Found 39 results
Author Title Type [ Year(Asc)]
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Mahoney, M. W., 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 M. W. (2016).  Avoiding communication in primal and dual block coordinate descent methods.
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M. W., et al. (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 M. W. (2016).  Exploiting Optimization for Local Graph Clustering.
Yang, J., Mahoney M. W., Saunders M. A., & Sun Y. (2016).  Feature-distributed sparse regression: a screen-and-clean approach. Proceedings of the 2016 NIPS Conference.
Chen, X., Roosta-Khorasani F., Bartlett P. L., & Mahoney M. W. (2016).  FLAG: Fast Linearly-Coupled Adaptive Gradient Method.
Lawlor, D., Budavári T., & Mahoney M. W. (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., et al. (2016).  Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies.
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.
Fountoulakis, K., Gleich D., & Mahoney M. W. (2016).  An optimization approach to locally-biased graph algorithms.
Shun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. W. (2016).  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12), 
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.
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.
Mahoney, M. W., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
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.