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2020 – today
- 2025
[j1]Bozhi Wu
, Chengjie Liu, Zhiming Li, Yushi Cao, Jun Sun, Shangwei Lin:
Enhancing Vulnerability Detection via Inter-procedural Semantic Completion. Proc. ACM Softw. Eng. 2(ISSTA): 825-847 (2025)- [c12]Zhiming Li, Junzhe Jiang, Yushi Cao, Aixin Cui, Bozhi Wu, Bo Li, Yang Liu, Danny Dongning Sun:
Logic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning. AAAI 2025: 18584-18592 - [i9]Yupeng Cheng, Zi Pong Lim, Sarthak Ketanbhai Modi, Yon Shin Teo, Yushi Cao, Shangwei Lin:
Towards Calibration Enhanced Network by Inverse Adversarial Attack. CoRR abs/2504.06358 (2025) - [i8]Sarthak Ketanbhai Modi, Zi Pong Lim, Yushi Cao, Yupeng Cheng, Yon Shin Teo, Shangwei Lin:
SOSAE: Self-Organizing Sparse AutoEncoder. CoRR abs/2507.04644 (2025) - [i7]Hongchao Jiang, Yiming Chen, Yushi Cao, Hung-yi Lee, Robby T. Tan:
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks. CoRR abs/2507.10535 (2025) - 2024
- [c11]Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Junzhe Jiang, Yang Liu:
Unveiling Project-Specific Bias in Neural Code Models. LREC/COLING 2024: 17205-17216 - [c10]Zhiming Li, Junzhe Jiang, Yushi Cao, Aixin Cui, Bozhi Wu, Bo Li, Yang Liu:
Logic-guided Deep Reinforcement Learning for Stock Trading. Tiny Papers @ ICLR 2024 - [c9]Zhiming Li, Yushi Cao, Yan Zheng, Xu Liu, Bozhi Wu, Tianlin Li, Xiufeng Xu, Junzhe Jiang, Yon Shin Teo, Shang-Wei Lin, Yang Liu:
Improving Neural Logic Machines via Failure Reflection. ICML 2024 - [c8]Yupeng Cheng, Zi Pong Lim, Sarthak Ketanbhai Modi, Yon Shin Teo, Yushi Cao, Shangwei Lin:
Towards a More Robust and Accurate OCR Model with Adversarial Techniques in HMI Testing Scenarios. CAI 2024: 1414-1421 - [c7]Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-Wei Lin, Yang Liu:
LLMs for Relational Reasoning: How Far are We? LLM4CODE@ICSE 2024: 119-126 - [c6]Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann:
Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiency. ECML/PKDD (3) 2024: 21-38 - [i6]Zhiming Li, Yushi Cao, Xiufeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-Wei Lin, Yang Liu:
LLMs for Relational Reasoning: How Far are We? CoRR abs/2401.09042 (2024) - 2023
- [c5]Yushi Cao, Yanran Li, Yon Shin Teo, Yan Zheng, Zhexin Liang, Shang-Wei Lin:
An Automatic Test Plan Generation Approach for Automotive Software Testing. SoMeT 2023: 18-27 - [i5]Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann:
Do We Really Need Graph Neural Networks for Traffic Forecasting? CoRR abs/2301.12603 (2023) - [i4]Zhiming Li, Junzhe Jiang, Yushi Cao, Aixin Cui, Bozhi Wu, Bo Li, Yang Liu:
Logic-guided Deep Reinforcement Learning for Stock Trading. CoRR abs/2310.05551 (2023) - 2022
- [c4]Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu:
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis. NeurIPS 2022 - [c3]Yushi Cao, Yon Shin Teo, Yan Zheng, Yuxuan Toh, Shang-Wei Lin:
A Holistic Automated Software Structure Exploration Framework for Testing. SoMeT 2022: 286-297 - [c2]Yushi Cao, David Berend, Palina Tolmach, Guy Amit, Moshe Levy, Yang Liu, Asaf Shabtai, Yuval Elovici:
Fair and accurate age prediction using distribution aware data curation and augmentation. WACV 2022: 2867-2877 - [i3]Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Xiaofei Xie, Yi Li, Yang Liu:
Unveiling Project-Specific Bias in Neural Code Models. CoRR abs/2201.07381 (2022) - [i2]Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li, Jianye Hao, Yang Liu:
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis. CoRR abs/2205.13728 (2022) - 2021
- [c1]Yushi Cao, Yan Zheng, Shang-Wei Lin, Yang Liu, Yon Shin Teo, Yuxuan Toh, Vinay Vishnumurthy Adiga:
Automatic HMI Structure Exploration Via Curiosity-Based Reinforcement Learning. ASE 2021: 1151-1155 - 2020
- [i1]Yushi Cao, David Berend, Palina Tolmach, Moshe Levy, Guy Amit, Asaf Shabtai, Yuval Elovici, Yang Liu:
Fairness Matters - A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems. CoRR abs/2009.05283 (2020)
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