| Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks M Li, M Soltanolkotabi, S Oymak International conference on artificial intelligence and statistics, 4313-4324, 2020 | 429 | 2020 |
| Generalization guarantees for neural networks via harnessing the low-rank structure of the jacobian S Oymak, Z Fabian, M Li, M Soltanolkotabi arXiv preprint arXiv:1906.05392, 2019 | 96 | 2019 |
| FedNest: Federated bilevel, minimax, and compositional optimization DA Tarzanagh, M Li, C Thrampoulidis, S Oymak International Conference on Machine Learning, 21146-21179, 2022 | 76 | 2022 |
| Autobalance: Optimized loss functions for imbalanced data M Li, X Zhang, C Thrampoulidis, J Chen, S Oymak Advances in Neural Information Processing Systems 34, 3163-3177, 2021 | 72 | 2021 |
| Robust 3-d human detection in complex environments with a depth camera L Tian, M Li, Y Hao, J Liu, G Zhang, YQ Chen IEEE Transactions on Multimedia 20 (9), 2249-2261, 2018 | 45 | 2018 |
| On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake? YC Chan, M Li, S Oymak ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 28 | 2021 |
| Generalization guarantees for neural architecture search with train-validation split S Oymak, M Li, M Soltanolkotabi International Conference on Machine Learning, 8291-8301, 2021 | 23 | 2021 |
| Reliably detecting humans in crowded and dynamic environments using RGB-D camera L Tian, G Zhang, M Li, J Liu, YQ Chen 2016 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2016 | 11 | 2016 |
| Robust human detection with super-pixel segmentation and random ferns classification using RGB-D camera L Tian, M Li, G Zhang, J Zhao, YQ Chen 2017 IEEE International Conference on Multimedia and Expo (ICME), 1542-1547, 2017 | 10 | 2017 |
| Provable and efficient continual representation learning Y Li, M Li, MS Asif, S Oymak arXiv preprint arXiv:2203.02026, 2022 | 9 | 2022 |
| Fedyolo: Augmenting federated learning with pretrained transformers X Zhang, M Li, X Chang, J Chen, AK Roy-Chowdhury, AT Suresh, ... arXiv preprint arXiv:2307.04905, 2023 | 8 | 2023 |
| Exploring weight importance and hessian bias in model pruning M Li, Y Sattar, C Thrampoulidis, S Oymak arXiv preprint arXiv:2006.10903, 2020 | 7 | 2020 |
| Generalization, adaptation and low-rank representation in neural networks S Oymak, Z Fabian, M Li, M Soltanolkotabi 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 581-585, 2019 | 5 | 2019 |
| On the power of convolution augmented transformer M Li, X Zhang, Y Huang, S Oymak arXiv preprint arXiv:2407.05591, 2024 | 2 | 2024 |
| Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective X Zhang, M Li, J Chen, C Thrampoulidis, S Oymak AAAI 2024, 2024 | 2 | 2024 |
| Selective Attention: Enhancing Transformer through Principled Context Control X Zhang, X Chang, M Li, A Roy-Chowdhury, J Chen, S Oymak Advances in Neural Information Processing Systems 37, 11061-11086, 2025 | | 2025 |
| Toward Effective Neural Architectures and Algorithms for Generalizable Deep Learning M Li | | 2024 |
| Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation DA Tarzanagh, M Li, P Sharma, S Oymak arXiv preprint arXiv:2306.01648, 2023 | | 2023 |
| Algorithmic Oversight for Deceptive Reasoning EO Taga, M Li, Y Chen, S Oymak Red Teaming GenAI: What Can We Learn from Adversaries?, 0 | | |
| Augmenting Federated Learning with Pretrained Transformers X Zhang, M Li, X Chang, J Chen, A Roy-Chowdhury, A Suresh, S Oymak International Workshop on Federated Learning in the Age of Foundation Models …, 0 | | |
Samet OymakEECS Prof @ University of Michigan在 umich.edu 的電子郵件地址已通過驗證
