Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Gang Niu 001; Lizhen Cui; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2020)

Guided Interpolation for Adversarial Training. - Chen Chen 043; Jingfeng Zhang; Xilie Xu; Tianlei Hu; Gang Niu 001; Gang Chen 001; Masashi Sugiyama (CoRR, 2021)

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Gang Niu 001; Lizhen Cui; Masashi Sugiyama (CoRR, 2021)

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2022)

Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (CoRR, 2022)

NoiLin: Improving adversarial training and correcting stereotype of noisy labels. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Lizhen Cui; Gang Niu 001; Masashi Sugiyama (Trans. Mach. Learn. Res., 2022)

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (NeurIPS, 2023)

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. - Xilie Xu; Jingfeng Zhang; Feng Liu 003; Masashi Sugiyama; Mohan S. Kankanhalli (NeurIPS, 2023)

AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework. - Xilie Xu; Jingfeng Zhang; Mohan S. Kankanhalli (CoRR, 2023)

Decision Boundary-Aware Data Augmentation for Adversarial Training. - Chen Chen 043; Jingfeng Zhang; Xilie Xu; Lingjuan Lyu; Chaochao Chen 001; Tianlei Hu; Gang Chen 001 (IEEE Trans. Dependable Secur. Comput., 2023)

An LLM can Fool Itself: A Prompt-Based Adversarial Attack. - Xilie Xu; Keyi Kong; Ning Liu 014; Lizhen Cui; Di Wang 015; Jingfeng Zhang; Mohan S. Kankanhalli (ICLR, 2024)

AutoLoRa: An Automated Robust Fine-Tuning Framework. - Xilie Xu; Jingfeng Zhang; Mohan S. Kankanhalli (ICLR, 2024)

Perplexity-aware Correction for Robust Alignment with Noisy Preferences. - Keyi Kong; Xilie Xu; Di Wang 015; Jingfeng Zhang; Mohan S. Kankanhalli (NeurIPS, 2024)

Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models. - Zihao Luo; Xilie Xu; Feng Liu; Yun Sing Koh; Di Wang 015; Jingfeng Zhang (CoRR, 2024)

Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM. - Yangyang Guo; Ziwei Xu 001; Xilie Xu; Yongkang Wong; Liqiang Nie; Mohan S. Kankanhalli (CoRR, 2024)

Privacy-Preserving Low-Rank Adaptation Against Membership Inference Attacks for Latent Diffusion Models. - Zihao Luo; Xilie Xu; Feng Liu; Yun Sing Koh; Di Wang 015; Jingfeng Zhang (AAAI, 2025)