Found 39 results
Author Title [ Type(Asc)] Year
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Conference Paper
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.
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.
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.
Yang, J., Sindhwani V., Fan Q., Avron H., & Mahoney M. W. (2014).  Random Laplace Feature Maps for Semigroup Kernels on Histograms.
Yang, J., Sindhwani V., Avron H., & Mahoney M. W. (2014).  Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.
Shun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. W. (2016).  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12), 
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.
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.
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.
Devarakonda, A., Fountoulakis K., Demmel J., & Mahoney M. W. (2016).  Avoiding communication in primal and dual block coordinate descent methods.
Mahoney, M. W., Rao S., Wang D., & Zhang P. (2016).  Approximating the Solution to Mixed Packing and Covering LPs in parallel time.
Gleich, D., & Mahoney M. W. (2014).  Anti-Differentiating Approximation Algorithms: A Case Study with Min-Cuts, Spectral, and Flow.
Book Chapter
Mahoney, M. W., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
Gleich, D., & Mahoney M. W. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.