Towards Fair Deep Clustering With Multi-State Protected Variables B Wang, I Davidson ICML Workshop on the Security and Privacy of Machine Learning, 2019 | 27 | 2019 |
GraphFM: Improving Large-Scale GNN Training via Feature Momentum H Yu, L Wang, B Wang, M Liu, T Yang, S Ji International Conference on Machine Learning, 25684-25701, 2022 | 24 | 2022 |
IntSGD: Adaptive Floatless Compression of Stochastic Gradients K Mishchenko, B Wang, D Kovalev, P Richtárik International Conference on Learning Representations, 2022 | 24* | 2022 |
When auc meets dro: Optimizing partial auc for deep learning with non-convex convergence guarantee D Zhu, G Li, B Wang, X Wu, T Yang International Conference on Machine Learning, 27548-27573, 2022 | 23 | 2022 |
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications B Wang, T Yang International Conference on Machine Learning, 2022 | 23 | 2022 |
Riemannian stochastic proximal gradient methods for nonsmooth optimization over the Stiefel manifold B Wang, S Ma, L Xue Journal of Machine Learning Research, 2022 | 20 | 2022 |
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. B Wang, Z Yuan, Y Ying, T Yang J. Mach. Learn. Res. 24, 145:1-145:46, 2023 | 15* | 2023 |
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques B Wang, M Safaryan, P Richtárik Conference on Neural Information Processing Systems (NeurIPS), 2022 | 11* | 2022 |
Everything perturbed all at once: Enabling differentiable graph attacks H Liu, B Wang, J Wang, X Dong, T Yang, J Caverlee TheWebConf, Short Paper Track, 2023 | 1 | 2023 |
ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization B Wang, T Yang arXiv preprint arXiv:2312.02277, 2023 | | 2023 |