| Multichannel Hankel matrix completion through nonconvex optimization S Zhang, Y Hao, M Wang, JH Chow IEEE Journal of Selected Topics in Signal Processing 12 (4), 617-632, 2018 | 39 | 2018 |
| Correction of corrupted columns through fast robust Hankel matrix completion S Zhang, M Wang IEEE Transactions on Signal Processing 67 (10), 2580-2594, 2019 | 38 | 2019 |
| Fast learning of graph neural networks with guaranteed generalizability: one-hidden-layer case S Zhang, M Wang, S Liu, PY Chen, J Xiong ICML, 11268-11277, 2020 | 33 | 2020 |
| Why lottery ticket wins? a theoretical perspective of sample complexity on sparse neural networks S Zhang, M Wang, S Liu, PY Chen, J Xiong NeurIPS 34, 2707-2720, 2021 | 31 | 2021 |
| How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis S Zhang, M Wang, S Liu, PY Chen, J Xiong ICLR, 2022, 2022 | 27* | 2022 |
| Multi-channel missing data recovery by exploiting the low-rank hankel structures S Zhang, Y Hao, M Wang, JH Chow 2017 IEEE 7th International Workshop on Computational Advances in Multi …, 2017 | 19 | 2017 |
| Improved linear convergence of training cnns with generalizability guarantees: A one-hidden-layer case S Zhang, M Wang, J Xiong, S Liu, PY Chen IEEE Transactions on Neural Networks and Learning Systems 32 (6), 2622-2635, 2020 | 17 | 2020 |
| Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks S Zhang, M Wang, PY Chen, S Liu, S Lu, M Liu ICLR, 2023, 2023 | 15 | 2023 |
| Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks MNR Chowdhury, S Zhang, M Wang, S Liu, PY Chen ICML, 2023, 2023 | 12 | 2023 |
| Correction of simultaneous bad measurements by exploiting the low-rank hankel structure S Zhang, M Wang 2018 IEEE International Symposium on Information Theory (ISIT), 646-650, 2018 | 12 | 2018 |
| On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration S Zhang, H Li, M Wang, M Liu, PY Chen, S Lu, S Liu, K Murugesan, ... NeurIPS, 2023, 2023 | 10 | 2023 |
| Review of low-rank data-driven methods applied to synchrophasor measurement M Wang, JH Chow, D Osipov, S Konstantinopoulos, S Zhang, ... IEEE Open Access Journal of Power and Energy 8, 532-542, 2021 | 9 | 2021 |
| Learning and generalization of one-hidden-layer neural networks, going beyond standard gaussian data H Li, S Zhang, M Wang 2022 56th Annual Conference on Information Sciences and Systems (CISS), 37-42, 2022 | 8 | 2022 |
| Guaranteed convergence of training convolutional neural networks via accelerated gradient descent S Zhang, M Wang, S Liu, PY Chen, J Xiong 2020 54th annual conference on information sciences and systems (CISS), 1-6, 2020 | 6 | 2020 |
| How does promoting the minority fraction affect generalization? a theoretical study of one-hidden-layer neural network on group imbalance H Li, S Zhang, Y Zhang, M Wang, S Liu, PY Chen IEEE Journal of Selected Topics in Signal Processing, 2024 | 5 | 2024 |
| A low-rank framework of pmu data recovery and event identification M Wang, JH Chow, Y Hao, S Zhang, W Li, R Wang, P Gao, C Lackner, ... 2019 International Conference on Smart Grid Synchronized Measurements and …, 2019 | 2 | 2019 |
| Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis H Li, M Wang, S Zhang, S Liu, PY Chen arXiv preprint arXiv:2406.17167, 2024 | | 2024 |
| ADR-BC: Adversarial Density Weighted Regression Behavior Cloning Z Zhang, Z Zhuang, D Wang, J Xu, M Liu, S Zhang arXiv preprint arXiv:2405.20351, 2024 | | 2024 |
| SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning S Zhang, HD Fernando, M Liu, K Murugesan, S Lu, PY Chen, T Chen, ... ICML, 2024, 2024 | | 2024 |
| FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-iid Data S Jing, A Yu, S Zhang, S Zhang ICML, 2024, 2024 | | 2024 |
Meng WangAssociate Professor, Rensselaer Polytechnic Institute在 rpi.edu 的电子邮件经过验证
