| On the Algorithmic Stability of Adversarial Training Y Xing, Q Song, G Cheng Neurips 2021, 2021 | 48 | 2021 |
| Directional Pruning of Deep Neural Networks SK Chao, Z Wang, Y Xing, G Cheng Advances in Neural Information Processing Systems 33, 2020 | 39 | 2020 |
| Benefit of interpolation in nearest neighbor algorithms Y Xing, Q Song, G Cheng arXiv preprint arXiv:2202.11817, 2022 | 34* | 2022 |
| On the generalization properties of adversarial training Y Xing, Q Song, G Cheng International Conference on Artificial Intelligence and Statistics, 505-513, 2021 | 33 | 2021 |
| DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models Y Cui, J Ren, H Xu, P He, H Liu, L Sun, J Tang arXiv preprint arXiv:2306.04642, 2023 | 23 | 2023 |
| Adversarially Robust Estimate and Risk Analysis in Linear Regression Y Xing, R Zhang, G Cheng International Conference on Artificial Intelligence and Statistics, 514-522, 2021 | 21 | 2021 |
| Why Do Artificially Generated Data Help Adversarial Robustness Y Xing, Q Song, G Cheng Advances in Neural Information Processing Systems, 2022 | 10 | 2022 |
| Phase Transition from Clean Training to Adversarial Training Y Xing, Q Song, G Cheng Advances in Neural Information Processing Systems, 2022 | 6 | 2022 |
| Unlabeled Data Help: Minimax Analysis and Adversarial Robustness Y Xing, Q Song, G Cheng International Conference on Artificial Intelligence and Statistics, 136-168, 2022 | 5 | 2022 |
| Predictive Power of Nearest Neighbors Algorithm under Random Perturbation Y Xing, Q Song, G Cheng International Conference on Artificial Intelligence and Statistics, 496-504, 2021 | 5 | 2021 |
| FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models Y Cui, J Ren, Y Lin, H Xu, P He, Y Xing, W Fan, H Liu, J Tang arXiv preprint arXiv:2310.02401, 2023 | 3 | 2023 |
| Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data Y Xing, X Lin, N Suh, Q Song, G Cheng arXiv preprint arXiv:2402.00743, 2024 | 2 | 2024 |
| Exploring Memorization in Fine-tuned Language Models S Zeng, Y Li, J Ren, Y Liu, H Xu, P He, Y Xing, S Wang, J Tang, D Yin arXiv preprint arXiv:2310.06714, 2023 | 2 | 2023 |
| The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) S Zeng, J Zhang, P He, Y Xing, Y Liu, H Xu, J Ren, S Wang, D Yin, ... arXiv preprint arXiv:2402.16893, 2024 | 1 | 2024 |
| Superiority of Multi-Head Attention in In-Context Linear Regression Y Cui, J Ren, P He, J Tang, Y Xing arXiv preprint arXiv:2401.17426, 2024 | 1 | 2024 |
| An Adversarially Robust Formulation of Linear Regression with Missing Data A Aghasi, S Ghadimi, Y Xing, MJ Feizollahi Available at SSRN, 2023 | 1* | 2023 |
| Variance Reduction for Risk Measures with Importance Sampling in Nested Simulation Y Xing, T Sit, HY Wong Quantitative Finance, 2021 | 1 | 2021 |
| Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention J Ren, Y Li, S Zen, H Xu, L Lyu, Y Xing, J Tang arXiv preprint arXiv:2403.11052, 2024 | | 2024 |
| Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability R Haldar, Y Xing, Q Song arXiv preprint arXiv:2403.03967, 2024 | | 2024 |
| Data Poisoning for In-context Learning P He, H Xu, Y Xing, H Liu, M Yamada, J Tang arXiv preprint arXiv:2402.02160, 2024 | | 2024 |
Qifan SongPurdue UniversityVerified email at purdue.edu
