| Cyclical stochastic gradient MCMC for Bayesian deep learning R Zhang, C Li, J Zhang, C Chen, AG Wilson ICLR 2020, 2019 | 287 | 2019 |
| Towards Fair Federated Learning with Zero-Shot Data Augmentation W Hao, M El-Khamy, J Lee, J Zhang, KJ Liang, C Chen, LC Duke Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 76 | 2021 |
| FLOP: Federated Learning on Medical Datasets using Partial Networks Q Yang, J Zhang, W Hao, G Spell, L Carin KDD 2021, 2021 | 72 | 2021 |
| Stochastic particle-optimization sampling and the non-asymptotic convergence theory J Zhang, R Zhang, L Carin, C Chen International Conference on Artificial Intelligence and Statistics, 1877-1887, 2020 | 44 | 2020 |
| Towards Building the Federated GPT: Federated Instruction Tuning J Zhang, S Vahidian, M Kuo, C Li, R Zhang, G Wang, Y Chen 2024 IEEE International Conference on Acoustics, Speech and Signal …, 2023 | 41 | 2023 |
| Rethinking normalization methods in federated learning Z Du, J Sun, A Li, PY Chen, J Zhang, HH Li, Y Chen Proceedings of the 3rd International Workshop on Distributed Machine …, 2022 | 16 | 2022 |
| Why do we need large batch sizes in contrastive learning? A gradient-bias perspective C Chen, J Zhang, Y Xu, L Chen, J Duan, Y Chen, S Tran, B Zeng, ... Thirty-sixth Conference on Neural Information Processing Systems (Neurips 2022), 2022 | 15 | 2022 |
| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction J Zhang, A Li, M Tang, J Sun, X Chen, F Zhang, C Chen, Y Chen, H Li Fortieth International Conference on Machine Learning (ICML 2023), 2022 | 14 | 2022 |
| Variance reduction in stochastic particle-optimization sampling J Zhang, Y Zhao, L Carin, C Chen International Conference on Machine Learning, 11307-11316, 2020 | 10 | 2020 |
| Self-adversarially learned bayesian sampling Y Zhao, J Zhang, C Chen Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5893-5900, 2019 | 6 | 2019 |
| FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices M Tang, J Zhang, M Ma, L DiValentin, A Ding, A Hassanzadeh, H Li, ... arXiv preprint arXiv:2209.03839, 2022 | 5 | 2022 |
| ReAugKD: Retrieval-augmented knowledge distillation for pre-trained language models J Zhang, A Muhamed, A Anantharaman, G Wang, C Chen, K Zhong, ... The 61st Annual Meeting of the Association for Computational Linguistics …, 2023 | 2 | 2023 |
| Next Generation Federated Learning for Edge Devices: An Overview J Zhang, Z Du, J Sun, A Li, M Tang, Y Wu, Z Gao, M Kuo, HH Li, Y Chen 2022 IEEE 8th International Conference on Collaboration and Internet …, 2022 | 2 | 2022 |
| Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer J Zhang, Y Chen, J Chen 2022 IEEE International Conference on Data Mining (ICDM), 2022 | 1 | 2022 |
| Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models J Zhang, J Sun, E Yeats, Y Ouyang, M Kuo, J Zhang, H Yang, H Li arXiv preprint arXiv:2404.02936, 2024 | | 2024 |
| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents Y Jia, S Vahidian, J Sun, J Zhang, V Kungurtsev, NZ Gong, Y Chen arXiv preprint arXiv:2312.01537, 2023 | | 2023 |
| DACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining M Kuo, J Zhang, Y Chen arXiv preprint arXiv:2311.04799, 2023 | | 2023 |
Chen ChangyouAssistant Professor at University at BuffaloVerified email at buffalo.edu
