Publications

Found 73 results
Author Title [ Type(Asc)] Year
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Journal Article
Yang, J., Rübel O., Prabhat, Mahoney M., & Bowen B. P. (2015).  Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions. Analytical Chemistry. 87(9), 4658-4666.
Liu, B.., Jing L.., Li J.., Yu J.., Gittens A.., & Mahoney M. (2018).  Group Collaborative Representation for Image Set Classification. International Journal of Computer Vision. 1-26.
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M., et al. (2017).  DCAR: A Discriminative and Compact Audio Representation for Audio Processing. IEEE Transactions on Multimedia. PP(99), 
Gittens, A.., Rothauge K.., Mahoney M., Wang S.., Gerhardt L.., Prabhat, et al. (2018).  Alchemist: An Apache Spark <=> MPI Interface. Concurrency and Computation: Practice and Experience (Special Issue of the Cray User Group, CUG 2018), e5026.
Conference Paper
Yang, J., Chow Y-L., Re C., & Mahoney M. (2015).  Weighted SGD for ℓp Regression with Randomized Preconditioning. Proceedings of the 27th Annual SODA Conference. 558-569.
Gleich, D., & Mahoney M. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
Wang, D., Rao S., & Mahoney M. (2015).  Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. Proceedings of the 43rd ICALP Conference.
Yao, Z.., Gholami A.., Xu P.., Keutzer K.., & Mahoney M. (2019).  Trust Region Based Adversarial Attack on Neural Networks. Proceedings of the 32nd CVPR Conference. 11350-11359.
Martin, C.. H., & Mahoney M. (2019).  Traditional and Heavy-Tailed Self Regularization in Neural Network Models. Proceeding of the 36th ICML Conference. 4284-4293.
Xu, P., Yang J., Roosta-Khorasani F., Re C., & Mahoney M. (2016).  Sub-sampled Newton Methods with Non-uniform Sampling. Proceedings of the 2016 NIPS Conference.
Martin, C.. H., & Mahoney M. (2019).  Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. Proceedings of the 25th Annual SIGKDD. 3239-3240.
Kim, S., Gholami A., Shaw A., Lee N., Mangalam K., Malik J., et al. (2022).  Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. NeurIPS.
Andersen, D. G., Du S. S., Mahoney M., 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. (2016).  A Simple and Strongly-Local Flow-Based Method for Cut Improvement. Proceedings of the 33rd ICML Conference.
Fountoulakis, K.., Gleich D.. F., & Mahoney M. (2018).  A Short Introduction to Local Graph Clustering Methods and Software. Abstracts of the 7th International Conference on Complex Networks and Their Applications.
Yang, J., Sindhwani V., Fan Q., Avron H., & Mahoney M. (2014).  Random Laplace Feature Maps for Semigroup Kernels on Histograms.
Yang, J., Sindhwani V., Avron H., & Mahoney M. (2014).  Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.
Shen, S.., Dong Z.., Ye J.., Ma L.., Yao Z.., Gholami A.., et al. (2020).  Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. Proceedings of the AAAI-20 Conference.
Shun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. (2016).  Parallel Local Graph Clustering. Proceedings of the VLDB Endowment. 9(12), 
Lim, S. Hoe, N. Erichson B., Hodgkinson L., & Mahoney M. (2021).  Noisy Recurrent Neural Networks. Advances in Neural Information Processing Systems Conference. 34,
N. Erichson, B., Taylor D., Wu Q., & Mahoney M. (2021).  Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 100-108.
Hodgkinson, L., & Mahoney M. (2020).  Multiplicative Noise and Heavy Tails in Stochastic Optimization.
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.
Derezinski, M.., Clarkson K.. L., Mahoney M., & Warmuth M.. K. (2019).  Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. Proceedings of 2019 COLT.
N. Erichson, B., Azencot O., Queiruga A., Hodgkinson L., & Mahoney M. (2021).  Lipschitz recurrent neural networks. International Conference on Learning Representations.

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