| DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models B Wang*, W Chen*, H Pei*, C Xie*, M Kang*, C Zhang*, C Xu, Z Xiong, ... NeurIPS 2023, 2023 | 116 | 2023 |
| Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning H Zhao, Y Fu, M Kang, Q Tian, F Wu, X Li TPAMI, 2021 | 68* | 2021 |
| Fairness in federated learning via core-stability B Ray Chaudhury, L Li, M Kang, B Li, R Mehta NeurIPS 2022, 2022 | 13 | 2022 |
| Certifying Some Distributional Fairness with Subpopulation Decomposition M Kang*, L Li*, M Weber, Y Liu, C Zhang, B Li NeurIPS 2022, 2022 | 12 | 2022 |
| Label-assemble: Leveraging multiple datasets with partial labels M Kang, B Li, Z Zhu, Y Lu, EK Fishman, A Yuille, Z Zhou ISBI 2023, 2023 | 9* | 2023 |
| FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust DNNs M Kang, L Li, B Li SaTML 2023, 2023 | 2 | 2023 |
| DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification M Kang, D Song, B Li NeurIPS 2023, 2023 | 1 | 2023 |
| C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models M Kang, NM Gürel, N Yu, D Song, B Li arXiv preprint arXiv:2402.03181, 2024 | | 2024 |
| COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits M Kang, NM Gürel, L Li, B Li ICLR 2024, 2023 | | 2023 |
Bo LiUniversity of Illinois at Urbana–ChampaignVerified email at illinois.edu
