Publications

Found 15 results
Author Title Type [ Year(Asc)]
Filters: First Letter Of Title is S and Author is Michael W. Mahoney  [Clear All Filters]
2022
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.
Kim, S., Gholami A., Shaw A., Lee N., Mangalam K., Malik J., et al. (2022).  Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. NeurIPS.
2020
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), 
2019
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.
Roosta-Khorasani, F.., & Mahoney M. (2019).  Sub-Sampled Newton Methods. Mathematical Programming. 293-326.
2018
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.
2016
Veldt, N., Gleich D., & Mahoney M. (2016).  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Mahoney, M., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
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.
2015
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.
Wang, R., Li Y., Mahoney M., & Darve E. (2015).  Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation.
2014
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.
Raskutti, G., & Mahoney M. (2014).  A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.