Mingchen Li
Mingchen Li
University of Michigan, Phd candidate
E-mail megerősítve itt: umich.edu - Kezdőlap
Hivatkozott rá
Hivatkozott rá
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
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
FedNest: Federated bilevel, minimax, and compositional optimization
DA Tarzanagh, M Li, C Thrampoulidis, S Oymak
International Conference on Machine Learning, 21146-21179, 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
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
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
Generalization guarantees for neural architecture search with train-validation split
S Oymak, M Li, M Soltanolkotabi
International Conference on Machine Learning, 8291-8301, 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
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
Provable and efficient continual representation learning
Y Li, M Li, MS Asif, S Oymak
arXiv preprint arXiv:2203.02026, 2022
Exploring weight importance and hessian bias in model pruning
M Li, Y Sattar, C Thrampoulidis, S Oymak
arXiv preprint arXiv:2006.10903, 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
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
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
X Zhang, M Li, J Chen, C Thrampoulidis, S Oymak
AAAI 2024, 2024
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 …, 2023
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation
DA Tarzanagh, M Li, P Sharma, S Oymak
arXiv preprint arXiv:2306.01648, 2023
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–16