| Robust optimization as data augmentation for large-scale graphs K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 199* | 2022 |
| On the Reliability of Watermarks for Large Language Models J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, K Kong, ... arXiv preprint arXiv:2306.04634, 2023 | 89 | 2023 |
| Data augmentation for meta-learning R Ni, M Goldblum, A Sharaf, K Kong, T Goldstein International Conference on Machine Learning, 8152-8161, 2021 | 86* | 2021 |
| Gradinit: Learning to initialize neural networks for stable and efficient training C Zhu, R Ni, Z Xu, K Kong, WR Huang, T Goldstein Advances in Neural Information Processing Systems 34, 16410-16422, 2021 | 65 | 2021 |
| VQ-GNN: A universal framework to scale up graph neural networks using vector quantization M Ding, K Kong, J Li, C Zhu, J Dickerson, F Huang, T Goldstein Advances in Neural Information Processing Systems 34, 6733-6746, 2021 | 41* | 2021 |
| A closer look at distribution shifts and out-of-distribution generalization on graphs M Ding, K Kong, J Chen, J Kirchenbauer, M Goldblum, D Wipf, F Huang, ... | 33* | 2021 |
| Shot-vae: semi-supervised deep generative models with label-aware elbo approximations HZ Feng, K Kong, M Chen, T Zhang, M Zhu, W Chen Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7413-7421, 2021 | 29* | 2021 |
| GOAT: A Global Transformer on Large-scale Graphs K Kong, J Chen, J Kirchenbauer, R Ni, CB Bruss, T Goldstein International Conference on Machine Learning 2023, 2023 | 25 | 2023 |
| Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets J Xia, L Gao, K Kong, Y Zhao, Y Chen, X Kui, Y Liang Journal of Visual Languages & Computing 48, 52-60, 2018 | 12 | 2018 |
| Insta-rs: Instance-wise randomized smoothing for improved robustness and accuracy C Chen, K Kong, P Yu, J Luque, T Goldstein, F Huang arXiv preprint arXiv:2103.04436, 2021 | 7* | 2021 |
| OpenTab: Advancing large language models as open-domain table reasoners K Kong, J Zhang, Z Shen, B Srinivasan, C Lei, C Faloutsos, H Rangwala, ... arXiv preprint arXiv:2402.14361, 2024 | 3 | 2024 |
| A Visual Analytics Approach for Traffic Flow Prediction Ensembles. K Kong, Y Ma, C Ye, J Lu, X Chen, W Zhang, W Chen PG (Short Papers and Posters), 61-64, 2018 | 2 | 2018 |
| Towards Generalized and Scalable Machine Learning on Structured Data K Kong | | 2024 |
| Visualization of neo-epidermis formation and evaluation of wound closure using UV fluorescence excitation imaging at two wavelengths (Conference Presentation) Y Wang, W Cai, K Kong, X Jia, RR Anderson, W Franco Photonics in Dermatology and Plastic Surgery 2019 10851, 108510H, 2019 | | 2019 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of Maryland在 cs.umd.edu 的电子邮件经过验证
