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Ying Wen

17 papers hereh-index 11443 citations19 works total

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

author position
  • first author1
  • middle author10
  • last author5

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.AI8
  • cs.CL4
  • cs.LG4
  • cs.CV1
same name
  • Ying Wen — 28 papers, h 25
  • Ying Wen — 16 papers, h 8
  • Ying Wen — 16 papers, h 6
  • Ying Wen — 8 papers, h 5
  • Ying Wen — 7 papers, h 3
  • Ying Wen — 5 papers, h 7

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 citedDS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

7 citations · 14 across the 15 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

Kun Hu, Muning Wen, Xihuai Wang +5

Multi-agent reinforcement learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multi-agent systems. Most MARL algorithms use the simultan…

cs.LG2024

Natural Language Reinforcement Learning

Xidong Feng, Bo Liu, Yan Song +7

Artificial intelligence progresses towards the "Era of Experience," where agents are expected to learn from continuous, grounded interaction. We argue that traditional Reinforcemen…

cs.LG2024★ 7 cited

DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

Siyuan Guo, Cheng Deng, Ying Wen +3

In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then build…

cs.LG2024

Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement

Muning Wen, Junwei Liao, Cheng Deng +3

Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. Traditional approaches often depend on meticulously designed prompts, hi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.