li Shen — 15 papers, h 15
  • Li Shen — 13 papers, h 6
  • Li Shen — 12 papers, h 8
  • Li Shen — 12 papers, h 4

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

most citedAligning Few-Step Diffusion Models with Dense Reward Difference Learning

2 citations · 4 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024★ 1 cited

A Unified Analysis for Finite Weight Averaging

Peng Wang, Li Shen, Zerui Tao +3

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential M…

cs.LG2024

Stability and Generalization for Distributed SGDA

Miaoxi Zhu, Yan Sun, Li Shen +2

Minimax optimization is gaining increasing attention in modern machine learning applications. Driven by large-scale models and massive volumes of data collected from edge devices,…

cs.LG2024★ 2 cited

Aligning Few-Step Diffusion Models with Dense Reward Difference Learning

Ziyi Zhang, Li Shen, Sen Zhang +6

Few-step diffusion models enable efficient high-resolution image synthesis but struggle to align with specific downstream objectives due to limitations of existing reinforcement le…

cs.LG2023★ 1 cited

Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm

Miaoxi Zhu, Li Shen, Bo Du +1

The growing size of available data has attracted increasing interest in solving minimax problems in a decentralized manner for various machine learning tasks. Previous theoretical…

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