Publications

Found 73 results
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N. Erichson, B., Mathelin L., Yao Z., Bruntonq S. L., Mahoney M., & J. Kutz N. (2020).  Shallow neural networks for fluid flow reconstruction with limited sensors. Proceedings of the Royal Society A. 476(2238), 
Fountoulakis, K.., Gleich D.. F., & Mahoney M. (2018).  A Short Introduction to Local Graph Clustering Methods and Software. Abstracts of the 7th International Conference on Complex Networks and Their Applications.
Giannakis, G. B., Bach F., Cendrillon R., Mahoney M., & 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. (2016).  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Chasins, S., Cheung A., Crooks N., Ghodsi A., Goldberg K., Gonzalez J. E., et al. (2022).  The Sky Above the Clouds: A Berkeley View on the Future of Cloud Computing. arxiv.org.
Andersen, D. G., Du S. S., Mahoney M., Melgaard C., Wu K., & Gu M. (2015).  Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrom Method.
Kim, S., Gholami A., Shaw A., Lee N., Mangalam K., Malik J., et al. (2022).  Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. NeurIPS.
Martin, C.. H., & Mahoney M. (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. (2014).  A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
Mahoney, M., & 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., & Darve E. (2015).  Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation.
Roosta-Khorasani, F.., & Mahoney M. (2019).  Sub-Sampled Newton Methods. Mathematical Programming. 293-326.
Roosta-Khorasani, F., & Mahoney M. (2016).  Sub-Sampled Newton Methods I: Globally Convergent Algorithms.
Roosta-Khorasani, F., & Mahoney M. (2016).  Sub-Sampled Newton Methods II: Local Convergence Rates.
Xu, P., Yang J., Roosta-Khorasani F., Re C., & Mahoney M. (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. (2015).  Weighted SGD for ℓp Regression with Randomized Preconditioning. Proceedings of the 27th Annual SODA Conference. 558-569.

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