Ganghua Wang

According to our database, Ganghua Wang authored at least 20 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science.

An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding

CoRR, March, 2026

2025

An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems.

Fangqiao Tian, An Luo, Jin Du, Xun Xian, Robert Specht, Ganghua Wang, Xuan Bi, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Rui Zhang, Zirui Liu, Mingyi Hong, Jie Ding

CoRR, May, 2025

Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference.

Jin Du, Li Chen, Xun Xian, An Luo, Fangqiao Tian, Ganghua Wang, Charles Doss, Xiaotong Shen, Jie Ding

CoRR, May, 2025

Cer-Eval: Certifiable and Cost-Efficient Evaluation Framework for LLMs.

Ganghua Wang, Zhaorun Chen, Bo Li, Haifeng Xu

CoRR, May, 2025

Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses.

Ganghua Wang, Yuhong Yang, Jie Ding

CoRR, February, 2025

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains.

Xun Xian, Ganghua Wang, Xuan Bi, Rui Zhang, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Proceedings of the Forty-second International Conference on Machine Learning, 2025

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science.

An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Can Agentic AI Match the Performance of Human Data Scientists?

An Luo, Jin Du, Fangqiao Tian, Xun Xian, Robert Specht, Ganghua Wang, Xuan Bi, Charles Fleming, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding

Proceedings of the 10th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2025

2024

Mitigating Group Bias in Federated Learning: Beyond Local Fairness.

Ganghua Wang, Ali Payani, Myungjin Lee, Ramana Rao Kompella

Trans. Mach. Learn. Res., 2024

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains.

Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

CoRR, 2024

Drift to Remember.

Jin Du, Xinhe Zhang, Hao Shen, Xun Xian, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Na Li, Jia Liu, Jie Ding

CoRR, 2024

RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees.

Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Demystifying Poisoning Backdoor Attacks from a Statistical Perspective.

Ganghua Wang, Xun Xian, Ashish Kundu, Jayanth Srinivasa, Xuan Bi, Mingyi Hong, Jie Ding

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization.

Gen Li, Ganghua Wang, Jie Ding

IEEE Trans. Inf. Theory, September, 2023

A Unified Detection Framework for Inference-Stage Backdoor Defenses.

Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Understanding Backdoor Attacks through the Adaptability Hypothesis.

Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

Proceedings of the International Conference on Machine Learning, 2023

Pruning Deep Neural Networks from a Sparsity Perspective.

Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation.

Wenjing Yang, Ganghua Wang, Enmao Diao, Vahid Tarokh, Jie Ding, Yuhong Yang

CoRR, 2022

2021

Subset Privacy: Draw from an Obfuscated Urn.

Ganghua Wang, Jie Ding

CoRR, 2021

2017

Error estimates on a finite volume method for diffusion problems with interface on rectangular grids.

Jie Peng, Shi Shu, Haiyuan Yu, Chunsheng Feng, Mingxian Kan, Ganghua Wang

Appl. Math. Comput., 2017