Publications

Found 73 results
Author Title [ Type(Desc)] Year
Filters: Author is Michael W. Mahoney  [Clear All Filters]
Book Chapter
Gleich, D., & Mahoney M. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.
Mahoney, M., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
Conference Paper
Gittens, A.., Rothauge K.., Wang S.., Mahoney M., Gerhardt L.., Prabhat, et al. (2018).  Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. Proceedings of the 24th Annual SIGKDD. 293-301.
Chen, J., Zheng L., Yao Z., Wang D., Stoica I., Mahoney M., et al. (2021).  ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training.
Utrera, F., Kravitz E., N. Erichson B., Khanna R., & Mahoney M. (2021).  Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. International Conference on Learning Representations.
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.
Gleich, D., & Mahoney M. (2014).  Anti-Differentiating Approximation Algorithms: A Case Study with Min-Cuts, Spectral, and Flow.
Mahoney, M., Rao S., Wang D., & Zhang P. (2016).  Approximating the Solution to Mixed Packing and Covering LPs in parallel time.
Devarakonda, A., Fountoulakis K., Demmel J., & Mahoney M. (2016).  Avoiding communication in primal and dual block coordinate descent methods.
Krishnapriyan, A. S., Gholami A., Zhe S., Kirby R. M., & Mahoney M. W. (2021).  Characterizing Possible Failure Modes in Physics-Informed Neural Networks. NeurIPS.
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M., et al. (2016).  A discriminative and compact audio representation for event detection. Proceedings of the 2016 ACM Conference on Multimedia (MM '16). 57-61.
Derezinski, M.., & Mahoney M. (2019).  Distributed estimation of the inverse Hessian by determinantal averaging. Proceedings of the 2019 NeurIPS Conference.
Lopes, M.. E., Wang S.., & Mahoney M. (2018).  Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. Proceedings of the 35th ICML Conference. 3223-3232.
Kwon, W., Kim S., Mahoney M. W., Hassoun J., Keutzer K., & Gholami A. (2022).  A Fast Post-Training Pruning Framework for Transformers. NeurIPS.
Yang, J., Mahoney M., Saunders M. A., & Sun Y. (2016).  Feature-distributed sparse regression: a screen-and-clean approach. Proceedings of the 2016 NIPS Conference.
Kylasa, S.. B., Roosta-Khorasani F.., Mahoney M., & Grama A.. (2019).  GPU Accelerated Sub-Sampled Newton's Method. Proceedings of the 2019 SDM Conference. 702-710.
Dong, Z.., Yao Z.., Gholami A.., Mahoney M., & Keutzer K.. (2019).  HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision. Proceedings of ICCV 2019.
Yao, Z., Dong Z., Zheng Z., Gholami A., Yu J., Tan E., et al. (2021).  HAWQV3: Dyadic Neural Network Quantization.
Martin, C.. H., & Mahoney M. (2020).  Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. Proceedings of 2020 SDM Conference.
Yao, Z.., Gholami A.., Lei Q.., Keutzer K.., & Mahoney M. (2018).  Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Proceedings of the 2018 NeurIPS Conference. 4954-4964.
Kim, S., Gholami A., Yao Z., Mahoney M., & Keutzer K. (2021).  I-BERT: Integer-only BERT Quantization.
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.
N. Erichson, B., Azencot O., Queiruga A., Hodgkinson L., & Mahoney M. (2021).  Lipschitz recurrent neural networks. International Conference on Learning Representations.
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.

Pages