Found 58 results
Author Title [ Type(Asc)] Year
Filters: Author is M. W. Mahoney  [Clear All Filters]
Conference Paper
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.
Gleich, D., & Mahoney M. W. (2015).  Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. Proceedings of the 21st Annual SIGKDD.
Wang, D., Rao S., & Mahoney M. W. (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.. W. (2019).  Trust Region Based Adversarial Attack on Neural Networks. Proceedings of the 32nd CVPR Conference. 11350-11359.
Martin, C.. H., & Mahoney M. W. (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. W. (2016).  Sub-sampled Newton Methods with Non-uniform Sampling. Proceedings of the 2016 NIPS Conference.
Martin, C.. H., & Mahoney M. W. (2019).  Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. Proceedings of the 25th Annual SIGKDD. 3239-3240.
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.
Fountoulakis, K.., Gleich D.. F., & Mahoney M.. W. (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. 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.
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. 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.
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.
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.
Yao, Z.., Gholami A.., Lei Q.., Keutzer K.., & Mahoney M.. W. (2018).  Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Proceedings of the 2018 NeurIPS Conference. 4954-4964.
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.
Kylasa, S.. B., Roosta-Khorasani F.., Mahoney M.. W., & Grama A.. (2019).  GPU Accelerated Sub-Sampled Newton's Method. Proceedings of the 2019 SDM Conference. 702-710.
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.
Lopes, M.. E., Wang S.., & Mahoney M.. W. (2018).  Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. Proceedings of the 35th ICML Conference. 3223-3232.
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.