Michael Mahoney - Publications

2020

• Determinantal Point Processes in Randomized Numerical Linear Algebra,
M. Derezinski and M. W. Mahoney,
Technical Report, Preprint: arXiv:2005.03185 (2020) (arXiv),
• 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),
• Rethinking Batch Normalization in Transformers,
S. Shen, Z. Yao, A. Gholami, M. W. Mahoney, K. Keutzer
Technical Report, Preprint: arXiv:2003.07845 (2020) (arXiv),
• Error Estimation for Sketched SVD via the Bootstrap,
M. E. Lopes, N. B. Erichson, M. W. Mahoney,
Technical Report, Preprint: arXiv:2003.04937 (2020) (arXiv),
• Forecasting Sequential Data using Consistent Koopman Autoencoders,
O. Azencot, N. B. Erichson, V. Lin, M. W. Mahoney,
Technical Report, Preprint: arXiv:2003.02236 (2020) (arXiv),
• Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms,
P. Ma, X. Zhang, X. Xing, J. Ma, M. W. Mahoney,
Technical Report, Preprint: arXiv:2002.10526 (2020) (arXiv),
Accepted for publication, Proc. of the AISTATS 2020 Conference,
Journal version submitted for publication.
• Stochastic Normalizing Flows,
L. Hodgkinson, C. van der Heide, F. Roosta, M. W. Mahoney,
Technical Report, Preprint: arXiv:2002.09547 (2020) (arXiv),
• Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nystrom method,
M. Derezinski, R. Khanna, and M. W. Mahoney,
Technical Report, Preprint: arXiv:2002.09073 (2020) (arXiv),
• 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),
• 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),
Accepted for publication, Proc. of the 33rd CVPR Conference, (2020).

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),
• 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),
• 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),
• 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),
• 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, 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, C. E. Priebe,
Technical Report, Preprint: arXiv:1910.00423 (2019) (arXiv),
• Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching,
M. E. Lopes, N. B. Erichson, M. W. Mahoney,
Technical Report, Preprint: arXiv:1909.06120 (2019) (arXiv),
• 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, K. Keutzer,
Technical Report, Preprint: arXiv:1909.05840 (2019) (arXiv),
Accepted for publication, Proc. of the AAAI-20 Conference.
• 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).
• On Linear Convergence of Weighted Kernel Herding,
R. Khanna and M. W. Mahoney,
Technical Report, Preprint: arXiv:1907.08410 (2019) (arXiv),
• Statistical guarantees for local graph clustering,
W. Ha, K. Fountoulakis, and M. W. Mahoney,
Technical Report, Preprint: arXiv:1906.04863 (2019) (arXiv),
Accepted for publication, Proc. of the AISTATS 2020 Conference,
Journal version submitted for publication.
• 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),
Accepted for publication, Proc. of the AISTATS 2020 Conference.
• 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, and M. W. Mahoney,
Technical Report, Preprint: arXiv:1905.11340 (2019) (arXiv),
• 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),
• 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),
Accepted for publication, Proc. of the ICPRAM 2020 Conference.
• 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),
• 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),
Accepted for publication, Proc. of the AAAI-20 Conference.
• 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),
Accepted for publication, Proceedings of the Royal Society A.
• 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),
Accepted for publication, Proc. 2020 SDM Conference.
• 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),
Presented in the Systems for Machine Learning Workshop at the 2018 NeurIPS Conference.
• 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),
Journal version submitted for publication.
• 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: Newton's Method Without Smoothness or Convexity,
F. Roosta, Y. Liu, P. Xu, and M. W. Mahoney,
Technical Report, Preprint: arXiv:1810.00303 (2018) (arXiv),
Journal version submitted for publication.
• 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),
• 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)
Journal version submitted for publication.
• 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),
Journal version submitted for publication.
• 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),
Journal version submitted for publication.

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),
Journal version submitted for publication.
• 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 the 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).
Accepted for publication, Proc. 2020 SDM Conference.
• 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),
Accepted for publication, Mathematical Programming ().
• 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. Rubel, 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).