Jiaguo Tian — accepted AI-conference papers

2026

CauScale: Neural Causal Discovery at Scale

Bo Peng, Sirui Chen, Jiaguo Tian, Yu Qiao, Chaochao Lu

ICML 2026poster

Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs. To address this challenge, we present CauScale, a neural architecture designed for eff…

Cited by 0 Source ↗Scholar ↗

2025

DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models

Shenyu Zhang, Jiaguo Tian, Zhengbang Zhu, Shan Huang, Jucheng Yang, Weinan Zhang

IROS 2025

Microscopic traffic simulation has become an important tool for autonomous driving training and testing. Although recent data-driven approaches advance realistic behavior generation, their learning still relies primarily on a single real-world dataset, which limits their diversity and thereby hinder

Cited by 9 Source ↗Scholar ↗