| Deep learning with Gaussian differential privacy Z Bu, J Dong, Q Long, WJ Su Harvard data science review 2020 (23), 2020 | 237 | 2020 |
| Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Z Bu, YX Wang, S Zha, G Karypis Advances in Neural Information Processing Systems 36, 2023 | 66 | 2023 |
| Scalable and efficient training of large convolutional neural networks with differential privacy Z Bu*, J Mao*, S Xu Advances in Neural Information Processing Systems 35, 38305-38318, 2022 | 59 | 2022 |
| Accuracy, interpretability, and differential privacy via explainable boosting H Nori, R Caruana, Z Bu, JH Shen, J Kulkarni International Conference on Machine Learning, 8227-8237, 2021 | 52 | 2021 |
| Differentially Private Bias-Term Fine-tuning of Foundation Models Z Bu, YX Wang, S Zha, G Karypis International Conference on Machine Learning, 2024 | 51* | 2024 |
| On the convergence and calibration of deep learning with differential privacy Z Bu, H Wang, Z Dai, Q Long Transactions on Machine Learning Research (2023)., 2021 | 49 | 2021 |
| Differentially private optimization on large model at small cost Z Bu, YX Wang, S Zha, G Karypis International Conference on Machine Learning, 3192-3218, 2023 | 47 | 2023 |
| Fast and memory efficient differentially private-SGD via JL projections Z Bu, S Gopi, J Kulkarni, YT Lee, H Shen, U Tantipongpipat Advances in Neural Information Processing Systems 34, 19680-19691, 2021 | 45 | 2021 |
| Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing Z Bu, JM Klusowski, C Rush, WJ Su IEEE Transactions on Information Theory 67 (1), 506-537, 2020 | 36 | 2020 |
| Algorithmic analysis and statistical estimation of SLOPE via approximate message passing Z Bu, J Klusowski, C Rush, W Su Advances in Neural Information Processing Systems 32, 2019 | 31 | 2019 |
| Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems Z Dai, Z Bu, Q Long 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 29 | 2021 |
| Sparse neural additive model: Interpretable deep learning with feature selection via group sparsity S Xu, Z Bu, P Chaudhari, IJ Barnett Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 21 | 2023 |
| A dynamical view on optimization algorithms of overparameterized neural networks Z Bu, S Xu, K Chen International Conference on Artificial Intelligence and Statistics, 3187-3195, 2021 | 15 | 2021 |
| Differentially private bayesian neural networks on accuracy, privacy and reliability Q Zhang*, Z Bu*, K Chen, Q Long Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 14* | 2022 |
| The complete LASSO tradeoff diagram H Wang, Y Yang, Z Bu, W Su Advances in Neural Information Processing Systems 33, 20051-20060, 2020 | 12 | 2020 |
| Characterizing the SLOPE trade-off: A variational perspective and the Donoho–Tanner limit Z Bu, JM Klusowski, C Rush, WJ Su The Annals of Statistics 51 (1), 33-61, 2023 | 11 | 2023 |
| Differentially private SGD without clipping bias: An error-feedback approach X Zhang, Z Bu, ZS Wu, M Hong International Conference on Learning Representations, 2023 | 8 | 2023 |
| Privacy amplification via iteration for shuffled and online PNSGD M Sordello, Z Bu, J Dong Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 8 | 2021 |
| Efficient designs of SLOPE penalty sequences in finite dimension Y Zhang, Z Bu International Conference on Artificial Intelligence and Statistics, 3277-3285, 2021 | 7 | 2021 |
| CEDAR: communication efficient distributed analysis for regressions C Chang, Z Bu, Q Long Biometrics 79 (3), 2357-2369, 2023 | 5 | 2023 |
Qi LongProfessor, University of Pennsylvaniaupenn.edu의 이메일 확인됨