| Learning concise and descriptive attributes for visual recognition A Yan, Y Wang, Y Zhong, C Dong, Z He, Y Lu, WY Wang, J Shang, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 57 | 2023 |
| Towards adaptive residual network training: A neural-ode perspective C Dong, L Liu, Z Li, J Shang International Conference on Machine Learning, 2616-2626, 2020 | 40 | 2020 |
| Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting C Dong, L Liu, J Shang Advances in Neural Information Processing Systems (Oral) 35, 17556-17567, 2022 | 27* | 2022 |
| Robust and interpretable medical image classifiers via concept bottleneck models A Yan, Y Wang, Y Zhong, Z He, P Karypis, Z Wang, C Dong, A Gentili, ... arXiv preprint arXiv:2310.03182, 2023 | 25 | 2023 |
| Bfclass: A backdoor-free text classification framework Z Li, D Mekala, C Dong, J Shang The 2021 Conference on Empirical Methods in Natural Language Processing, 2021 | 23 | 2021 |
| Bridging discrete and backpropagation: Straight-through and beyond L Liu, C Dong, X Liu, B Yu, J Gao Advances in Neural Information Processing Systems (Oral) 36, 2024 | 16 | 2024 |
| Data quality matters for adversarial training: An empirical study C Dong, L Liu, J Shang arXiv preprint arXiv:2102.07437, 2021 | 16* | 2021 |
| LOPS: Learning order inspired pseudo-label selection for weakly supervised text classification D Mekala, C Dong, J Shang The 2022 Conference on Empirical Methods in Natural Language Processing, 2022 | 15 | 2022 |
| Toward student-oriented teacher network training for knowledge distillation C Dong, L Liu, J Shang The Twelfth International Conference on Learning Representations, 2024 | 10* | 2024 |
| Perturbation Deterioration: The Other Side of Catastrophic Overfitting Z Li, L Liu, C Dong, J Shang | 5* | |
| Debiasing made state-of-the-art: Revisiting the simple seed-based weak supervision for text classification C Dong, Z Wang, J Shang The 2023 Conference on Empirical Methods in Natural Language Processing, 2023 | 4 | 2023 |
| " Average" Approximates" First Principal Component"? An Empirical Analysis on Representations from Neural Language Models Z Wang, C Dong, J Shang Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 3 | 2021 |
| Physics-Informed Data Denoising for Real-Life Sensing Systems X Zhang, X Fu, D Teng, C Dong, K Vijayakumar, J Zhang, RR Chowdhury, ... Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems …, 2023 | 2 | 2023 |
| Understand and modularize generator optimization in ELECTRA-style pretraining C Dong, L Liu, H Cheng, J Shang, J Gao, X Liu International Conference on Machine Learning, 8244-8259, 2023 | 2 | 2023 |
| SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank D Mekala, A Samavedhi, C Dong, J Shang Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 | 2 | 2023 |
| Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs S Zhou, F Yao, C Dong, Z Wang, J Shang Findings of the Association for Computational Linguistics: ACL 2024, 2024 | 1 | 2024 |
| Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation C An, S Imani, F Yao, C Dong, A Abbasi, H Shrivastava, S Buss, J Shang, ... arXiv preprint arXiv:2411.00863, 2024 | | 2024 |
| Decoupled optimization of models during pretraining X Liu, D Chengyu, L Liu, H Cheng, J Gao US Patent App. 18/212,491, 2024 | | 2024 |
| When is the consistent prediction likely to be a correct prediction? A Nguyen, D Mekala, C Dong, J Shang arXiv preprint arXiv:2407.05778, 2024 | | 2024 |
| Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification L Peng, Y Gu, C Dong, Z Wang, J Shang The 2024 Conference on Empirical Methods in Natural Language Processing, 2024 | | 2024 |
Jingbo ShangAssociate Professor, UC San DiegoVerified email at ucsd.edu
