Michael Mahoney - Publications
(dblp,
GoogleScholar)
2023
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When are ensembles really effective?,
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R. Theisen, H. Kim, Y. Yang, L. Hodgkinson, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2305.12313 (2023)
(arXiv),
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End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs,
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J. Campos, Z. Dong, J. Duarte, A. Gholami, M. W. Mahoney, J. Mitrevski, and N. Tran,
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Technical Report, Preprint: arXiv:2304.06745 (2023)
(arXiv),
-
Full Stack Optimization of Transformer Inference: a Survey,
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S. Kim, C. Hooper, T. Wattanawong, M. Kang, R. Yan, H. Genc, G. Dinh, Q. Huang, K. Keutzer, M. W. Mahoney, Y. S. Shao, and A. Gholami,
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Technical Report, Preprint: arXiv:2302.14017 (2023)
(arXiv),
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Accepted for publication, Proc. of the ASSYST / MLArchSys 2023 Workshop ().
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Learning Physical Models that Can Respect Conservation Laws,
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D. Hansen, D. C. Maddix, S. Alizadeh, G. Gupta, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2302.11002 (2023)
(arXiv),
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Accepted for publication, Proc. of the 2023 ICML Conference ().
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Big Little Transformer Decoder,
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S. Kim, K. Mangalam, J. Malik, M. W. Mahoney, A. Gholami, and K. Keutzer,
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Technical Report, Preprint: arXiv:2302.07863 (2023)
(arXiv),
2022
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Gated Recurrent Neural Networks with Weighted Time-Delay Feedback,
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N. B. Erichson, S. H. Lim, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2212.00228 (2022)
(arXiv),
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Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems,
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Y. Fang, S. Na, M. W. Mahoney, and M. Kolar,
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Technical Report, Preprint: arXiv:2211.15943 (2022)
(arXiv),
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Randomized Numerical Linear Algebra: A Perspective on the Field With an Eye to Software,
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R. Murray, J. Demmel, M. W. Mahoney, N. B. Erichson, M. Melnichenko, O. A. Malik, L. Grigori, P. Luszczek, M. Dereziński, M. E. Lopes, T. Liang, H. Luo, and J. Dongarra,
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LAWNs (LAPACK Working Notes), UCB/EECS-2022-235 (2022)
(pdf),
-
Technical Report, Preprint: arXiv:2302.11474 (2023)
(arXiv),
-
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes,
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L. Hodgkinson, C. van der Heide, F. Roosta, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2210.07612 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2023 ICML Conference ().
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Gradient Gating for Deep Multi-Rate Learning on Graphs,
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T. K. Rusch, B. P. Chamberlain, M. W. Mahoney, M. M. Bronstein, and S. Mishra,
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Technical Report, Preprint: arXiv:2210.00513 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2023 ICLR Conference
().
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Learning differentiable solvers for systems with hard constraints,
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G. Negiar, M. W. Mahoney, and A. S. Krishnapriyan,
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Technical Report, Preprint: arXiv:2207.08675 (2022)
(arXiv),
-
Accepted for publication, Proc. of the 2023 ICLR Conference
().
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Adaptive Self-supervision Algorithms for Physics-informed Neural Networks,
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S. Subramanian, R. M. Kirby, M. W. Mahoney, and A. Gholami,
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Technical Report, Preprint: arXiv:2207.04084 (2022)
(arXiv),
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GACT: Activation Compressed Training for General Architectures,
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X. Liu, L. Zheng, D. Wang, Y. Cen, W. Chen, X. Han, J. Chen, Z. Liu, J. Tang, J. Gonzalez, M. W. Mahoney, and A. Cheung,
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Technical Report, Preprint: arXiv:2206.11357 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICML Conference ().
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Neurotoxin: Durable Backdoors in Federated Learning,
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Z. Zhang, A. Panda, L. Song, Y. Yang, M. W. Mahoney, J. E. Gonzalez, K. Ramchandran, and P. Mittal,
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Technical Report, Preprint: arXiv:2206.10341 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICML Conference ().
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Squeezeformer: An Efficient Transformer for Automatic Speech Recognition,
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S. Kim, A. Gholami, A. Shaw, N. Lee, K. Mangalam, J. Malik, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2206.00888 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 NeurIPS Conference ().
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Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming,
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S. Na and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2205.13687 (2022)
(arXiv),
-
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows,
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F. Liang, L. Hodgkinson, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2205.07918 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICML Conference ().
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The Sky Above The Clouds,
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S. Chasins, A. Cheung, N. Crooks, A. Ghodsi, K. Goldberg, J. E. Gonzalez, J. M. Hellerstein, M. I. Jordan, A. D. Joseph, M. W. Mahoney, A. Parameswaran, D. Patterson, R. Ada Popa, K. Sen, S. Shenker, D. Song, and I. Stoica,
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Technical Report, Preprint: arXiv:2205.07147 (2022)
(arXiv),
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A Fast Post-Training Pruning Framework for Transformers,
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W. Kwon, S. Kim, M. W. Mahoney, J. Hassoun, K. Keutzer, and A. Gholami,
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Technical Report, Preprint: arXiv:2204.09656 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 NeurIPS Conference ().
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Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence,
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S. Na, M. Derezinski, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2204.09266 (2022)
(arXiv),
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Mathematical Programming, 000(000-000): 000-000 (2022)
(pdf).
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Fast Feature Selection with Fairness Constraints,
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F. Quinzan, R. Khanna, M. Hershcovitch, S. Cohen, D. G. Waddington, T. Friedrich, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2202.13718 (2022)
(arXiv),
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Proc. of the 26th International Conference on AISTATS, PMLR 000:0000-0000 (2023)
().
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AutoIP: A United Framework to Integrate Physics into Gaussian Processes,
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D. Long, Z. Wang, A. Krishnapriyan, R. Kirby, S. Zhe, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2202.12316 (2022)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICML Conference ().
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Learning continuous models for continuous physics,
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A. S. Krishnapriyan, A. F. Queiruga, N. B. Erichson, M. W. Mahoney,
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Technical Report, Preprint: arXiv:2202.08494 (2022)
(arXiv),
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Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data,
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Y. Yang, R. Theisen, L. Hodgkinson, J. E. Gonzalez, K. Ramchandran, C. H. Martin, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2202.02842 (2022)
(arXiv),
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Proc. of the 29th Annual SIGKDD, 0000-0000 (2023)
().
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NoisyMix: Boosting Model Robustness to Common Corruptions,
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N. B. Erichson, S. H. Lim, F. Utrera, W. Xu, Z. Cao, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2202.01263 (2022)
(arXiv),
2021
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Learning from learning machines: a new generation of AI technology to meet the needs of science,
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L. Pion-Tonachini, K. Bouchard, H. G. Martin, S. Peisert, W. B. Holtz, A. Aswani, D. Dwivedi, H. Wainwright, G. Pilania, B. Nachman, B. L. Marrone, N. Falco, Prabhat, D. Arnold, A. Wolf-Yadlin, S. Powers, S. Climer, Q. Jackson, T. Carlson, M. Sohn, P. Zwart, N. Kumar, A. Justice, C. Tomlin, D. Jacobson, G. Micklem, G. V. Gkoutos, P. J. Bickel, J.-B. Cazier, J. Muller, B.-J. Webb-Robertson, R. Stevens, M. Anderson, K. Kreutz-Delgado, M. W. Mahoney, and J. B. Brown,
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Technical Report, Preprint: arXiv:2111.13786 (2021)
(arXiv),
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Long Expressive Memory for Sequence Modeling,
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T. K. Rusch, S. Mishra, N. B. Erichson, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2110.04744 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICLR Conference
().
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Noisy Feature Mixup,
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S. H. Lim, N. B. Erichson, F. Utrera, W. Xu, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2110.02180 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICLR Conference
().
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Inexact Newton-CG Algorithms With Complexity Guarantees,
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Z. Yao, P. Xu, F. Roosta, S. J. Wright, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2109.14016 (2021)
(arXiv),
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Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information,
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M. Jahani, S. Rusakov, Z. Shi, P. Richtarik, M. W. Mahoney, and M. Takac,
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Technical Report, Preprint: arXiv:2109.05198 (2021)
(arXiv),
-
Accepted for publication, Proc. of the 2022 ICLR Conference
().
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What's Hidden in a One-layer Randomly Weighted Transformer?,
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S. Shen, Z. Yao, D. Kiela, K. Keutzer, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2109.03939 (2021)
(arXiv),
-
Accepted for publication, Proc. of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)
().
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Characterizing possible failure modes in physics-informed neural networks,
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A. S. Krishnapriyan, A. Gholami, S. Zhe, R. M. Kirby, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2109.01050 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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Generalization Properties of Stochastic Optimizers via Trajectory Analysis,
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L. Hodgkinson, U. Simsekli, R. Khanna, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2108.00781 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2022 ICML Conference ().
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Taxonomizing local versus global structure in neural network loss landscapes,
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Y. Yang, L. Hodgkinson, R. Theisen, J. Zou, J. E. Gonzalez, K. Ramchandran, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2107.11228 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update,
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M. Derezinski, J. Lacotte, M. Pilanci, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2107.07480 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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Stateful ODE-Nets using Basis Function Expansions,
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A. Queiruga, N. B. Erichson, L. Hodgkinson, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2106.10820 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics,
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C. H. Martin and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2106.00734 (2021)
(arXiv),
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LEAP: Learnable Pruning for Transformer-based Models,
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Z. Yao, X. Wu, L. Ma, S. Shen, K. Keutzer, M. W. Mahoney, and Y. He,
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Technical Report, Preprint: arXiv:2105.14636 (2021)
(arXiv),
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LocalNewton: Reducing Communication Bottleneck for Distributed Learning,
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V. Gupta, A. Ghosh, M. Derezinski, R. Khanna, K. Ramchandran, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2105.07320 (2021)
(arXiv),
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Accepted for publication, Proc. of the 37th UAI Conference 000-000 (2021)
().
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ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training,
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J. Chen, L. Zheng, Z. Yao, D. Wang, I. Stoica, M. W. Mahoney, and J. E. Gonzalez,
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Technical Report, Preprint: arXiv:2104.14129 (2021)
(arXiv),
-
Proc. of the 38th ICML Conference PMLR 139:1803-1813 (2021)
(pdf,
supp).
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Integer-only Zero-shot Quantization for Efficient Speech Recognition,
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S. Kim, A. Gholami, Z. Yao, N. Lee, P. Wang, A. Nrusimha, B. Zhai, T. Gao, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2103.16827 (2021)
(arXiv),
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Accepted for publication, Proc. of the ICASSP 2022 Conference ().
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A Survey of Quantization Methods for Efficient Neural Network Inference,
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A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2103.13630 (2021)
(arXiv),
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Chapter in Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence, pp. 291-326 (2021).
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Hessian Eigenspectra of More Realistic Nonlinear Models,
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Z. Liao and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2103.01519 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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A Differential Geometry Perspective on Orthogonal Recurrent Models,
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O. Azencot, N. B. Erichson, M. Ben-Chen, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2102.09589 (2021)
(arXiv),
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Noisy Recurrent Neural Networks,
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S. H. Lim, N. B. Erichson, L. Hodgkinson, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2102.04877 (2021)
(arXiv),
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Accepted for publication, Proc. of the 2021 NeurIPS Conference ().
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Hessian-Aware Pruning and Optimal Neural Implant,
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S. Yu, Z. Yao, A. Gholami, Z. Dong, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2101.08940 (2021)
(arXiv),
-
Accepted for publication, Proc. of the 2022 WACV Conference ().
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I-BERT: Integer-only BERT Quantization,
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S. Kim, A. Gholami, Z. Yao, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2101.01321 (2021)
(arXiv),
-
Proc. of the 38th ICML Conference PMLR 139:5506-5518 (2021)
(pdf,
supp).
2020
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Sparse sketches with small inversion bias,
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M. Derezinski, Z. Liao, E. Dobriban, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2011.10695 (2020)
(arXiv),
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Accepted for publication, Proc. of the 2021 COLT, 000-000 (2021)
().
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HAWQV3: Dyadic Neural Network Quantization,
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Z. Yao, Z. Dong, Z. Zheng, A. Gholami, J. Yu, E. Tan, L. Wang, Q. Huang, Y. Wang, M. W. Mahoney, and K. Keutzer,
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Technical Report, Preprint: arXiv:2011.10680 (2020)
(arXiv),
-
Proc. of the 38th ICML Conference PMLR 139:11875-11886 (2021)
(pdf,
supp).
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A Statistical Framework for Low-bitwidth Training of Deep Neural Networks,
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J. Chen, Y. Gai, Z. Yao, M. W. Mahoney, and J. E. Gonzalez,
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Technical Report, Preprint: arXiv:2010.14298 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 883-894 (2020)
(pdf).
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Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism,
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V. Gupta, D. Choudhary, P. Tak Peter Tang, X. Wei, X. Wang, Y. Huang, A. Kejariwal, K. Ramchandran, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2010.08899 (2020)
(arXiv),
-
Proc. of the 27th Annual SIGKDD, 2928-2936 (2021)
(pdf).
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MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding,
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Q. Wang, H. Tan, S. Shen, M. W. Mahoney, Z. Yao,
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Technical Report, Preprint: arXiv:2010.05379 (2020)
(arXiv),
-
Accepted for publication, Proc. of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
().
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Sparse Quantized Spectral Clustering,
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Z. Liao, R. Couillet, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2010.01376 (2020)
(arXiv),
-
Proc. of the 2021 ICLR Conference
(pdf).
-
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models,
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Z. Zhang, Z. Yao, Y. Yang, Y. Yan, J. E. Gonzalez, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2008.11364 (2020)
(arXiv),
-
Accepted for publication, Proc. 2021 IEEE BigData, 000-000 (2021)
().
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Continuous-in-Depth Neural Networks,
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A. F. Queiruga, N. B. Erichson, D. Taylor, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2008.02389 (2020)
(arXiv),
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Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware,
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N. B. Erichson, D. Taylor, Q. Wu, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2008.00123 (2020)
(arXiv),
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Proc. 2021 SDM Conference, 000-000 (2021)
(pdf).
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Adversarially-Trained Deep Nets Transfer Better,
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F. Utrera, E. Kravitz, N. B. Erichson, R. Khanna, and M. W. Mahoney,
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Technical Report, Preprint: arXiv:2007.05869 (2020)
(arXiv),
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Proc. of the 2021 ICLR Conference
(pdf).
-
Boundary thickness and robustness in learning models,
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Y. Yang, R. Khanna, Y. Yu, A. Gholami, K. Keutzer, J. E. Gonzalez, K. Ramchandran, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2007.05086 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 6223-6234 (2020)
(pdf).
-
Prospectus for the Next LAPACK and ScaLAPACK Libraries: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC),
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J. Demmel, J. Dongarra, J. Langou, J. Langou, P. Luszczek, and M. W. Mahoney,
-
LAWNs (LAPACK Working Notes), ICL-UT-20-07 (2020)
(pdf).
-
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization,
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M. Derezinski, B. Bartan, M. Pilanci, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2007.01327 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 6684-6695 (2020)
(pdf).
-
Good classifiers are abundant in the interpolating regime,
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R. Theisen, J. M. Klusowski, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.12625 (2020)
(arXiv),
-
Proc. of the 24th International Conference on AISTATS, PMLR 130:3376-3384 (2021)
(pdf).
-
Lipschitz Recurrent Neural Networks,
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N. B. Erichson, O. Azencot, A. Queiruga, L. Hodgkinson, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.12070 (2020)
(arXiv),
-
Proc. of the 2021 ICLR Conference
(pdf).
-
Precise expressions for random projections: Low-rank approximation and randomized Newton,
-
M. Derezinski, F. Liang, Z. Liao, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.10653 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 18272-18283 (2020)
(pdf).
-
Multiplicative noise and heavy tails in stochastic optimization,
-
L. Hodgkinson and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.06293 (2020)
(arXiv),
-
Proc. of the 38th ICML Conference PMLR 139:4262-4274 (2021)
(pdf,
supp).
-
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent,
-
Z. Liao, R. Couillet, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.05013 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 13939-13950 (2020)
(pdf),
-
Journal of Statistical Mechanics, Theory and Experiment 124006 (2021)
(pdf).
-
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning,
-
Z. Yao, A. Gholami, S. Shen, M. Mustafa, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2006.00719 (2020)
(arXiv),
(code),
-
Proc. of the AAAI-21 Conference, 10665-10673 (2021)
(pdf).
-
Determinantal Point Processes in Randomized Numerical Linear Algebra,
-
M. Derezinski and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2005.03185 (2020)
(arXiv),
-
Notices of the AMS, 68 (1) 34-45 (2021)
(pdf).
-
Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance,
-
K. Fountoulakis, M. Liu, D. F. Gleich, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2004.09608 (2020)
(arXiv),
-
Accepted for publication, SIAM Review ().
-
PowerNorm: Rethinking Batch Normalization in Transformers,
-
S. Shen, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer,
-
Technical Report, Preprint: arXiv:2003.07845 (2020)
(arXiv),
-
Proc. of the 37th ICML Conference 4566-4576 (2020)
(pdf).
-
Error Estimation for Sketched SVD via the Bootstrap,
-
M. E. Lopes, N. B. Erichson, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2003.04937 (2020)
(arXiv),
-
Proc. of the 37th ICML Conference 5435-5445 (2020)
(pdf).
-
Forecasting Sequential Data using Consistent Koopman Autoencoders,
-
O. Azencot, N. B. Erichson, V. Lin, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2003.02236 (2020)
(arXiv),
-
Proc. of the 37th ICML Conference 4493-4503 (2020)
(pdf).
-
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms,
-
P. Ma, X. Zhang, X. Xing, J. Ma, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2002.10526 (2020)
(arXiv),
-
Proc. of the 23rd International Conference on AISTATS, PMLR 108:1026-1035 (2020)
(pdf),
-
J. Machine Learning Research, 23(177):1−45, (2022)
(pdf).
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Stochastic Normalizing Flows,
-
L. Hodgkinson, C. van der Heide, F. Roosta, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2002.09547 (2020)
(arXiv),
-
Accepted for publication, Proc. of the 37th UAI Conference 000-000 (2021)
().
-
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method,
-
M. Derezinski, R. Khanna, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:2002.09073 (2020)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 4953-4964 (2020)
(pdf) (Awarded Best Paper Award),
-
Accepted for publication, IJCAI 2021 Sister Conference Best Paper (SCBP) Track
().
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data,
-
C. H. Martin, T. S. Peng, and M. W. Mahoney
-
Technical Report, Preprint: arXiv:2002.06716 (2020)
(arXiv),
(code),
-
Nature Communications, 12, 4122 (2021)
(pdf).
-
ZeroQ: A Novel Zero Shot Quantization Framework,
-
Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer,
-
Technical Report, Preprint: arXiv:2001.00281 (2020)
(arXiv),
(code),
-
Proc. of the 33rd CVPR Conference, 13169-13178 (2020)
(pdf,
supp).
2019
-
PyHessian: Neural Networks Through the Lens of the Hessian,
-
Z. Yao, A. Gholami, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1912.07145 (2019)
(arXiv),
(code),
-
Accepted for publication, Proc. 2020 IEEE BigData, 000-000 (2020)
().
-
Exact expressions for double descent and implicit regularization via surrogate random design,
-
M. Derezinski, F. Liang, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1912.04533 (2019)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 5152-5164 (2020)
(pdf).
-
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data,
-
A. Eshragh, F. Roosta, A. Nazari, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1911.12321 (2019)
(arXiv),
-
J. Machine Learning Research, 23(22):1−36, (2022)
(pdf).
-
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks,
-
Z. Dong, Z. Yao, Y. Cai, D. Arfeen, A. Gholami, M. W. Mahoney, and K. Keutzer,
-
Technical Report, Preprint: arXiv:1911.03852 (2019)
(arXiv),
-
Proc. of the 2020 NeurIPS Conference, 33: 18518-18529 (2020)
(pdf).
-
Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times,
-
K. Rothauge, H. Ayyalasomayajula, K. J. Maschhoff, M. Ringenburg, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1910.01354 (2019)
(arXiv),
(code),
-
Proc. Cray User Group, CUG 2019 (2019)
(pdf).
-
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings,
-
K. Levin, F. Roosta, M. Tang, M. W. Mahoney, and C. E. Priebe,
-
Technical Report, Preprint: arXiv:1910.00423 (2019)
(arXiv),
-
J. Machine Learning Research, 22(194): 1−59, (2021)
(pdf).
-
Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching,
-
M. E. Lopes, N. B. Erichson, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1909.06120 (2019)
(arXiv),
-
Bernoulli Journal, 29(1): 428-450 (2023) ().
-
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT,
-
S. Shen, Z. Dong, J. Ye, L. Ma, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer,
-
Technical Report, Preprint: arXiv:1909.05840 (2019)
(arXiv),
-
Proc. of the AAAI-20 Conference, 8815-8821 (2020)
(pdf).
-
The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1909.03033 (2019)
(arXiv),
-
Appeared in
SIAM News,
SIGACT News,
etc.
-
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks,
-
C. H. Martin and M. W. Mahoney,
-
Proc. of the 25th Annual SIGKDD, 3239-3240 (2019)
(pdf).
-
Geometric Rates of Convergence for Kernel-based Sampling Algorithms,
-
R. Khanna, L. Hodgkinson, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1907.08410 (2019)
(arXiv),
-
Accepted for publication, Proc. of the 37th UAI Conference 000-000 (2021)
().
-
Statistical guarantees for local graph clustering,
-
W. Ha, K. Fountoulakis, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1906.04863 (2019)
(arXiv),
-
Proc. of the 23rd International Conference on AISTATS, PMLR 108:2687-2697 (2020)
(pdf),
-
J. Machine Learning Research, 22(148): 1−54, (2021)
(pdf).
-
ANODEV2: A Coupled Neural ODE Evolution Framework,
-
T. Zhang, Z. Yao, A. Gholami, K. Keutzer, J. Gonzalez, G. Biros, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1906.04596 (2019)
(arXiv),
(code),
-
Proc. of the 2019 NeurIPS Conference, 5151-5161 (2019)
(pdf).
-
Bayesian experimental design using regularized determinantal point processes,
-
M. Derezinski, F. Liang, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1906.04133 (2019)
(arXiv),
-
Proc. of the 23rd International Conference on AISTATS, PMLR 108:3197-3207 (2020)
(pdf,
supp)
(talk).
-
Distributed estimation of the inverse Hessian by determinantal averaging,
-
M. Derezinski and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1905.11546 (2019)
(arXiv),
-
Proc. of the 2019 NeurIPS Conference, 11405-11415 (2019)
(pdf).
-
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization,
-
K. Rothauge, Z. Yao, Z. Hu, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1905.13386 (2019)
(arXiv).
-
Parallel and Communication Avoiding Least Angle Regression,
-
S. Das, J. Demmel, K. Fountoulakis, L. Grigori, M. W. Mahoney, and S. Yang,
-
Technical Report, Preprint: arXiv:1905.11340 (2019)
(arXiv),
-
SIAM J. Scientific Computing, 43(2), C154–C176 (2021)
().
-
Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction,
-
N. B. Erichson, M. Muehlebach, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1905.10866 (2019)
(arXiv),
-
Proc. Second Workshop on Machine Learning and the Physical Sciences, at the 2018 NeurIPS Conference
(pdf).
-
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision,
-
Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer,
-
Technical Report, Preprint: arXiv:1905.03696 (2019)
(arXiv),
-
Proc. ICCV 2019 293-302 (2019)
(pdf).
-
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks,
-
N. B. Erichson, Z. Yao, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1904.03750 (2019)
(arXiv),
-
Proc. of the 9th ICPRAM Conference 103-114 (2020)
(pdf).
-
OverSketched Newton: Fast Convex Optimization for Serverless Systems,
-
V. Gupta, S. Kadhe, T. Courtade, M. W. Mahoney, and K. Ramchandran,
-
Technical Report, Preprint: arXiv:1903.08857 (2019)
(arXiv),
-
Accepted for publication, Proc. 2020 IEEE BigData, 000-000 (2020)
().
-
Inefficiency of K-FAC for Large Batch Size Training,
-
L. Ma, G. Montague, J. Ye, Z. Yao, A. Gholami, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1903.06237 (2019)
(arXiv),
-
Proc. of the AAAI-20 Conference, 5053-5060 (2020)
(pdf).
-
Sub-Sampled Newton Methods,
-
F. Roosta-Khorasani and M. W. Mahoney,
-
Mathematical Programming, 174(1-2): 293-326 (2019)
(pdf).
-
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data,
-
N. B. Erichson, L. Mathelin, Z. Yao, S. L. Brunton, M. W. Mahoney, and J. N. Kutz,
-
Technical Report, Preprint: arXiv:1902.07358 (2019)
(arXiv),
-
Proceedings of the Royal Society A, 476:20200097 (2020)
(pdf).
-
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression,
-
M. Derezinski, K. L. Clarkson, M. W. Mahoney, and M. K. Warmuth,
-
Technical Report, Preprint: arXiv:1902.00995 (2019)
(arXiv),
-
Proc. of 2019 COLT, PMLR 99:1050-1069 (2019)
(pdf).
-
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks,
-
C. H. Martin and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1901.08278 (2019)
(arXiv),
(code),
-
Proc. 2020 SDM Conference, 505-513 (2020)
(pdf).
-
Traditional and Heavy-Tailed Self Regularization in Neural Network Models,
-
C. H. Martin and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1901.08276 (2019)
(arXiv),
(iclr19),
(code),
-
Proc. of the 36th ICML Conference 4284-4293 (2019)
(pdf).
2018
-
Trust Region Based Adversarial Attack on Neural Networks,
-
Z. Yao, A. Gholami, P. Xu, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1812.06371 (2018)
(arXiv),
(code),
-
Proc. of the 32nd CVPR Conference, 11350-11359 (2019)
(pdf).
-
Parameter Re-Initialization through Cyclical Batch Size Schedules,
-
N. Mu, Z. Yao, A. Gholami, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1812.01216 (2018)
(arXiv),
-
Proc. Systems for Machine Learning Workshop, at the 2018 NeurIPS Conference
(pdf).
-
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent,
-
N. Golmant, N. Vemuri, Z. Yao, V. Feinberg, A. Gholami, K. Rothauge, M. W. Mahoney, and J. Gonzalez,
-
Technical Report, Preprint: arXiv:1811.12941 (2018)
(arXiv),
(iclr19).
-
The Mathematics of Data,
-
M. W. Mahoney, J. C. Duchi, and A. C. Gilbert, Eds.
-
AMS, IAS/PCMI, and SIAM (2018)
(web),
(intro).
-
A Short Introduction to Local Graph Clustering Methods and Software,
-
K. Fountoulakis, D. F. Gleich, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1810.07324 (2018)
(arXiv),
-
Absts. of the 7th Intl. Conference on Complex Networks and Their Applications
(pdf),
(code).
-
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning,
-
C. H. Martin and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1810.01075 (2018)
(arXiv),
(code),
-
J. Machine Learning Research, 22(165): 1−73, (2021)
(pdf).
-
Large batch size training of neural networks with adversarial training and second-order information,
-
Z. Yao, A. Gholami, D. Arfeen, R. Liaw, J. Gonzalez, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1810.01021 (2018)
(arXiv),
(iclr19),
(code).
-
Newton-MR: Inexact Newton Method With Minimum Residual Sub-problem Solver,
-
F. Roosta, Y. Liu, P. Xu, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1810.00303 (2018)
(arXiv),
-
EURO Journal on Computational Optimization, 10: 100035 (2022)
(pdf).
-
Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Multiclass Classification Problems,
-
C.-H. Fang, S. B Kylasa, F. Roosta, M. W. Mahoney, and A. Grama,
-
Technical Report, Preprint: arXiv:1807.07132 (2018)
(arXiv),
(code),
-
Proc. SC20 Conference, 50:1-12 (2020)
(pdf).
-
Alchemist: An Apache Spark <=> MPI Interface,
-
A. Gittens, K. Rothauge, M. W. Mahoney, S. Wang, L. Gerhardt, Prabhat, J. Kottalam, M. Ringenburg, and K. Maschhoff,
-
Technical Report, Preprint: arXiv:1806.01270 (2018)
(arXiv),
(code),
-
Concurrency and Computation: Practice and Experience (Special Issue of the Cray User Group, CUG 2018), e5026 (2018)
(pdf).
-
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist,
-
A. Gittens, K. Rothauge, S. Wang, M. W. Mahoney, L. Gerhardt, Prabhat, J. Kottalam, M. Ringenburg, and K. Maschhoff,
-
Technical Report, Preprint: arXiv:1805.11800 (2018)
(arXiv),
-
Proc. of the 24th Annual SIGKDD, 293-301 (2018)
(pdf).
-
Group Collaborative Representation for Image Set Classification,
-
B. Liu, L. Jing, J. Li, J. Yu, A. Gittens, and M. W. Mahoney,
-
International Journal of Computer Vision, 1-26 (2018)
(pdf).
-
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap,
-
M. E. Lopes, S. Wang, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1803.08021 (2018)
(arXiv),
-
Proc. of the 35th ICML Conference 3223-3232 (2018)
(pdf).
-
GPU Accelerated Sub-Sampled Newton's Method,
-
S. B. Kylasa, F. Roosta-Khorasani, M. W. Mahoney, and A. Grama,
-
Technical Report, Preprint: arXiv:1802.09113 (2018)
(arXiv),
(code),
-
Proc. 2019 SDM Conference, 702-710 (2019)
(pdf).
-
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries,
-
Z. Yao, A. Gholami, Q. Lei, K. Keutzer, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1802.08241 (2018)
(arXiv),
-
Proc. of the 2018 NeurIPS Conference, 4954-4964 (2018)
(pdf).
-
Inexact Non-Convex Newton-Type Methods,
-
Z. Yao, P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1802.06925 (2018)
(arXiv),
-
Accepted for publication, INFORMS Journal on Optimization
().
-
Out-of-sample extension of graph adjacency spectral embedding,
-
K. Levin, F. Roosta-Khorasani, M. W. Mahoney, and C. E. Priebe,
-
Technical Report, Preprint: arXiv:1802.06307 (2018)
(arXiv),
-
Proc. of the 35th ICML Conference 2981-2990 (2018)
(pdf).
2017
-
Lectures on Randomized Numerical Linear Algebra,
-
P. Drineas and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1712.08880 (2017)
(arXiv),
-
In: Lectures of the 2016 PCMI Summer School on Mathematics of Data.
-
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization,
-
A. Devarakonda, K. Fountoulakis, J. Demmel, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1712.06047 (2017)
(arXiv),
-
Proc. of the 2018 IPDPS Conference 409-418 (2018)
(pdf).
-
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior,
(click here for a blog about this paper)
-
C. H. Martin and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1710.09553 (2017)
(arXiv),
(iclr18).
-
LASAGNE: Locality And Structure Aware Graph Node Embedding,
-
E. Faerman, F. Borutta, K. Fountoulakis, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1710.06520 (2017)
(arXiv),
-
Proc. 2018 International Conference on Web Intelligence, 246-253 (2018)
(pdf). (Awarded Best Student Paper Award.)
-
A Berkeley View of Systems Challenges for AI,
-
I. Stoica, D. Song, R. A. Popa, D. A. Patterson, M. W. Mahoney, R. H. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. Gonzalez, K. Goldberg, A. Ghodsi, D. E. Culler, and P. Abbeel,
-
Technical Report No. UCB/EECS-2017-159, October 2017
(www),
-
Technical Report, Preprint: arXiv:1712.05855 (2017)
(arXiv).
-
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization,
-
S. Wang, F. Roosta-Khorasani, P. Xu, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1709.03528 (2017)
(arXiv),
(Spark code),
(Python code),
-
Proc. of the 2018 NeurIPS Conference, 2338-2348 (2018)
(pdf).
-
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study,
-
P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1708.07827 (2017)
(arXiv),
(code),
-
Proc. 2020 SDM Conference, 199-207 (2020)
(pdf).
-
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information,
-
P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1708.07164 (2017)
(arXiv),
-
Mathematical Programming, 184: 35-70(2020)
().
-
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication,
-
M. E. Lopes, S. Wang, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1708.01945 (2017)
(arXiv),
-
J. Machine Learning Research, 20(39): 1−40 (2019)
(pdf).
-
Capacity releasing diffusions for speed and locality,
-
D. Wang, K. Fountoulakis, M. Henzinger, M. W. Mahoney, and S. Rao,
-
Technical Report, Preprint: arXiv:1706.05826 (2017)
(arXiv),
-
Proc. of the 34th ICML Conference 3598-3607 (2017)
(pdf,
supp)
(talk).
-
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds,
-
S. Wang, A. Gittens, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1706.02803 (2017)
(arXiv),
-
J. Machine Learning Research, 20(12): 1-49 (2019)
(pdf).
-
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction,
-
K. E. Bouchard, A. F. Bujan, F. Roosta-Khorasani, S. Ubaru, Prabhat, A. M. Snijders, J.-H. Mao, E. F. Chang, M. W. Mahoney, S. Bhattacharyya,
-
Technical Report, Preprint: arXiv:1705.07585 (2017)
(arXiv),
-
Proc. of the 2017 NIPS Conference, 1078-1086 (2017)
(pdf).
-
Skip-Gram - Zipf + Uniform = Vector Additivity,
-
A. Gittens, D. Achlioptas, and M. W. Mahoney,
-
Proc. of the 55th ACL Meeting 69-76 (2017)
(pdf).
-
Principles and Applications of Science of Information [Scanning the Issue],
-
T. Courtade, A. Grama, M. W. Mahoney, and T. Weissman,
-
Proceedings of the IEEE, 105(2): 183-188 (2017)
(pdf).
-
Social Discrete Choice Models,
-
D. Zhang, K. Fountoulakis, J. Cao, M. Yin, M. W. Mahoney, and A. Pozdnoukhov,
-
Technical Report, Preprint: arXiv:1703.07520 (2017)
(arXiv).
-
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging,
-
S. Wang, A. Gittens, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1702.04837 (2017)
(arXiv),
-
Proc. of the 34th ICML Conference 3608-3616 (2017)
(pdf),
-
J. Machine Learning Research, 18(218): 1-50 (2018)
(pdf).
2016
-
Avoiding communication in primal and dual block coordinate descent methods,
-
A. Devarakonda, K. Fountoulakis, J. Demmel, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1612.04003 (2016)
(arXiv),
-
SIAM J. Scientific Computing, 41(1), C1-C27 (2019)
(pdf).
-
Feature-distributed sparse regression: a screen-and-clean approach,
-
J. Yang, M. W. Mahoney, M. A. Saunders, and Y. Sun,
-
Proc. of the 2016 NIPS Conference, 2711-2719 (2016)
(pdf).
-
Multi-label learning with semantic embeddings,
-
L. Jing, M. Cheng, L. Yang, A. Gittens, M. W. Mahoney,
-
ICLR 2017 OpenReview.net
(iclr17).
-
Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data,
-
D. Lawlor, T. Budavari, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1609.03932 (2016)
(arXiv),
-
The Astrophysical Journal, 833:1, 26 (2016)
(pdf).
-
Lecture Notes on Spectral Graph Methods,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1608.04845 (2016)
(arXiv),
-
Lecture Notes on Randomized Linear Algebra,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1608.04481 (2016)
(arXiv),
-
An optimization approach to locally-biased graph algorithms,
-
K. Fountoulakis, D. F. Gleich, M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1607.04940 (2016)
(arXiv),
-
Proceedings of the IEEE, 105(2): 256-272 (2017)
(pdf).
-
DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection,
-
L. Jing, B. Liu, J. Choi, A. Janin, J. Bernd, M. W. Mahoney, and G. Friedland,
-
Technical Report, Preprint: arXiv:1607.04378 (2016)
(arXiv),
-
Proc. of the 2016 ACM Multimedia Conference 57-61 (2016)
(pdf),
-
IEEE Transactions on Multimedia, 19(12): 2637-2650 (2017)
(pdf).
-
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies,
-
A. Gittens, A. Devarakonda, E. Racah, M. Ringenburg, L. Gerhardt, J. Kottalam, J. Liu, K. Maschhoff, S. Canon, J. Chhugani, P. Sharma, J. Yang, J. Demmel, J. Harrell, V. Krishnamurthy, M. W. Mahoney, and Prabhat,
-
Technical Report, Preprint: arXiv:1607.01335 (2016)
(arXiv),
(code),
-
Proc. 2016 IEEE BigData, 204-213 (2016)
(pdf).
-
Sub-sampled Newton Methods with Non-uniform Sampling,
-
P. Xu, J. Yang, F. Roosta-Khorasani, C. Re, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1607.00559 (2016)
(arXiv),
-
Proc. of the 2016 NIPS Conference, 3000-3008 (2016)
(pdf).
-
Approximating the Solution to Mixed Packing and Covering LPs in parallel
time,
-
M. W. Mahoney, S. Rao, D. Wang, and P. Zhang,
-
Proc. of the 43rd ICALP Conference, 52:1-52:14 (2016)
(pdf).
-
A Simple and Strongly-Local Flow-Based Method for Cut Improvement,
-
N. Veldt, D. F. Gleich, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1605.08490 (2016)
(arXiv),
-
Proc. of the 33rd ICML Conference 1938-1947 (2016)
(pdf,
supp).
-
RandNLA: Randomized Numerical Linear Algebra,
-
P. Drineas and M. W. Mahoney,
-
Communications of the ACM, 59, 80-90 (2016)
(pdf).
-
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods,
-
X. Cheng, F. Roosta-Khorasani, S. Palombo, P. L. Bartlett, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1605.08108 (2016)
(arXiv),
-
Proc. of the 21st International Conference on AISTATS, PMLR 84:404-414 (2018)
(pdf,
supp).
-
Parallel Local Graph Clustering,
-
J. Shun, F. Roosta-Khorasani, K. Fountoulakis, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1604.07515 (2016)
(arXiv),
-
Proceedings of the VLDB Endowment, 9(12) 1041-1052 (2016)
(pdf).
-
A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark,
-
A. Gittens, J. Kottalam, J. Yang, M. F. Ringenburg, J. Chhugani, E. Racah, M. Singh, Y. Yao, C. Fischer, O. Ruebel, B. Bowen, N. G. Lewis, M. W. Mahoney, V. Krishnamurthy, and Prabhat,
-
Proc. 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, at IPDPS,
2016
(pdf).
-
Mining Large Graphs,
-
D. F. Gleich and M. W. Mahoney,
-
In
Handbook of Big Data.
pp. 191-220,
edited by
P. Buhlmann, P. Drineas, M. Kane, and M. van de Laan,
Chapman and Hall/CRC Press,
2016
(pdf).
-
Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms,
-
M. W. Mahoney and P. Drineas,
-
In
Handbook of Big Data.
pp. 137-154,
edited by
P. Buhlmann, P. Drineas, M. Kane, and M. van de Laan,
Chapman and Hall/CRC Press,
2016
(pdf).
-
Variational Perspective on Local Graph Clustering,
-
K. Fountoulakis, X. Cheng, J. Shun, F. Roosta-Khorasani and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1602.01886 (2016)
(arXiv),
-
Mathematical Programming, 174(1-2): 553-573 (2019)
(pdf).
-
Sub-Sampled Newton Methods II: Local Convergence Rates,
-
F. Roosta-Khorasani and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1601.04738 (2016)
(arXiv).
-
Sub-Sampled Newton Methods I: Globally Convergent Algorithms,
-
F. Roosta-Khorasani and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1601.04737 (2016)
(arXiv).
-
RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi,
-
E. Gallopoulos, P. Drineas, I. Ipsen, and M. W. Mahoney,
-
SIAM News 49:1 January/February 2016
(web),
(pdf).
2015
-
Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent,
-
D. Wang, M. W. Mahoney, N. Mohan, and S. Rao,
-
Technical Report, Preprint: arXiv:1511.06468 (2015)
(arXiv).
-
A Local Perspective on Community Structure in Multilayer Networks,
-
L. G. S. Jeub, M. W. Mahoney, P. J. Mucha, and M. A. Porter,
-
Technical Report, Preprint: arXiv:1510.05185 (2015)
(arXiv),
-
Network Science, 5(2): 144-163, 2017
(pdf).
-
Optimal Subsampling Approaches for Large Sample Linear Regression,
-
R. Zhu, P. Ma, M. W. Mahoney, and B. Yu,
-
Technical Report, Preprint: arXiv:1509.05111 (2015)
(arXiv).
-
Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction,
-
D. Wang, S. Rao, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1508.02439 (2015)
(arXiv),
-
Proc. of the 43rd ICALP Conference, 50:1-50:13 (2016)
(pdf).
-
Using local spectral methods to robustify graph-based learning algorithms,
-
D. F. Gleich and M. W. Mahoney,
-
Proc. of the 21st Annual SIGKDD, 359-368 (2015)
(pdf)
(code).
-
Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation,
-
R. Wang, Y. Li, M. W. Mahoney, and E. Darve,
-
Technical Report, Preprint: arXiv:1502.03571 (2015)
(arXiv),
-
SIAM J. Matrix Analysis and Applications, 40(4), 1497–1526 (2019)
(pdf).
-
Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions,
-
J. Yang, O. Ruebel, Prabhat, M. W. Mahoney, and B. P. Bowen,
-
Analytical Chemistry, 87 (9), 4658-4666 (2015)
(pdf)
(code).
-
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrom Method,
-
D. G. Anderson, S. S. Du, M. W. Mahoney, C. Melgaard, K. Wu, and M. Gu,
-
Proc. of the 18th International Conference on AISTATS, PMLR 38:19-27 (2015)
(pdf,
supp)
(code).
-
Weighted SGD for Lp Regression with Randomized Preconditioning,
-
J. Yang, Y.-L. Chow, C. Re, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1502.03571 (2015)
(arXiv),
-
Proc. of the 27th Annual SODA, 558-569 (2016)
(pdf),
-
J. Machine Learning Research, 18(211): 1-43 (2018)
(pdf).
-
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments,
-
J. Yang, X. Meng, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1502.03032 (2015)
(arXiv)
(code),
-
Proceedings of the IEEE 104(1): 58-92 (2016)
(pdf).
2014
-
Tree decompositions and social graphs,
-
A. B. Adcock, B. D. Sullivan, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1411.1546 (2014)
(arXiv),
(code).
-
Internet Mathematics, 12(5), 315-361 (2016)
(pdf).
-
Fast Randomized Kernel Methods With Statistical Guarantees,
-
A. El Alaoui and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1411.0306 (2014)
(arXiv),
-
Proc. of the 2015 NIPS Conference, 775-783 (2015)
(pdf).
-
Signal Processing for Big Data (Editorial for Special Issue)
-
G. B. Giannakis, F. Bach, R. Cendrillon, M. Mahoney, and J. Neville,
-
IEEE Signal Processing Magazine, 31: 15-16 (September 2014)
(pdf).
-
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares,
-
G. Raskutti and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1406.5986 (2014)
(arXiv),
-
Proc. of the 32nd ICML Conference, 617-625 (2015)
(pdf),
-
J. Machine Learning Research, 17(214): 1-31, (2016)
(pdf).
-
Random Laplace Feature Maps for Semigroup Kernels on Histograms,
-
J. Yang, V. Sindhwani, Q. Fan, H. Avron, and M. W. Mahoney,
-
Proc. of the 27th CVPR Conference, 971-978 (2014)
(pdf).
-
Anti-differentiating Approximation Algorithms: A case study with Min-cuts, Spectral, and Flow,
-
D. F. Gleich and M. W. Mahoney,
-
Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 1018-1025 (2014)
(pdf)
(code, code)
(talk).
-
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels,
-
J. Yang, V. Sindhwani, H. Avron, and M. W. Mahoney,
-
Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 485-493 (2014)
(pdf),
(code),
-
Technical Report, Preprint: arXiv:1412.8293 (2014)
(arXiv),
-
J. Machine Learning Research, 17(120): 1-38 (2016)
(pdf).
-
Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks,
-
L. G. S. Jeub, P. Balachandran, M. A. Porter, P. J. Mucha, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1403.3795 (2014)
(arXiv),
(code, code),
-
Physical Review E, 91, 012821 (2015)
(pdf).
-
A new spin on an old algorithm: technical perspective on "Communication costs of Strassen's matrix multiplication,"
-
M. W. Mahoney,
-
Communications of the ACM, 57(2): 106 (2014)
(pdf).
2013
-
Tree-like Structure in Large Social and Information Networks,
-
A. B. Adcock, B. D. Sullivan, and M. W. Mahoney,
-
Proc. of the 2013 IEEE ICDM, 1-10 (2013)
(pdf).
-
Objective Identification of Informative Wavelength Regions in Galaxy Spectra,
-
C.-W. Yip, M. W. Mahoney, A. S. Szalay, I. Csabai, T. Budavari, R. F. G. Wyse,
and L. Dobos,
-
Technical Report, Preprint: arXiv:1312.0637 (2013)
(arXiv),
-
Astronomical Journal, 147, 5, 110 (2014)
(pdf).
-
Evaluating OpenMP Tasking at Scale for the Computation of Graph Hyperbolicity,
-
A. B. Adcock, B. D. Sullivan, O. R. Hernandez, and M. W. Mahoney,
-
Proc. of the 9th IWOMP, 71-83 (2013)
(pdf).
-
Frontiers in Massive Data Analysis,
-
Committee on the Analysis of Massive Data, et al. (M. I. Jordan, et al.),
-
The National Academies Press (2013)
(pdf),
(web).
-
A Statistical Perspective on Algorithmic Leveraging,
-
P. Ma, M. W. Mahoney, and B. Yu,
-
Technical Report, Preprint: arXiv:1306.5362 (2013)
(arXiv),
-
Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 91-99 (2014)
(pdf),
-
J. Machine Learning Research, 16, 861-911 (2015)
(pdf).
-
Robust Regression on MapReduce,
-
X. Meng, and M. W. Mahoney,
-
Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 888-896 (2013)
(pdf).
-
Quantile Regression for Large-scale Applications,
-
J. Yang, X. Meng, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1305.0087 (2013)
(arXiv),
(code),
-
Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 881-887 (2013)
(pdf),
-
SIAM J. Scientific Computing, 36(5), S78-S110 (2014)
(pdf).
-
Revisiting the Nystrom Method for Improved Large-Scale Machine Learning,
-
A. Gittens and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1303.1849 (2013)
(arXiv),
(code),
-
Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 567-575 (2013)
(pdf),
-
J. Machine Learning Research, 17(117): 1-65 (2016)
(pdf).
2012
-
Semi-supervised Eigenvectors for Large-scale Locally-biased Learning,
-
T. J. Hansen and M. W. Mahoney,
-
Proc. of the 2012 NIPS Conference, 2528-2536 (2012)
(pdf),
(code),
-
Technical Report, Preprint: arXiv:1304.7528 (2013)
(arXiv),
-
J. Machine Learning Research, 15, 3691-3734 (2014)
(pdf).
-
Low-distortion Subspace Embeddings in Input-sparsity Time and Applications to Robust Linear Regression,
-
X. Meng and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1210.3135 (2012)
(arXiv),
-
Proc. of the 45th STOC, 91-100 (2013)
(pdf).
-
The Fast Cauchy Transform and Faster Robust Linear Regression,
-
K. L. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff,
-
Technical Report, Preprint: arXiv:1207.4684 (2012)
(arXiv),
-
Proc. of the 24th Annual SODA, 466-477 (2013)
(pdf),
-
SIAM J. Computing, 45, 763-810 (2016)
(pdf).
-
rCUR: an R package for CUR matrix decomposition,
-
A. Bodor, I. Csabai, M. W. Mahoney, and N. Solymosi,
-
BMC Bioinformatics, 13:103 (2012)
(pdf),
(code).
-
Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1203.0786 (2012)
(arXiv),
-
Proc. of the 2012 ACM Symposium on Principles of Database Systems, 143-154, 2012
(pdf).
-
On the Hyperbolicity of Small-World and Tree-Like Random Graphs,
-
W. Chen, W. Fang, G. Hu, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1201.1717 (2012)
(arXiv),
-
Proc. of the 23rd ISAAC 278-288 (2012)
(pdf),
-
Internet Mathematics, 9(4), 434-491 (2013)
(pdf).
2011
-
Randomized Dimensionality Reduction for K-means Clustering,
-
C. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas,
-
Technical Report, Preprint: arXiv:1110.2897 (2011)
(arXiv),
-
IEEE Transactions on Information Theory, 61(2), 1045-1062 (2015)
(pdf).
-
Regularized Laplacian Estimation and Fast Eigenvector Approximation,
-
P. O. Perry and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1110.1757 (2011)
(arXiv),
-
Proc. of the 2011 NIPS Conference, 2420-2428 (2011)
(pdf).
-
LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems,
-
X. Meng, M. A. Saunders, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1109.5981 (2011)
(arXiv),
(code),
-
SIAM J. Scientific Computing, 36(2), C95-C118 (2014)
(pdf).
-
Fast approximation of matrix coherence and statistical leverage,
-
P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. P. Woodruff,
-
Technical Report, Preprint: arXiv:1109.3843 (2011)
(arXiv),
-
Proc. of the 29th ICML Conference, 1051-1058 (2012)
(pdf),
-
J. Machine Learning Research, 13, 3475-3506 (2012)
(pdf).
-
Localization on low-order eigenvectors of data matrices,
-
M. Cucuringu and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1109.1355 (2011)
(arXiv).
-
Efficient Genomewide Selection of PCA-Correlated tSNPs for Genotype Imputation,
-
A. Javed, P. Drineas, M. W. Mahoney, and P. Paschou,
-
Annals of Human Genetics, 75, 707-722 (2011)
(pdf).
-
Randomized Algorithms for Matrices and Data,
-
M. W. Mahoney,
-
Foundations and Trends in Machine Learning,
NOW Publishers,
Volume 3, Issue 2, 2011
(now),
-
TR version:
Technical Report, Preprint: arXiv:1104.5557 (2011)
(arXiv).
-
(Abridged version in:
Advances in Machine Learning and Data Mining for Astronomy,
edited by
M. J. Way, et al.,
pp. 647-672,
2012.)
2010
-
Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1012.4231 (2010)
(arXiv),
-
Appeared in
SIGKDD Explorations,
SIGACT News,
ASA-SCGN Newsletter,
and IMS Bulletin.
-
CUR from a Sparse Optimization Viewpoint,
-
J. Bien, Y. Xu, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1011.0413 (2010)
(arXiv),
-
Proc. of the 2010 NIPS Conference, 217-225 (2010)
(ps,
pdf).
-
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis,
-
M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1010.1609 (2010)
(arXiv),
-
In:
Combinatorial Scientific Computing,
pp. 427-469,
edited by
U. Naumann and O. Schenk,
2012.
-
Implementing regularization implicitly via approximate eigenvector computation,
-
M. W. Mahoney and L. Orecchia,
-
Technical Report, Preprint: arXiv:1010.0703 (2010)
(arXiv),
-
Proc. of the 28th ICML Conference, 121-128 (2011)
(pdf)
(talk).
-
Approximating Higher-Order Distances Using Random Projections,
-
P. Li, M. W. Mahoney, and Y. She,
-
Proc. of the 26th UAI Conference, 312-321 (2010)
(ps,
pdf),
-
Technical Report, Preprint: arXiv:1203.3492 (2012)
(arXiv).
-
Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving,
-
P. Drineas and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1005.3097 (2010)
(arXiv).
-
Empirical Comparison of Algorithms for Network Community Detection,
-
J. Leskovec, K. J. Lang, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:1004.3539 (2010)
(arXiv),
-
Proc. of the 19th International WWW, 631-640 (2010)
(ps,
pdf).
2009
-
A Local Spectral Method for Graphs: with Applications to Improving Graph
Partitions and Exploring Data Graphs Locally,
-
M. W. Mahoney, L. Orecchia, and N. K. Vishnoi,
-
Technical Report, Preprint: arXiv:0912.0681 (2009)
(arXiv),
-
J. Machine Learning Research, 13, 2339-2365 (2012)
(ps,
pdf).
-
Unsupervised Feature Selection for the k-means Clustering Problem,
-
C. Boutsidis, M. W. Mahoney, and P. Drineas,
-
Proc. of the 2009 NIPS Conference, 153-161 (2009)
(ps,
pdf).
-
Learning with Spectral Kernels and Heavy-Tailed Data,
-
M. W. Mahoney and H. Narayanan,
-
Technical Report, Preprint: arXiv:0906.4539 (2009)
(arXiv).
-
Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow,
-
K. J. Lang, M. W. Mahoney, and L. Orecchia,
-
Proc. of the 8th International SEA, 197-208 (2009)
(ps,
pdf).
-
CUR Matrix Decompositions for Improved Data Analysis,
-
M. W. Mahoney and P. Drineas,
-
Proc. Natl. Acad. Sci. USA, 106, 697-702 (2009)
(ps,
pdf).
2008
-
An Improved Approximation Algorithm for the Column Subset Selection Problem,
-
C. Boutsidis, M. W. Mahoney, and P. Drineas,
-
Technical Report, Preprint: arXiv:0812.4293 (2008)
(arXiv),
-
Proc. of the 20th Annual SODA, 968-977 (2009)
(ps,
pdf).
-
Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008
-
M. W. Mahoney, L.-H. Lim, and G. E. Carlsson
-
Technical Report, Preprint: arXiv:0812.3702 (2008)
(arXiv),
-
Appeared in
SIGKDD Explorations
(ps,
pdf),
SIAM News
(ps,
pdf),
and
ASA-SCGN Newsletter
(ps,
pdf),
and abridged versions appeared in IMS Bulletin
(ps,
pdf)
and AmStat News.
-
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters,
-
J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:0810.1355 (2008)
(arXiv),
-
Internet Mathematics, 6(1), 29-123 (2009)
(pdf).
-
Unsupervised Feature Selection for Principal Components Analysis,
-
C. Boutsidis, M. W. Mahoney, and P. Drineas,
-
Proc. of the 14th Annual SIGKDD, 61-69 (2008)
(ps,
pdf).
-
Statistical Properties of Community Structure in Large Social and Information Networks,
-
J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney,
-
Proc. of the 17th International WWW, 695-704 (2008)
(ps,
pdf).
2007
-
Faster Least Squares Approximation,
-
P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlos,
-
Technical Report, Preprint: arXiv:0710.1435 (2007)
(arXiv),
-
Numerische Mathematik, 117, 219-249 (2011)
(pdf).
-
PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations,
-
P. Paschou, E. Ziv, E. G. Burchard, S. Choudhry, W. Rodriguez-Cintron, M. W. Mahoney, and P. Drineas,
-
PLoS Genetics, 3, 1672-1686 (2007)
(ps,
pdf).
-
Relative-Error CUR Matrix Decompositions,
-
P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
-
Technical Report, Preprint: arXiv:0708.3696 (2007)
(arXiv),
-
SIAM J. Matrix Analysis and Applications, 30, 844-881 (2008)
(ps,
pdf).
-
Feature Selection Methods for Text Classification,
-
A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. W. Mahoney,
-
Proc. of the 13th Annual SIGKDD, 230-239 (2007)
(ps,
pdf).
-
Sampling Algorithms and Coresets for Lp Regression,
-
A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney,
-
Technical Report, Preprint: arXiv:0707.1714 (2007)
(arXiv),
-
Proc. of the 19th Annual SODA, 932-941 (2008)
(ps,
pdf),
-
SIAM J. Computing, 38, 2060-2078 (2009)
(ps,
pdf).
-
Web Information Retrieval and Linear Algebra Algorithms,
-
A. Frommer, M. W. Mahoney, and D. B. Szyld (Eds.),
-
Proc. of Dagstuhl Seminar 07071, (2007)
(web).
-
Intra- and interpopulation genotype reconstruction from tagging SNPs,
-
P. Paschou, M. W. Mahoney, A. Javed, J. R. Kidd, A. J. Pakstis, S. Gu, K. K. Kidd, and P. Drineas,
-
Genome Research, 17(1), 96-107 (2007)
(ps,
pdf).
2006
-
Bridging the Gap Between Numerical Linear Algebra, Theoretical Computer Science, and Data Applications,
-
G. H. Golub, M. W. Mahoney, P. Drineas, and L.-H. Lim,
-
SIAM News 39:8 October 2006
(ps,
pdf).
-
Randomized Algorithms for Matrices and Massive Data Sets,
-
P. Drineas and M. W. Mahoney,
-
Proc. of the 32nd Annual VLDB, 1269 (2006)
(ps,
pdf).
-
Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods,
-
P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
-
Proc. of the 14th Annual ESA, 304-314 (2006)
(ps,
pdf).
-
Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods,
-
P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
-
Proc. of the 10th Annual RANDOM, 316-326 (2006)
(ps,
pdf).
-
Tensor-CUR Decompositions For Tensor-Based Data,
-
M. W. Mahoney, M. Maggioni, and P. Drineas,
-
Proc. of the 12th Annual SIGKDD, 327-336 (2006)
(ps,
pdf),
-
SIAM J. Matrix Analysis and Applications, 30, 957-987 (2008)
(ps,
pdf).
-
Polynomial Time Algorithm for Column-Row-Based Relative-Error Low-Rank Matrix Approximation,
-
P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
-
Technical Report, DIMACS TR 2006-04 March 2006
(ps,
pdf).
-
Sampling Algorithms for L2 Regression and Applications,
-
P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
-
Proc. of the 17th Annual SODA, 1127-1136 (2006)
(ps,
pdf).
2005
-
A Randomized Algorithm for a Tensor-Based Generalization of the Singular Value Decomposition,
-
P. Drineas and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1327, June 2005
(ps,
pdf),
-
Linear Algebra and its Applications, 420, 553-571 (2007)
(ps,
pdf).
-
On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning,
-
P. Drineas and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1319, April 2005
(ps,
pdf),
-
Proc. of the 18th Annual COLT, 323-337 (2005)
(ps,
pdf),
-
J. Machine Learning Research, 6, 2153-2175 (2005)
(ps,
pdf).
2004
-
Sampling Sub-problems of Heterogeneous Max-Cut Problems and Approximation Algorithms,
-
P. Drineas, R. Kannan, and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1283, April 2004
(ps,
pdf),
-
Proc. of the 22nd Annual STACS, 57-68 (2005)
(ps,
pdf),
-
Random Structures and Algorithms, 32:3, 307-333 (2008)
(ps,
pdf).
-
Fast Monte Carlo Algorithms for Matrices III: Computing an Efficient Approximate Decomposition of a Matrix,
-
P. Drineas, R. Kannan, and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1271, February 2004
(ps,
pdf),
-
SIAM J. Computing, 36, 184-206 (2006)
(ps,
pdf).
-
Fast Monte Carlo Algorithms for Matrices II: Computing Low-Rank Approximations to a Matrix,
-
P. Drineas, R. Kannan, and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1270, February 2004
(ps,
pdf),
-
SIAM J. Computing, 36, 158-183 (2006)
(ps,
pdf).
-
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication,
-
P. Drineas, R. Kannan, and M. W. Mahoney,
-
Technical Report, YALEU/DCS/TR-1269, February 2004
(ps,
pdf),
-
SIAM J. Computing, 36, 132-157 (2006)
(ps,
pdf).
2003
-
Rapid Mixing of Several Markov Chains for a Hard-Core Model,
-
R. Kannan, M. W. Mahoney, and R. Montenegro,
-
Proc. of the 14th Annual ISAAC, 663-675 (2003)
(pdf).
2001
-
Quantum, Intramolecular Flexibility, and Polarizability Effects on the Reproduction of the Density Anomaly of Liquid Water by Simple Potential Functions,
-
M. W. Mahoney and W. L. Jorgensen,
-
J. Chem. Phys., 115, 10758-10768 (2001)
(pdf).
-
Rapid Estimation of Electronic Degrees of Freedom in Monte Carlo Calculations for Polarizable Models of Liquid Water,
-
M. W. Mahoney and W. L. Jorgensen,
-
J. Chem. Phys., 114, 9337-9349 (2001)
(pdf).
-
Diffusion Constant of the TIP5P Model of Liquid Water,
-
M. W. Mahoney and W. L. Jorgensen,
-
J. Chem. Phys., 114, 363-366 (2001)
(pdf).
2000
-
A Five-Site Model for Liquid Water and the Reproduction of the Density Anomaly by Rigid, Nonpolarizable Potential Functions,
-
M. W. Mahoney and W. L. Jorgensen,
-
J. Chem. Phys., 112, 8910-8922 (2000)
(pdf).
1997
-
Repression and Activation of Promoter-Bound RNA Polymerase Activity by Gal Repressor,
-
H. E. Choy, R. R. Hanger, T. Aki, M. Mahoney, K. Murakami, A. Ishihama, and S. Adhya,
-
J. Mol. Biol. 272: 293-300, 1997
(pdf).
-
Discrete Representations of the Protein C-alpha Chain,
-
X. F. de la Cruz, M. W. Mahoney, and B. K. Lee,
-
Fold. & Des. 2: 223-234, 1997
(pdf).
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