| Prompt Distribution Learning Y Lu, J Liu, Y Zhang, Y Liu, X Tian CVPR 2022, 2022 | 453 | 2022 |
| Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs Y Chen, Y Zhang, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng NeurIPS 2022, 2022 | 272* | 2022 |
| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Z Yang*, Y Zhang*, Y Zheng, X Tian, H Peng, T Liu, B Han NeurIPS 2023, 2023 | 158 | 2023 |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Z Tang*, Y Zhang*, S Shi, X He, B Han, X Chu ICML 2022, 2022 | 135 | 2022 |
| Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Y Chen, H Yang, Y Zhang, K Ma, T Liu, B Han, J Cheng ICLR 2022, 2022 | 130 | 2022 |
| CausalAdv: Adversarial Robustness Through the Lens of Causality Y Zhang, M Gong, T Liu, G Niu, X Tian, B Han, B Schölkopf, K Zhang ICLR 2022, 2022 | 124 | 2022 |
| Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Y Chen, K Zhou, Y Bian, B Xie, K Ma, Y Zhang, H Yang, B Han, J Cheng ICLR 2023, 2023 | 96 | 2023 |
| Meta Convolutional Neural Networks for Single Domain Generalization C Wan, X Shen, Y Zhang, Z Yin, X Tian, F Gao, J Huang, XS Hua CVPR 2022, 2022 | 70 | 2022 |
| Learning to augment distributions for out-of-distribution detection Q Wang, Z Fang, Y Zhang, F Liu, Y Li, B Han NeurIPS 2023, 2023 | 64 | 2023 |
| Principal Component Adversarial Example Y Zhang, X Tian, Y Li, X Wang, D Tao TIP 2020, 2020 | 63 | 2020 |
| Class-Disentanglement and Applications in Adversarial Detection and Defense K Yang, T Zhou, Y Zhang, X Tian, D Tao NeurIPS 2021, 2021 | 60 | 2021 |
| Out-of-Distribution Detection with Negative Prompts J Nie, Y Zhang, Z Fang, T Liu, B Han, X Tian ICLR 2024, 2024 | 55 | 2024 |
| Watermarking for Out-of-distribution Detection Q Wang, F Liu, Y Zhang, J Zhang, C Gong, T Liu, B Han NeurIPS 2022 (Spotlight), 2022 | 48 | 2022 |
| From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning W Chen, Z Huang, L Xie, B Lin, H Li, L Lu, X Tian, D Cai, Y Zhang, ... ICML 2024, 2024 | 41 | 2024 |
| Hard Sample Matters a Lot in Zero-Shot Quantization H Li*, X Wu, F Lv, D Liao, TH Li, Y Zhang*, B Han, M Tan CVPR 2023, 2023 | 40 | 2023 |
| Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting R Dai, Y Zhang, A Li, T Liu, X Yang, B Han ICLR 2024, 2024 | 37 | 2024 |
| FedImpro: Measuring and Improving Client Update in Federated Learning Z Tang, Y Zhang, S Shi, X Tian, T Liu, B Han, X Chu ICLR 2024, 2024 | 36 | 2024 |
| ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection B Peng, Y Luo, Y Zhang, Y Li, Z Fang ICLR 2024, 2024 | 33 | 2024 |
| Moderately Distributional Exploration for Domain Generalization R Dai, Y Zhang*, Z Fang, B Han, X Tian* ICML 2023, 2023 | 33 | 2023 |
| Invariant Learning via Probability of Sufficient and Necessary Causes M Yang, Z Fang, Y Zhang*, Y Du, F Liu, JF Ton, J Wang* NeurIPS 2023 (Spotlight), 2023 | 31 | 2023 |
Bo HanHKBU / RIKENVerified email at comp.hkbu.edu.hk
