New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries

Author:

Bhaskara Aditya1ORCID,Evert Eric2ORCID,Srinivas Vaidehi2ORCID,Vijayaraghavan Aravindan2ORCID

Affiliation:

1. University of Utah, Salt Lake City, USA

2. Northwestern University, Evanston, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference30 articles.

1. Kwangjun Ahn, Dhruv Medarametla, and Aaron Potechin. 2016. Graph Matrices: Norm Bounds and Applications.. arXiv: Combinatorics, https://api.semanticscholar.org/CorpusID:211252816

2. Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT)

3. Animashree Anandkumar, Daniel J. Hsu, and Sham M. Kakade. 2012. A Method of Moments for Mixture Models and Hidden Markov Models. In COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland, Shie Mannor, Nathan Srebro, and Robert C. Williamson (Eds.) (JMLR Proceedings, Vol. 23). JMLR.org, 33.1–33.34. http://proceedings.mlr.press/v23/anandkumar12/anandkumar12.pdf

4. Nima Anari Constantinos Daskalakis Wolfgang Maass Christos Papadimitriou Amin Saberi and Santosh Vempala. 2018. Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. In Advances in Neural Information Processing Systems.

5. Pranjal Awasthi, Alex Tang, and Aravindan Vijayaraghavan. 2021. Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations. In Proceedings of the Neural Information Processing Systems (NeurIPS).

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