Publications

Found 39 results
Author Title [ Type(Desc)] Year
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Miscellaneous
Chen, X., Roosta-Khorasani F., Bartlett P. L., & Mahoney M. W. (2016).  FLAG: Fast Linearly-Coupled Adaptive Gradient Method.
Jeub, L. G. S., Mahoney M. W., Mucha P. J., & Porter M. A. (2015).  A Local Perspective on Community Structure in Multilayer Networks.
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.
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.
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. (2016).  Sub-Sampled Newton Methods I: Globally Convergent Algorithms.
Roosta-Khorasani, F., & Mahoney M. W. (2016).  Sub-Sampled Newton Methods II: Local Convergence Rates.
Technical Report
Alaou, A. El, & Mahoney M. W. (2014).  Fast Randomized Kernel Methods With Statistical Guarantees.
Adcock, A. B., Sullivan B. D., & Mahoney M. W. (2014).  Tree Decompositions and Social Graphs.
Unpublished
Yang, J., Meng X., & Mahoney M. W. (2015).  Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments.
Raskutti, G., & Mahoney M. W. (2014).  A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
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.
Web Article
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.

Pages