Found 39 results
Author Title Type [ Year(Asc)]
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Mahoney MW, Rao S, Wang D, Zhang P.  2016.  Approximating the Solution to Mixed Packing and Covering LPs in parallel time.
Devarakonda A, Fountoulakis K, Demmel J, Mahoney MW.  2016.  Avoiding communication in primal and dual block coordinate descent methods.
Jing L, Liu B, Choi J, Janin A, Bernd J, Mahoney MW, Friedland G.  2016.  A discriminative and compact audio representation for event detection. Proceedings of the 2016 ACM Conference on Multimedia (MM '16). :57-61.
Fountoulakis K, Chen X, Shun J, Roosta-Khorasani F, Mahoney MW.  2016.  Exploiting Optimization for Local Graph Clustering.
Yang J, Mahoney MW, Saunders MA, Sun Y.  2016.  Feature-distributed sparse regression: a screen-and-clean approach. Proceedings of the 2016 NIPS Conference.
Chen X, Roosta-Khorasani F, Bartlett PL, Mahoney MW.  2016.  FLAG: Fast Linearly-Coupled Adaptive Gradient Method.
Lawlor D, Budavári T, Mahoney MW.  2016.  Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data. The Astrophysical Journal.
Gittens A, Devarakonda A, Racah E, Ringenburg M, Gerhardt L, Kottalam J, Liu J, Maschhoff K, Canon S, Chhugani J et al..  2016.  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
Gleich DF, Mahoney MW.  2016.  Mining Large graphs. Handbook of Big Data. :191-220.
Gittens A, Kottalam J, Yang J, Ringenburg MF, Chhugani J, Racah E, Singh M, Yao Y, Fischer C, Ruebel O 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.
Fountoulakis K, Gleich D, Mahoney MW.  2016.  An optimization approach to locally-biased graph algorithms.
Shun J, Roosta-Khorasani F, Fountoulakis K, Mahoney MW.  2016.  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12)
Gallopoulos E, Drineas P, Ipsen I, Mahoney MW.  2016.  RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi. SIAM News. (January/February 2016)
Drineas P, Mahoney MW.  2016.  RandNLA: Randomized Numerical Linear Algebra. Communications of the ACM. 59:80-90.
Veldt N, Gleich DF, Mahoney MW.  2016.  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Mahoney MW, Drineas P.  2016.  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. :137-154.
Roosta-Khorasani F, Mahoney MW.  2016.  Sub-Sampled Newton Methods I: Globally Convergent Algorithms.
Roosta-Khorasani F, Mahoney MW.  2016.  Sub-Sampled Newton Methods II: Local Convergence Rates.
Xu P, Yang J, Roosta-Khorasani F, Re C, Mahoney MW.  2016.  Sub-sampled Newton Methods with Non-uniform Sampling. Proceedings of the 2016 NIPS Conference.