Publications

Found 58 results
Author [ Title(Desc)] Type Year
Filters: Author is M. W. Mahoney  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
M
Mahoney, M. W., Duchi J. C., & Gilbert A. C. (2018).  The Mathematics of Data. 25,
Gittens, A., Devarakonda A., Racah E., Ringenburg M., Gerhardt L., Kottalam J., et al. (2016).  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
Derezinski, M.., Clarkson K.. L., Mahoney M. W., & Warmuth M.. K. (2019).  Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. Proceedings of 2019 COLT.
Gleich, D., & Mahoney M. W. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.
Gittens, A., Kottalam J., Yang J., Ringenburg M. F., Chhugani J., Racah E., et al. (2016).  A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark. Proceedings of the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics.
N
Mahoney, M. W. (2014).  A New Spin on an Old Algorithm: Technical Perspective on "Communication Costs of Strassen's Matrix Multiplication". Communications of the ACM. 57(2), 106.
O
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.
P
Shun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. W. (2016).  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12), 
Q
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.
R
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.
S
Fountoulakis, K.., Gleich D.. F., & Mahoney M.. W. (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. 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.

Pages