Learning Polynomial Transformations via Generalized Tensor Decompositions-Reference-Cited by-同舟云学术

Learning Polynomial Transformations via Generalized Tensor Decompositions

Published:2023-06-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 55th Annual ACM Symposium on Theory of Computing
language:
Short-container-title:

Author:

Chen Sitan¹,Li Jerry²,Li Yuanzhi³,Zhang Anru R.⁴

Affiliation:

1. University of California at Berkeley, Berkeley, USA / Harvard University, USA

2. Microsoft Research, USA

3. Microsoft Research, USA / Carnegie Mellon University, USA

4. Duke University, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3564246.3585209

Reference108 articles.

1. Jayadev Acharya Ashkan Jafarpour Alon Orlitsky and Ananda Theertha Suresh. 2014. Near-optimal-sample estimators for spherical gaussian mixtures. arXiv preprint arXiv:1402.4746. Jayadev Acharya Ashkan Jafarpour Alon Orlitsky and Ananda Theertha Suresh. 2014. Near-optimal-sample estimators for spherical gaussian mixtures. arXiv preprint arXiv:1402.4746.

2. On Spectral Learning of Mixtures of Distributions

3. Zeyuan Allen-Zhu and Yuanzhi Li. 2021. Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. arXiv preprint arXiv:2106.02619. Zeyuan Allen-Zhu and Yuanzhi Li. 2021. Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. arXiv preprint arXiv:2106.02619.

4. Joseph Anderson , Mikhail Belkin , Navin Goyal , Luis Rademacher , and James Voss . 2014 . The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures . In Conference on Learning Theory. 1135–1164 . Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, and James Voss. 2014. The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures. In Conference on Learning Theory. 1135–1164.