Trainify: A CEGAR-Driven Training and Verification Framework for Safe Deep Reinforcement Learning. - Peng Jin; Jiaxu Tian; Dapeng Zhi; Xuejun Wen; Min Zhang 002 (CAV (1), 2022)

BBReach: Tight and Scalable Black-Box Reachability Analysis of Deep Reinforcement Learning Systems. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Guy Katz; Min Zhang 002 (CoRR, 2022)

Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Cheng Chen 015; Min Zhang 002 (NeurIPS, 2023)

Parameter-Selective Continual Test-Time Adaptation. - Jiaxu Tian; Fan Lyu (ACCV (8), 2024)

Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training. - Jiaxu Tian; Dapeng Zhi; Si Liu 003; Peixin Wang; Guy Katz; Min Zhang 002 (VMCAI (2), 2024)

ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models. - Jiaxu Tian; Xuehui Yu; Yaoxing Wang; Pan Wang; Guangqian Guo; Shan Gao (CoRR, 2025)