Provably training overparameterized neural network classifiers with non-convex constraints-Reference-Cited by-同舟云学术

Provably training overparameterized neural network classifiers with non-convex constraints

Published:2022-01-01 Issue:2 Volume:16 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Chen You-Lin¹,Wang Zhaoran²,Kolar Mladen³

Affiliation:

1. Department of Statistics, The University of Chicago, 5747 S. Ellis Ave. Chicago, IL 60637 USA

2. Department of Industrial Engineering and Management Sciences, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA

3. The University of Chicago Booth School of Business, 5807 S. Woodlawn Ave. Chicago, IL 60637 USA

Publisher

Institute of Mathematical Statistics

Subject

Statistics and Probability,Statistics, Probability and Uncertainty

Reference71 articles.

1. AGARWAL, A., BEYGELZIMER, A., DUDIK, M., LANGFORD, J. and WALLACH, H. (2018). A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning (J. DY and A. KRAUSE, eds.). Proceedings of Machine Learning Research 80 60–69. PMLR.

2. AGRAWAL, S., WANG, Z. and YE, Y. (2014). A Dynamic Near-Optimal Algorithm for Online Linear Programming. Operations Research 62 876–890.

3. ALEMOHAMMAD, S., WANG, Z., BALESTRIERO, R. and BARANIUK, R. (2021). The Recurrent Neural Tangent Kernel. In International Conference on Learning Representations.

4. ALLEN-ZHU, Z. and LI, Y. (2019). What Can ResNet Learn Efficiently, Going Beyond Kernels? In Advances in Neural Information Processing Systems (H. WALLACH, H. LAROCHELLE, A. BEYGELZIMER, F. D' ALCHÉ-BUC, E. FOX and R. GARNETT, eds.) 32. Curran Associates, Inc.

5. ALLEN-ZHU, Z., LI, Y. and LIANG, Y. (2019). Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. In Advances in Neural Information Processing Systems (H. WALLACH, H. LAROCHELLE, A. BEYGELZIMER, F. D' ALCHÉ-BUC, E. FOX and R. GARNETT, eds.) 32. Curran Associates, Inc.