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Wentao Zhang

4 papers hereh-index 325 citations5 works total

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

author position
  • first author2
  • middle author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.CL1
  • cs.LG1
same name
  • Wentao Zhang — 35 papers, h 12
  • Wentao Zhang — 28 papers, h 23
  • Wentao Zhang — 15 papers, h 1
  • Wentao Zhang — 14 papers, h 3
  • Wentao Zhang — 14 papers, h 5
  • Wentao Zhang — 13 papers, h 11

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
20232026
most citedSeeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2026

SemOPT: Fixing Semantic Errors in LLM-based Optimization Modeling via Reward-Guided Search

Zetong Zhou, Wentao Zhang, Jingyuan Wang +3

Operations research supports decision-making in domains such as energy, economics, and healthcare. Solving operations research problems typically begins with optimization modeling,…

cs.AI2026

CityPlanner: A Sandbox Agent for Executable Urban Planning

Wentao Zhang, Jingyuan Wang, Zetong Zhou +2

Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical objectives such as cost and service…

cs.LG2024

VecCity: A Taxonomy-guided Library for Map Entity Representation Learning

Wentao Zhang, Jingyuan Wang, Yifan Yang +1

Electronic maps consist of diverse entities, such as points of interest (POIs), road networks, and land parcels, playing a vital role in applications like ITS and LBS. Map entity r…

cs.AI2023★ 1 cited

Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

Jiahao Ji, Wentao Zhang, Jingyuan Wang +1

Traffic prediction is essential for intelligent transportation systems and urban computing. It aims to establish a relationship between historical traffic data X and future traffic…

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