Publications

Found 50 results
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Filters: Author is Michael W. Mahoney  [Clear All Filters]
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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.
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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.
Gleich, D., & Mahoney M. W. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.
Derezinski, M.., Clarkson K.. L., Mahoney M. W., & Warmuth M.. K. (2019).  Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. Proceedings of 2019 COLT.
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.
Mahoney, M. W., Duchi J. C., & Gilbert A. C. (2018).  The Mathematics of Data. 25,
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.
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Jeub, L. G. S., Mahoney M. W., Mucha P. J., & Porter M. A. (2015).  A Local Perspective on Community Structure in Multilayer Networks.
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Ma, L.., Montague G.., Ye J.., Yao Z.., Gholami A.., Keutzer K.., et al. (2020).  Inefficiency of K-FAC for Large Batch Size Training. Proceedings of the AAAI-20 Conference.
Yang, J., Meng X., & Mahoney M. W. (2015).  Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments.
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.
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Martin, C.. H., & Mahoney M. W. (2020).  Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. Proceedings of 2020 SDM Conference.
Dong, Z.., Yao Z.., Gholami A.., Mahoney M. W., & Keutzer K.. (2019).  HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision. Proceedings of ICCV 2019.
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Chen, X., Roosta-Khorasani F., Bartlett P. L., & Mahoney M. W. (2016).  FLAG: Fast Linearly-Coupled Adaptive Gradient Method.
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.
Wang, D., Mahoney M. W., Mohan N., & Rao S. (2015).  Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent.
Alaou, A. El, & Mahoney M. W. (2014).  Fast Randomized Kernel Methods With Statistical Guarantees.
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Fountoulakis, K., Chen X., Shun J., Roosta-Khorasani F., & Mahoney M. W. (2016).  Exploiting Optimization for Local Graph Clustering.
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Derezinski, M.., & Mahoney M. W. (2019).  Distributed estimation of the inverse Hessian by determinantal averaging. Proceedings of the 2019 NeurIPS 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.
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), 
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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.
Zhang, T.., Yao Z.., Gholami A.., Keutzer K.., Gonzalez J.., Biros G.., et al. (2019).  ANODEV2: A Coupled Neural ODE Evolution Framework. Proceedings of the 2019 NeurIPS Conference.

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