| Deep learning with Gaussian differential privacy Z Bu, J Dong, Q Long, WJ Su Harvard data science review 2020 (23), 2020 | 135 | 2020 |
| 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 | 27 | 2021 |
| 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 | 23 | 2019 |
| 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 | 22 | 2020 |
| 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 | 15 | 2021 |
| On the convergence and calibration of deep learning with differential privacy Z Bu, H Wang, Q Long International Conference on Machine Learning (Theory and Practice of …, 2021 | 15 | 2021 |
| 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 | 9 | 2021 |
| Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Z Bu, YX Wang, S Zha, G Karypis arXiv preprint arXiv:2206.07136, 2022 | 8 | 2022 |
| Characterizing the SLOPE trade-off: a variational perspective and the Donoho-Tanner limit Z Bu, J Klusowski, C Rush, WJ Su The Annals of Statistics, 2022 | 8 | 2022 |
| 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 | 8 | 2021 |
| The complete LASSO tradeoff diagram H Wang, Y Yang, Z Bu, W Su Advances in Neural Information Processing Systems 33, 20051-20060, 2020 | 7 | 2020 |
| Differentially private bayesian neural networks on accuracy, privacy and reliability Q Zhang, Z Bu, K Chen, Q Long Machine Learning and Knowledge Discovery in Databases: European Conference …, 2023 | 6 | 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, 2022 | 6 | 2022 |
| Efficient designs of SLOPE penalty sequences in finite dimension Y Zhang, Z Bu International Conference on Artificial Intelligence and Statistics, 3277-3285, 2021 | 5 | 2021 |
| Privacy amplification via iteration for shuffled and online PNSGD M Sordello, Z Bu, J Dong Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 3 | 2021 |
| Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity S Xu, Z Bu, P Chaudhari, IJ Barnett International Conference on Learning Representations (PAIR2Struct Workshop), 2022 | 2 | 2022 |
| Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing K Chen, Z Bu, S Xu Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 1* | 2021 |
| Differentially Private Optimizers Can Learn Adversarially Robust Models Y Zhang, Z Bu arXiv preprint arXiv:2211.08942, 2022 | | 2022 |
| Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation Z Bu arXiv preprint arXiv:2211.04973, 2022 | | 2022 |
| Differentially Private Optimization on Large Model at Small Cost Z Bu, YX Wang, S Zha, G Karypis arXiv preprint arXiv:2210.00038, 2022 | | 2022 |
Weijie J. SuAssociate Professor, Wharton and Computer Science, University of PennsylvaniaBestätigte E-Mail-Adresse bei wharton.upenn.edu
