Publications

Found 50 results
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Filters: Author is Michael W. Mahoney  [Clear All Filters]
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Zhu, R., Ma P., Mahoney M. W., & Yu B. (2015).  Optimal Subsampling Approaches for Large Sample Linear Regression.
Fountoulakis, K., Gleich D., & Mahoney M. W. (2016).  An optimization approach to locally-biased graph algorithms.
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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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Shen, S.., Dong Z.., Ye J.., Ma L.., Yao Z.., Gholami A.., et al. (2020).  Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. Proceedings of the AAAI-20 Conference.
Yang, J., Sindhwani V., Avron H., & Mahoney M. W. (2014).  Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.
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Gallopoulos, E., Drineas P., Ipsen I., & Mahoney M. W. (2016).  RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi. SIAM News.
Drineas, P., & Mahoney M. W. (2016).  RandNLA: Randomized Numerical Linear Algebra. Communications of the ACM. 59, 80-90.
Yang, J., Sindhwani V., Fan Q., Avron H., & Mahoney M. W. (2014).  Random Laplace Feature Maps for Semigroup Kernels on Histograms.
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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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Jeub, L. G. S., Balachandran P., Porter M. A., Mucha P. J., & Mahoney M. W. (2014).  Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks.
Martin, C.. H., & Mahoney M. W. (2019).  Traditional and Heavy-Tailed Self Regularization in Neural Network Models. Proceeding of the 36th ICML Conference. 4284-4293.
Adcock, A. B., Sullivan B. D., & Mahoney M. W. (2014).  Tree Decompositions and Social Graphs.
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Wang, D., Rao S., & Mahoney M. W. (2015).  Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. Proceedings of the 43rd ICALP Conference.
Gleich, D., & Mahoney M. W. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
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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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