| Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective K Xu, H Chen, S Liu, PY Chen, TW Weng, M Hong, X Lin (IJCAI-2019) The International Joint Conferences on Artificial Intelligence, 2019 | 420 | 2019 |
| Beta-crown: Efficient bound propagation with per-neuron split constraints for neural network robustness verification S Wang, H Zhang, K Xu, X Lin, S Jana, CJ Hsieh, JZ Kolter (NeurIPS-2021) Advances in Neural Information Processing Systems 34, 29909-29921, 2021 | 373* | 2021 |
| Adversarial T-shirt! Evading Person Detectors in a Physical World K Xu, G Zhang, S Liu, Q Fan, M Sun, H Chen, PY Chen, Y Wang, X Lin (ECCV-2020 Spotlight) The European Conference on Computer Vision, 665-681, 2020 | 350 | 2020 |
| Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond K Xu, Z Shi, H Zhang, Y Wang, KW Chang, M Huang, B Kailkhura, X Lin, ... (NeurIPS-2020) Advances in Neural Information Processing Systems, 2020 | 263 | 2020 |
| A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly Y Yao, J Duan, K Xu, Y Cai, Z Sun, Y Zhang High-Confidence Computing, 100211, 2024 | 215 | 2024 |
| Adversarial Robustness vs. Model Compression, or Both? S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ... (ICCV-2019) The International Conference on Computer Vision, 2019 | 191 | 2019 |
| Structured Adversarial Attack: Towards General Implementation and Better Interpretability K Xu, S Liu, P Zhao, PY Chen, H Zhang, D Erdogmus, Y Wang, X Lin (ICLR-2019) The International Conference on Learning Representations, 2018 | 185 | 2018 |
| Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers K Xu, H Zhang, S Wang, Y Wang, S Jana, X Lin, CJ Hsieh (ICLR-2021) The International Conference on Learning Representations, 2020 | 179 | 2020 |
| Trustllm: Trustworthiness in large language models L Sun, Y Huang, H Wang, S Wu, Q Zhang, C Gao, Y Huang, W Lyu, ... arXiv preprint arXiv:2401.05561, 2024 | 132 | 2024 |
| REQ-YOLO: A Resource-aware, Efficient Quantization Framework for Object Detection on FPGAs C Ding, S Wang, N Liu, K Xu, Y Wang, Y Liang (FPGA-2019) Proceedings of the 2019 ACM/SIGDA International Symposium on …, 2019 | 117 | 2019 |
| ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization X Chen, S Liu, K Xu, X Li, X Lin, M Hong, D Cox (NeurIPS-2019) Advances in Neural Information Processing Systems, 2019 | 109 | 2019 |
| Progressive DNN Compression: A Key to Achieve Ultra-high Weight Pruning and Quantization Rates Using ADMM S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ... arXiv preprint arXiv:1903.09769, 2019 | 108* | 2019 |
| General cutting planes for bound-propagation-based neural network verification H Zhang, S Wang, K Xu, L Li, B Li, S Jana, CJ Hsieh, JZ Kolter (NeurIPS-2022) Advances in Neural Information Processing Systems, 2022 | 97 | 2022 |
| Min-max Optimization without Gradients: Convergence and Applications to Black-box Evasion and Poisoning Attacks S Liu, S Lu, X Chen, Y Feng, K Xu, A Al-Dujaili, M Hong, UM O’Reilly (ICML-2020) The International Conference on Machine Learning, 2020 | 90 | 2020 |
| On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method P Zhao, S Liu, PY Chen, N Hoang, K Xu, B Kailkhura, X Lin (ICCV-2019) The International Conference on Computer Vision, 2019 | 74 | 2019 |
| Are diffusion models vulnerable to membership inference attacks? J Duan, F Kong, S Wang, X Shi, K Xu (ICML-2023) The International Conference on Machine Learning, 2023 | 66 | 2023 |
| Defending against Backdoor Attack on Deep Neural Networks K Xu, S Liu, PY Chen, P Zhao, X Lin (KDD workshop-2019) 3rd Workshop on Adversarial Learning Methods for Machine …, 2020 | 56 | 2020 |
| Asymmetric Discrete Graph Hashing X Shi, F Xing, K Xu, M Sapkota, L Yang (AAAI-2017) The Association for the Advancement of Artificial Intelligence, 2017 | 55 | 2017 |
| On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning R Wang, K Xu, S Liu, PY Chen, TW Weng, C Gan, M Wang (ICLR-2021) The International Conference on Learning Representations, 2021 | 49 | 2021 |
| Interpreting Adversarial Examples by Activation Promotion and Suppression K Xu, S Liu, G Zhang, M Sun, P Zhao, Q Fan, C Gan, X Lin arXiv preprint arXiv:1904.02057, 2019 | 43 | 2019 |
Xue LinNortheastern UniversityBestätigte E-Mail-Adresse bei northeastern.edu
