Publications

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Shun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. W. (2016).  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12), 
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Giannakis, G. B., Bach F., Cendrillon R., Mahoney M. W., & Neville J. (2014).  Signal Processing for Big Data (Editorial for Special Issue). IEEE Signal Processing Magazine. 31, 15-16.
Veldt, N., Gleich D., & Mahoney M. W. (2016).  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Andersen, D. G., Du S. S., Mahoney M. W., Melgaard C., Wu K., & Gu M. (2015).  Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrom Method.
Martin, C.. H., & Mahoney M. W. (2019).  Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. Proceedings of the 25th Annual SIGKDD. 3239-3240.
Raskutti, G., & Mahoney M. W. (2014).  A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
Mahoney, M. W., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
Wang, R., Li Y., Mahoney M. W., & Darve E. (2015).  Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation.
Roosta-Khorasani, F.., & Mahoney M. W. (2019).  Sub-Sampled Newton Methods. Mathematical Programming. 293-326.
Roosta-Khorasani, F., & Mahoney M. W. (2016).  Sub-Sampled Newton Methods I: Globally Convergent Algorithms.
Roosta-Khorasani, F., & Mahoney M. W. (2016).  Sub-Sampled Newton Methods II: Local Convergence Rates.
Xu, P., Yang J., Roosta-Khorasani F., Re C., & Mahoney M. W. (2016).  Sub-sampled Newton Methods with Non-uniform Sampling. Proceedings of the 2016 NIPS Conference.
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Yang, J., Chow Y-L., Re C., & Mahoney M. W. (2015).  Weighted SGD for ℓp Regression with Randomized Preconditioning. Proceedings of the 27th Annual SODA Conference. 558-569.

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