Publications

Found 50 results
Author Title [ Type(Desc)] Year
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Conference Paper
Gleich, D., & Mahoney M. W. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
Yang, J., Chow Y-L., Re C., & Mahoney M. W. (2015).  Weighted SGD for ℓp Regression with Randomized Preconditioning. Proceedings of the 27th Annual SODA Conference. 558-569.
Journal Article
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M. W., et al. (2017).  DCAR: A Discriminative and Compact Audio Representation for Audio Processing. IEEE Transactions on Multimedia. PP(99), 
Yang, J., Rübel O., Prabhat, Mahoney M. W., & Bowen B. P. (2015).  Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions. Analytical Chemistry. 87(9), 4658-4666.
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.
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.
Drineas, P., & Mahoney M. W. (2016).  RandNLA: Randomized Numerical Linear Algebra. Communications of the ACM. 59, 80-90.
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.
Roosta-Khorasani, F.., & Mahoney M. W. (2019).  Sub-Sampled Newton Methods. Mathematical Programming. 293-326.
Miscellaneous
Fountoulakis, K., Chen X., Shun J., Roosta-Khorasani F., & Mahoney M. W. (2016).  Exploiting Optimization for Local Graph Clustering.
Wang, D., Mahoney M. W., Mohan N., & Rao S. (2015).  Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent.
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.

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