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ORCIDMichael W. Mahoney
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- affiliation: University of California, Berkeley, Department of Statistics
- affiliation: Stanford University, Department of Mathematics
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2020 – today
- 2024
[j63]Amir Gholami
, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer:
AI and Memory Wall. IEEE Micro 44(3): 33-39 (2024)- [j62]Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar:
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. SIAM J. Optim. 34(2): 2007-2037 (2024) - [j61]Javier Campos, Jovan Mitrevski, Nhan Tran, Zhen Dong, Amir Gholaminejad, Michael W. Mahoney, Javier M. Duarte:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. ACM Trans. Reconfigurable Technol. Syst. 17(3): 36:1-36:22 (2024) - [c154]Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael W. Mahoney, Kurt Keutzer, Amir Gholami:
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. ACL (Findings) 2024: 6498-6526 - [c153]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS 2024: 2413-2421 - [c152]N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney:
NoisyMix: Boosting Model Robustness to Common Corruptions. AISTATS 2024: 4033-4041 - [c151]Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot:
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. ICLR 2024 - [c150]Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson:
Robustifying State-space Models for Long Sequences via Approximate Diagonalization. ICLR 2024 - [c149]Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer:
SqueezeLLM: Dense-and-Sparse Quantization. ICML 2024 - [c148]Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami:
An LLM Compiler for Parallel Function Calling. ICML 2024 - [c147]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. ICML 2024 - [c146]Konstantin Schürholt, Michael W. Mahoney, Damian Borth:
Towards Scalable and Versatile Weight Space Learning. ICML 2024 - [c145]Michal Derezinski, Michael W. Mahoney:
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. KDD 2024: 6470-6479 - [c144]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. VTS 2024: 1-5 - [i216]Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney:
SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. CoRR abs/2401.00122 (2024) - [i215]Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami:
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization. CoRR abs/2401.18079 (2024) - [i214]Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney:
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning. CoRR abs/2402.15734 (2024) - [i213]Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - [i212]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. CoRR abs/2403.10642 (2024) - [i211]Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer:
AI and Memory Wall. CoRR abs/2403.14123 (2024) - [i210]Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael W. Mahoney, Kurt Keutzer, Amir Gholami:
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. CoRR abs/2403.15042 (2024) - [i209]Annan Yu, Michael W. Mahoney, N. Benjamin Erichson:
There is HOPE to Avoid HiPPOs for Long-memory State Space Models. CoRR abs/2405.13975 (2024) - [i208]Dongwei Lyu, Rie Nakata, Pu Ren, Michael W. Mahoney, Arben Pitarka, Nori Nakata, N. Benjamin Erichson:
WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning. CoRR abs/2405.20516 (2024) - [i207]Konstantin Schürholt, Michael W. Mahoney, Damian Borth:
Towards Scalable and Versatile Weight Space Learning. CoRR abs/2406.09997 (2024) - [i206]Michal Derezinski, Michael W. Mahoney:
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. CoRR abs/2406.11151 (2024) - [i205]Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin:
Reliable edge machine learning hardware for scientific applications. CoRR abs/2406.19522 (2024) - [i204]Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang:
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance. CoRR abs/2407.12996 (2024) - [i203]Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney:
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. CoRR abs/2407.14129 (2024) - [i202]Pu Ren, Rie Nakata, Maxime Lacour, Ilan Naiman, Nori Nakata, Jialin Song, Zhengfa Bi, Osman Asif Malik, Dmitriy Morozov, Omri Azencot, N. Benjamin Erichson, Michael W. Mahoney:
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. CoRR abs/2407.15089 (2024) - [i201]Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar:
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. CoRR abs/2409.15734 (2024) - [i200]Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson:
Tuning Frequency Bias of State Space Models. CoRR abs/2410.02035 (2024) - [i199]Mansi Sakarvadia, Aswathy Ajith, Arham Khan, Nathaniel Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney:
Mitigating Memorization In Language Models. CoRR abs/2410.02159 (2024) - [i198]Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney, Xiaoye S. Li, N. Benjamin Erichson:
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. CoRR abs/2410.03229 (2024) - [i197]Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang:
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. CoRR abs/2410.10912 (2024) - 2023
- [j60]Sen Na, Michal Derezinski, Michael W. Mahoney:
Hessian averaging in stochastic Newton methods achieves superlinear convergence. Math. Program. 201(1): 473-520 (2023) - [j59]Kimon Fountoulakis, Meng Liu, David F. Gleich, Michael W. Mahoney:
Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance. SIAM Rev. 65(1): 59-143 (2023) - [c143]Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney:
Fast Feature Selection with Fairness Constraints. AISTATS 2023: 7800-7823 - [c142]Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami:
Adaptive Self-Supervision Algorithms for Physics-Informed Neural Networks. ECAI 2023: 2234-2241 - [c141]Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan:
Learning differentiable solvers for systems with hard constraints. ICLR 2023 - [c140]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR 2023 - [c139]Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. ICML 2023: 12469-12510 - [c138]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML 2023: 13085-13117 - [c137]Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. ICML 2023: 13174-13198 - [c136]Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney:
A Three-regime Model of Network Pruning. ICML 2023: 42790-42809 - [c135]Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. KDD 2023: 3011-3021 - [c134]Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer:
Speculative Decoding with Big Little Decoder. NeurIPS 2023 - [c133]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
A Heavy-Tailed Algebra for Probabilistic Programming. NeurIPS 2023 - [c132]Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami:
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. NeurIPS 2023 - [c131]Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney:
When are ensembles really effective? NeurIPS 2023 - [c130]Yefan Zhou, Tianyu Pang, Keqin Liu, Charles H. Martin, Michael W. Mahoney, Yaoqing Yang:
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. NeurIPS 2023 - [c129]Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney:
Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. SC Workshops 2023: 868-874 - [i196]Sehoon Kim, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer:
Big Little Transformer Decoder. CoRR abs/2302.07863 (2023) - [i195]Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. CoRR abs/2302.11002 (2023) - [i194]Riley Murray, James Demmel, Michael W. Mahoney, N. Benjamin Erichson, Maksim Melnichenko, Osman Asif Malik, Laura Grigori, Piotr Luszczek, Michal Derezinski, Miles E. Lopes, Tianyu Liang, Hengrui Luo, Jack J. Dongarra:
Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. CoRR abs/2302.11474 (2023) - [i193]Sehoon Kim, Coleman Hooper, Thanakul Wattanawong, Minwoo Kang, Ruohan Yan, Hasan Genc, Grace Dinh, Qijing Huang, Kurt Keutzer, Michael W. Mahoney, Yakun Sophia Shao, Amir Gholami:
Full Stack Optimization of Transformer Inference: a Survey. CoRR abs/2302.14017 (2023) - [i192]Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran:
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. CoRR abs/2304.06745 (2023) - [i191]Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney:
When are ensembles really effective? CoRR abs/2305.12313 (2023) - [i190]Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. CoRR abs/2305.18379 (2023) - [i189]Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney:
A Three-regime Model of Network Pruning. CoRR abs/2305.18383 (2023) - [i188]Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami:
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. CoRR abs/2306.00258 (2023) - [i187]Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer:
SqueezeLLM: Dense-and-Sparse Quantization. CoRR abs/2306.07629 (2023) - [i186]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
A Heavy-Tailed Algebra for Probabilistic Programming. CoRR abs/2306.09262 (2023) - [i185]Pu Ren, N. Benjamin Erichson, Shashank Subramanian, Omer San, Zarija Lukic, Michael W. Mahoney:
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning. CoRR abs/2306.14070 (2023) - [i184]Sitan Yang, Malcolm Wolff, Shankar Ramasubramanian, Vincent Quenneville-Bélair, Ronak Metha, Michael W. Mahoney:
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting. CoRR abs/2307.03595 (2023) - [i183]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
The Interpolating Information Criterion for Overparameterized Models. CoRR abs/2307.07785 (2023) - [i182]Geoffrey Négiar, Ruijun Ma, O. Nangba Meetei, Mengfei Cao, Michael W. Mahoney:
Probabilistic Forecasting with Coherent Aggregation. CoRR abs/2307.09797 (2023) - [i181]Younghyun Cho, James Weldon Demmel, Michal Derezinski, Haoyun Li, Hengrui Luo, Michael W. Mahoney, Riley J. Murray:
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems. CoRR abs/2308.15720 (2023) - [i180]Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson:
Robustifying State-space Models for Long Sequences via Approximate Diagonalization. CoRR abs/2310.01698 (2023) - [i179]Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot:
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. CoRR abs/2310.02619 (2023) - [i178]Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney:
Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. CoRR abs/2310.02926 (2023) - [i177]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR abs/2310.05387 (2023) - [i176]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR abs/2311.07013 (2023) - [i175]Maksim Melnichenko, Oleg Balabanov, Riley Murray, James Demmel, Michael W. Mahoney, Piotr Luszczek:
CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT). CoRR abs/2311.08316 (2023) - [i174]Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - [i173]Yefan Zhou, Tianyu Pang, Keqin Liu, Charles H. Martin, Michael W. Mahoney, Yaoqing Yang:
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. CoRR abs/2312.00359 (2023) - [i172]Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami:
An LLM Compiler for Parallel Function Calling. CoRR abs/2312.04511 (2023) - [i171]Omar Eldaghar, Michael W. Mahoney, David F. Gleich:
Multi-scale Local Network Structure Critically Impacts Epidemic Spread and Interventions. CoRR abs/2312.17351 (2023) - 2022
- [j58]Fred Roosta, Yang Liu, Peng Xu, Michael W. Mahoney:
Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. EURO J. Comput. Optim. 10: 100035 (2022) - [j57]Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney:
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. J. Mach. Learn. Res. 23: 22:1-22:36 (2022) - [j56]Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney:
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. J. Mach. Learn. Res. 23: 177:1-177:45 (2022) - [c128]Sehoon Kim, Amir Gholami, Zhewei Yao, Nicholas Lee, Patrick Wang, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W. Mahoney, Kurt Keutzer:
Integer-Only Zero-Shot Quantization for Efficient Speech Recognition. ICASSP 2022: 4288-4292 - [c127]Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác:
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR 2022 - [c126]Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney:
Noisy Feature Mixup. ICLR 2022 - [c125]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. ICLR 2022 - [c124]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML 2022: 8774-8795 - [c123]Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson:
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. ICML 2022: 13257-13270 - [c122]Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael W. Mahoney, Alvin Cheung:
GACT: Activation Compressed Training for Generic Network Architectures. ICML 2022: 14139-14152 - [c121]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML 2022: 14210-14222 - [c120]Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Prateek Mittal, Kannan Ramchandran, Joseph Gonzalez:
Neurotoxin: Durable Backdoors in Federated Learning. ICML 2022: 26429-26446 - [c119]Sehoon Kim, Amir Gholami, Albert E. Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer:
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. NeurIPS 2022 - [c118]Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami:
A Fast Post-Training Pruning Framework for Transformers. NeurIPS 2022 - [c117]Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Sehoon Kim, Michael W. Mahoney, Kurt Keutzer:
Hessian-Aware Pruning and Optimal Neural Implant. WACV 2022: 3665-3676 - [i170]N. Benjamin Erichson, Soon Hoe Lim, Francisco Utrera, Winnie Xu, Ziang Cao, Michael W. Mahoney:
NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. CoRR abs/2202.01263 (2022) - [i169]Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. CoRR abs/2202.02842 (2022) - [i168]Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney:
Learning continuous models for continuous physics. CoRR abs/2202.08494 (2022) - [i167]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR abs/2202.12316 (2022) - [i166]Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney:
Fast Feature Selection with Fairness Constraints. CoRR abs/2202.13718 (2022) - [i165]Sen Na, Michal Derezinski, Michael W. Mahoney:
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence. CoRR abs/2204.09266 (2022) - [i164]Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami:
A Fast Post-Training Pruning Framework for Transformers. CoRR abs/2204.09656 (2022) - [i163]Sarah E. Chasins, Alvin Cheung, Natacha Crooks, Ali Ghodsi, Ken Goldberg, Joseph E. Gonzalez, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Michael W. Mahoney, Aditya G. Parameswaran, David A. Patterson, Raluca Ada Popa, Koushik Sen, Scott Shenker, Dawn Song, Ion Stoica:
The Sky Above The Clouds. CoRR abs/2205.07147 (2022) - [i162]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. CoRR abs/2205.07918 (2022) - [i161]Sen Na, Michael W. Mahoney:
Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. CoRR abs/2205.13687 (2022) - [i160]Sehoon Kim, Amir Gholami, Albert E. Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer:
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. CoRR abs/2206.00888 (2022) - [i159]Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, Prateek Mittal:
Neurotoxin: Durable Backdoors in Federated Learning. CoRR abs/2206.10341 (2022) - [i158]Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael W. Mahoney, Alvin Cheung:
GACT: Activation Compressed Training for General Architectures. CoRR abs/2206.11357 (2022) - [i157]Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami:
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks. CoRR abs/2207.04084 (2022) - [i156]Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan:
Learning differentiable solvers for systems with hard constraints. CoRR abs/2207.08675 (2022) - [i155]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR abs/2210.00513 (2022) - [i154]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR abs/2210.07612 (2022) - [i153]N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney:
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. CoRR abs/2212.00228 (2022) - 2021
- [j55]Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney:
Inexact Nonconvex Newton-Type Methods. INFORMS J. Optim. 3(2): 154-182 (2021) - [j54]Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney:
Statistical guarantees for local graph clustering. J. Mach. Learn. Res. 22: 148:1-148:54 (2021) - [j53]Charles H. Martin, Michael W. Mahoney:
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. J. Mach. Learn. Res. 22: 165:1-165:73 (2021) - [j52]Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe:
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. J. Mach. Learn. Res. 22: 194:1-194:59 (2021) - [j51]Swapnil Das, James Demmel, Kimon Fountoulakis, Laura Grigori, Michael W. Mahoney, Shenghao Yang:
Parallel and Communication Avoiding Least Angle Regression. SIAM J. Sci. Comput. 43(2): C154-C176 (2021) - [c116]Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney:
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. AAAI 2021: 10665-10673 - [c115]Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney:
Good Classifiers are Abundant in the Interpolating Regime. AISTATS 2021: 3376-3384 - [c114]Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. IEEE BigData 2021: 1214-1225 - [c113]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. COLT 2021: 1467-1510 - [c112]Sheng Shen, Zhewei Yao, Douwe Kiela, Kurt Keutzer, Michael W. Mahoney:
What's Hidden in a One-layer Randomly Weighted Transformer? EMNLP (1) 2021: 2914-2921 - [c111]N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Liam Hodgkinson, Michael W. Mahoney:
Lipschitz Recurrent Neural Networks. ICLR 2021 - [c110]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
Sparse Quantized Spectral Clustering. ICLR 2021 - [c109]Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney:
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. ICLR 2021 - [c108]Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph Gonzalez:
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. ICML 2021: 1803-1813 - [c107]Liam Hodgkinson, Michael W. Mahoney:
Multiplicative Noise and Heavy Tails in Stochastic Optimization. ICML 2021: 4262-4274 - [c106]Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer:
I-BERT: Integer-only BERT Quantization. ICML 2021: 5506-5518 - [c105]Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer:
HAWQ-V3: Dyadic Neural Network Quantization. ICML 2021: 11875-11886 - [c104]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). IJCAI 2021: 4765-4769 - [c103]Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney:
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism. KDD 2021: 2928-2936 - [c102]Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney:
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. NeurIPS 2021: 2835-2847 - [c101]Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Noisy Recurrent Neural Networks. NeurIPS 2021: 5124-5137 - [c100]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. NeurIPS 2021: 18722-18733 - [c99]Zhenyu Liao, Michael W. Mahoney:
Hessian Eigenspectra of More Realistic Nonlinear Models. NeurIPS 2021: 20104-20117 - [c98]Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Stateful ODE-Nets using Basis Function Expansions. NeurIPS 2021: 21770-21781 - [c97]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. NeurIPS 2021: 26548-26560 - [c96]N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney:
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. SDM 2021: 100-108 - [c95]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing communication rounds for distributed learning. UAI 2021: 632-642 - [c94]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic continuous normalizing flows: training SDEs as ODEs. UAI 2021: 1130-1140 - [c93]Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney:
Geometric rates of convergence for kernel-based sampling algorithms. UAI 2021: 2156-2164 - [e2]Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Michael W. Mahoney, Anuj Karpatne, Matthew W. Farthing, Tyler J. Hesser:
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to - 24th, 2021. CEUR Workshop Proceedings 2964, CEUR-WS.org 2021 [contents] - [i152]Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer:
I-BERT: Integer-only BERT Quantization. CoRR abs/2101.01321 (2021) - [i151]Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Michael W. Mahoney, Kurt Keutzer:
Hessian-Aware Pruning and Optimal Neural Implant. CoRR abs/2101.08940 (2021) - [i150]Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Noisy Recurrent Neural Networks. CoRR abs/2102.04877 (2021) - [i149]Omri Azencot, N. Benjamin Erichson, Mirela Ben-Chen, Michael W. Mahoney:
A Differential Geometry Perspective on Orthogonal Recurrent Models. CoRR abs/2102.09589 (2021) - [i148]Zhenyu Liao, Michael W. Mahoney:
Hessian Eigenspectra of More Realistic Nonlinear Models. CoRR abs/2103.01519 (2021) - [i147]Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer:
A Survey of Quantization Methods for Efficient Neural Network Inference. CoRR abs/2103.13630 (2021) - [i146]Sehoon Kim, Amir Gholami, Zhewei Yao, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W. Mahoney, Kurt Keutzer:
Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition. CoRR abs/2103.16827 (2021) - [i145]Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph E. Gonzalez:
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. CoRR abs/2104.14129 (2021) - [i144]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR abs/2105.07320 (2021) - [i143]Zhewei Yao, Linjian Ma, Sheng Shen, Kurt Keutzer, Michael W. Mahoney:
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models. CoRR abs/2105.14636 (2021) - [i142]Charles H. Martin, Michael W. Mahoney:
Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics. CoRR abs/2106.00734 (2021) - [i141]Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Compressing Deep ODE-Nets using Basis Function Expansions. CoRR abs/2106.10820 (2021) - [i140]Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney:
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. CoRR abs/2107.07480 (2021) - [i139]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. CoRR abs/2107.11228 (2021) - [i138]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR abs/2108.00781 (2021) - [i137]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. CoRR abs/2109.01050 (2021) - [i136]Sheng Shen, Zhewei Yao, Douwe Kiela, Kurt Keutzer, Michael W. Mahoney:
What's Hidden in a One-layer Randomly Weighted Transformer? CoRR abs/2109.03939 (2021) - [i135]Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác:
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR abs/2109.05198 (2021) - [i134]Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney:
Noisy Feature Mixup. CoRR abs/2110.02180 (2021) - [i133]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. CoRR abs/2110.04744 (2021) - [i132]Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown:
Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR abs/2111.13786 (2021) - 2020
- [j50]Peng Xu, Fred Roosta, Michael W. Mahoney:
Newton-type methods for non-convex optimization under inexact Hessian information. Math. Program. 184(1): 35-70 (2020) - [c92]Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
Inefficiency of K-FAC for Large Batch Size Training. AAAI 2020: 5053-5060 - [c91]Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. AAAI 2020: 8815-8821 - [c90]Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney:
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. AISTATS 2020: 1026-1035 - [c89]Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney:
Statistical guarantees for local graph clustering. AISTATS 2020: 2687-2697 - [c88]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Bayesian experimental design using regularized determinantal point processes. AISTATS 2020: 3197-3207 - [c87]Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran:
OverSketched Newton: Fast Convex Optimization for Serverless Systems. IEEE BigData 2020: 288-297 - [c86]Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
PyHessian: Neural Networks Through the Lens of the Hessian. IEEE BigData 2020: 581-590 - [c85]Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
ZeroQ: A Novel Zero Shot Quantization Framework. CVPR 2020: 13166-13175 - [c84]Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao:
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. EMNLP (1) 2020: 2030-2038 - [c83]Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney:
Forecasting Sequential Data Using Consistent Koopman Autoencoders. ICML 2020: 475-485 - [c82]Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney:
Error Estimation for Sketched SVD via the Bootstrap. ICML 2020: 6382-6392 - [c81]Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
PowerNorm: Rethinking Batch Normalization in Transformers. ICML 2020: 8741-8751 - [c80]N. Benjamin Erichson, Zhewei Yao, Michael W. Mahoney:
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. ICPRAM 2020: 103-114 - [c79]Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez:
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. NeurIPS 2020 - [c78]Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. NeurIPS 2020 - [c77]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS 2020 - [c76]Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney:
Precise expressions for random projections: Low-rank approximation and randomized Newton. NeurIPS 2020 - [c75]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. NeurIPS 2020 - [c74]Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. NeurIPS 2020 - [c73]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. NeurIPS 2020 - [c72]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. NeurIPS 2020 - [c71]Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama:
Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. SC 2020: 57 - [c70]Peng Xu, Fred Roosta, Michael W. Mahoney:
Second-order Optimization for Non-convex Machine Learning: an Empirical Study. SDM 2020: 199-207 - [c69]Charles H. Martin, Michael W. Mahoney:
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. SDM 2020: 505-513 - [i131]Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
ZeroQ: A Novel Zero Shot Quantization Framework. CoRR abs/2001.00281 (2020) - [i130]Charles H. Martin, Tongsu Peng, Michael W. Mahoney:
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data. CoRR abs/2002.06716 (2020) - [i129]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. CoRR abs/2002.09073 (2020) - [i128]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic Normalizing Flows. CoRR abs/2002.09547 (2020) - [i127]Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney:
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. CoRR abs/2002.10526 (2020) - [i126]Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney:
Forecasting Sequential Data using Consistent Koopman Autoencoders. CoRR abs/2003.02236 (2020) - [i125]Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney:
Error Estimation for Sketched SVD via the Bootstrap. CoRR abs/2003.04937 (2020) - [i124]Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
Rethinking Batch Normalization in Transformers. CoRR abs/2003.07845 (2020) - [i123]Kimon Fountoulakis, Meng Liu, David F. Gleich, Michael W. Mahoney:
Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance. CoRR abs/2004.09608 (2020) - [i122]Michal Derezinski, Michael W. Mahoney:
Determinantal Point Processes in Randomized Numerical Linear Algebra. CoRR abs/2005.03185 (2020) - [i121]Zhewei Yao, Amir Gholami, Sheng Shen, Kurt Keutzer, Michael W. Mahoney:
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. CoRR abs/2006.00719 (2020) - [i120]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. CoRR abs/2006.05013 (2020) - [i119]Liam Hodgkinson, Michael W. Mahoney:
Multiplicative noise and heavy tails in stochastic optimization. CoRR abs/2006.06293 (2020) - [i118]Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney:
Precise expressions for random projections: Low-rank approximation and randomized Newton. CoRR abs/2006.10653 (2020) - [i117]N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Michael W. Mahoney:
Lipschitz Recurrent Neural Networks. CoRR abs/2006.12070 (2020) - [i116]Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney:
Good linear classifiers are abundant in the interpolating regime. CoRR abs/2006.12625 (2020) - [i115]Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. CoRR abs/2007.01327 (2020) - [i114]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. CoRR abs/2007.05086 (2020) - [i113]Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney:
Adversarially-Trained Deep Nets Transfer Better. CoRR abs/2007.05869 (2020) - [i112]N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney:
Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. CoRR abs/2008.00123 (2020) - [i111]Alejandro F. Queiruga, N. Benjamin Erichson, Dane Taylor, Michael W. Mahoney:
Continuous-in-Depth Neural Networks. CoRR abs/2008.02389 (2020) - [i110]Zhengming Zhang, Zhewei Yao, Yaoqing Yang, Yujun Yan, Joseph E. Gonzalez, Michael W. Mahoney:
Benchmarking Semi-supervised Federated Learning. CoRR abs/2008.11364 (2020) - [i109]Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
Sparse Quantized Spectral Clustering. CoRR abs/2010.01376 (2020) - [i108]Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao:
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. CoRR abs/2010.05379 (2020) - [i107]Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney:
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism. CoRR abs/2010.08899 (2020) - [i106]Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez:
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. CoRR abs/2010.14298 (2020) - [i105]Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer:
HAWQV3: Dyadic Neural Network Quantization. CoRR abs/2011.10680 (2020) - [i104]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. CoRR abs/2011.10695 (2020)
2010 – 2019
- 2019
- [j49]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff:
Alchemist: An Apache Spark ⇔ MPI interface. Concurr. Comput. Pract. Exp. 31(16) (2019) - [j48]Bo Liu, Liping Jing, Jia Li, Jian Yu, Alex Gittens, Michael W. Mahoney:
Group Collaborative Representation for Image Set Classification. Int. J. Comput. Vis. 127(2): 181-206 (2019) - [j47]Shusen Wang, Alex Gittens, Michael W. Mahoney:
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds. J. Mach. Learn. Res. 20: 12:1-12:49 (2019) - [j46]Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. J. Mach. Learn. Res. 20: 39:1-39:40 (2019) - [j45]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-sampled Newton methods. Math. Program. 174(1-2): 293-326 (2019) - [j44]Kimon Fountoulakis, Farbod Roosta-Khorasani, Julian Shun, Xiang Cheng, Michael W. Mahoney:
Variational perspective on local graph clustering. Math. Program. 174(1-2): 553-573 (2019) - [j43]Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve:
Block Basis Factorization for Scalable Kernel Evaluation. SIAM J. Matrix Anal. Appl. 40(4): 1497-1526 (2019) - [j42]Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney:
Avoiding Communication in Primal and Dual Block Coordinate Descent Methods. SIAM J. Sci. Comput. 41(1): C1-C27 (2019) - [j41]Michael W. Mahoney:
The Difficulties of Addressing Interdisciplinary Challenges at theFoundations of Data Science. SIGACT News 50(3): 91-95 (2019) - [c68]Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth:
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. COLT 2019: 1050-1069 - [c67]Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael W. Mahoney:
Trust Region Based Adversarial Attack on Neural Networks. CVPR 2019: 11350-11359 - [c66]Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. ICCV 2019: 293-302 - [c65]Michael W. Mahoney, Charles H. Martin:
Traditional and Heavy Tailed Self Regularization in Neural Network Models. ICML 2019: 4284-4293 - [c64]Charles H. Martin, Michael W. Mahoney:
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. KDD 2019: 3239-3240 - [c63]Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros:
ANODEV2: A Coupled Neural ODE Framework. NeurIPS 2019: 5152-5162 - [c62]Michal Derezinski, Michael W. Mahoney:
Distributed estimation of the inverse Hessian by determinantal averaging. NeurIPS 2019: 11401-11411 - [c61]Sudhir B. Kylasa, Fred (Farbod) Roosta, Michael W. Mahoney, Ananth Grama:
GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. SDM 2019: 702-710 - [i103]Charles H. Martin, Michael W. Mahoney:
Traditional and Heavy-Tailed Self Regularization in Neural Network Models. CoRR abs/1901.08276 (2019) - [i102]Charles H. Martin, Michael W. Mahoney:
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. CoRR abs/1901.08278 (2019) - [i101]Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth:
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. CoRR abs/1902.00995 (2019) - [i100]N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz:
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. CoRR abs/1902.07358 (2019) - [i99]Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
Inefficiency of K-FAC for Large Batch Size Training. CoRR abs/1903.06237 (2019) - [i98]Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran:
OverSketched Newton: Fast Convex Optimization for Serverless Systems. CoRR abs/1903.08857 (2019) - [i97]N. Benjamin Erichson, Zhewei Yao, Michael W. Mahoney:
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. CoRR abs/1904.03750 (2019) - [i96]Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision. CoRR abs/1905.03696 (2019) - [i95]N. Benjamin Erichson, Michael Muehlebach, Michael W. Mahoney:
Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction. CoRR abs/1905.10866 (2019) - [i94]Swapnil Das, Jim Demmel, Kimon Fountoulakis, Laura Grigori, Michael W. Mahoney:
Parallel and Communication Avoiding Least Angle Regression. CoRR abs/1905.11340 (2019) - [i93]Michal Derezinski, Michael W. Mahoney:
Distributed estimation of the inverse Hessian by determinantal averaging. CoRR abs/1905.11546 (2019) - [i92]Kai Rothauge, Zhewei Yao, Zixi Hu, Michael W. Mahoney:
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization. CoRR abs/1905.13386 (2019) - [i91]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Bayesian experimental design using regularized determinantal point processes. CoRR abs/1906.04133 (2019) - [i90]Tianjun Zhang, Zhewei Yao, Amir Gholami, Kurt Keutzer, Joseph Gonzalez, George Biros, Michael W. Mahoney:
ANODEV2: A Coupled Neural ODE Evolution Framework. CoRR abs/1906.04596 (2019) - [i89]Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney:
Statistical guarantees for local graph clustering. CoRR abs/1906.04863 (2019) - [i88]Rajiv Khanna, Michael W. Mahoney:
On Linear Convergence of Weighted Kernel Herding. CoRR abs/1907.08410 (2019) - [i87]Michael W. Mahoney:
The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science. CoRR abs/1909.03033 (2019) - [i86]Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. CoRR abs/1909.05840 (2019) - [i85]Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney:
Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching. CoRR abs/1909.06120 (2019) - [i84]Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe:
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. CoRR abs/1910.00423 (2019) - [i83]Kai Rothauge, Haripriya Ayyalasomayajula, Kristyn J. Maschhoff, Michael F. Ringenburg, Michael W. Mahoney:
Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times. CoRR abs/1910.01354 (2019) - [i82]Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. CoRR abs/1911.03852 (2019) - [i81]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. CoRR abs/1912.04533 (2019) - [i80]Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
PyHessian: Neural Networks Through the Lens of the Hessian. CoRR abs/1912.07145 (2019) - 2018
- [c60]Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney:
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. AISTATS 2018: 404-414 - [c59]Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe:
Out-of-sample extension of graph adjacency spectral embedding. ICML 2018: 2981-2990 - [c58]Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. ICML 2018: 3223-3232 - [c57]Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney:
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization. IPDPS 2018: 409-418 - [c56]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff:
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. KDD 2018: 293-301 - [c55]Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney:
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. NeurIPS 2018: 2338-2348 - [c54]Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney:
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. NeurIPS 2018: 4954-4964 - [c53]Evgheniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney:
LASAGNE: Locality and Structure Aware Graph Node Embedding. WI 2018: 246-253 - [i79]Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney:
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. CoRR abs/1802.08241 (2018) - [i78]Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama:
GPU Accelerated Sub-Sampled Newton\textsf{'}s Method. CoRR abs/1802.09113 (2018) - [i77]Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. CoRR abs/1803.08021 (2018) - [i76]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff:
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. CoRR abs/1805.11800 (2018) - [i75]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff:
Alchemist: An Apache Spark MPI Interface. CoRR abs/1806.01270 (2018) - [i74]Chih-Hao Fang, Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama:
Distributed Second-order Convex Optimization. CoRR abs/1807.07132 (2018) - [i73]Fred (Farbod) Roosta, Yang Liu, Peng Xu, Michael W. Mahoney:
Newton-MR: Newton's Method Without Smoothness or Convexity. CoRR abs/1810.00303 (2018) - [i72]Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
Large batch size training of neural networks with adversarial training and second-order information. CoRR abs/1810.01021 (2018) - [i71]Charles H. Martin, Michael W. Mahoney:
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. CoRR abs/1810.01075 (2018) - [i70]Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney:
A Short Introduction to Local Graph Clustering Methods and Software. CoRR abs/1810.07324 (2018) - [i69]Noah Golmant, Nikita Vemuri, Zhewei Yao, Vladimir Feinberg, Amir Gholami, Kai Rothauge, Michael W. Mahoney, Joseph Gonzalez:
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent. CoRR abs/1811.12941 (2018) - [i68]Norman Mu, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney:
Parameter Re-Initialization through Cyclical Batch Size Schedules. CoRR abs/1812.01216 (2018) - [i67]Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael W. Mahoney:
Trust Region Based Adversarial Attack on Neural Networks. CoRR abs/1812.06371 (2018) - 2017
- [j40]Jiyan Yang, Yin-Lam Chow, Christopher Ré, Michael W. Mahoney:
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning. J. Mach. Learn. Res. 18: 211:1-211:43 (2017) - [j39]Shusen Wang, Alex Gittens, Michael W. Mahoney:
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. J. Mach. Learn. Res. 18: 218:1-218:50 (2017) - [j38]Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter:
A local perspective on community structure in multilayer networks. Netw. Sci. 5(2): 144-163 (2017) - [j37]Thomas A. Courtade, Ananth Grama, Michael W. Mahoney, Tsachy Weissman:
Principles and Applications of Science of Information. Proc. IEEE 105(2): 183-188 (2017) - [j36]Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney:
An Optimization Approach to Locally-Biased Graph Algorithms. Proc. IEEE 105(2): 256-272 (2017) - [j35]Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland:
DCAR: A Discriminative and Compact Audio Representation for Audio Processing. IEEE Trans. Multim. 19(12): 2637-2650 (2017) - [c52]Alex Gittens, Dimitris Achlioptas, Michael W. Mahoney:
Skip-Gram - Zipf + Uniform = Vector Additivity. ACL (1) 2017: 69-76 - [c51]Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao:
Capacity Releasing Diffusion for Speed and Locality. ICML 2017: 3598-3607 - [c50]Shusen Wang, Alex Gittens, Michael W. Mahoney:
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. ICML 2017: 3608-3616 - [c49]Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya:
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS 2017: 1078-1086 - [i66]Shusen Wang, Alex Gittens, Michael W. Mahoney:
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. CoRR abs/1702.04837 (2017) - [i65]Danqing Zhang, Kimon Fountoulakis, Junyu Cao, Mogeng Yin, Michael W. Mahoney, Alexei Pozdnoukhov:
Social Discrete Choice Models. CoRR abs/1703.07520 (2017) - [i64]Shusen Wang, Alex Gittens, Michael W. Mahoney:
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds. CoRR abs/1706.02803 (2017) - [i63]Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao:
Capacity Releasing Diffusion for Speed and Locality. CoRR abs/1706.05826 (2017) - [i62]Miles E. Lopes, Shusen Wang, Michael W. Mahoney:
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. CoRR abs/1708.01945 (2017) - [i61]Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney:
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information. CoRR abs/1708.07164 (2017) - [i60]Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney:
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study. CoRR abs/1708.07827 (2017) - [i59]Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney:
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. CoRR abs/1709.03528 (2017) - [i58]Evgheniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney:
LASAGNE: Locality And Structure Aware Graph Node Embedding. CoRR abs/1710.06520 (2017) - [i57]Charles H. Martin, Michael W. Mahoney:
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior. CoRR abs/1710.09553 (2017) - [i56]Ion Stoica, Dawn Song, Raluca Ada Popa, David A. Patterson, Michael W. Mahoney, Randy H. Katz, Anthony D. Joseph, Michael I. Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David E. Culler, Pieter Abbeel:
A Berkeley View of Systems Challenges for AI. CoRR abs/1712.05855 (2017) - [i55]Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney:
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization. CoRR abs/1712.06047 (2017) - [i54]Petros Drineas, Michael W. Mahoney:
Lectures on Randomized Numerical Linear Algebra. CoRR abs/1712.08880 (2017) - 2016
- [j34]Petros Drineas, Michael W. Mahoney:
RandNLA: randomized numerical linear algebra. Commun. ACM 59(6): 80-90 (2016) - [j33]Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney:
Tree decompositions and social graphs. Internet Math. 12(5): 315-361 (2016) - [j32]Alex Gittens, Michael W. Mahoney:
Revisiting the Nystrom Method for Improved Large-scale Machine Learning. J. Mach. Learn. Res. 17: 117:1-117:65 (2016) - [j31]Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. 17: 120:1-120:38 (2016) - [j30]Garvesh Raskutti, Michael W. Mahoney:
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. J. Mach. Learn. Res. 17: 214:1-214:31 (2016) - [j29]Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. Proc. IEEE 104(1): 58-92 (2016) - [j28]Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney:
Parallel Local Graph Clustering. Proc. VLDB Endow. 9(12): 1041-1052 (2016) - [j27]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform and Faster Robust Linear Regression. SIAM J. Comput. 45(3): 763-810 (2016) - [c48]Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat:
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. IEEE BigData 2016: 204-213 - [c47]Di Wang, Satish Rao, Michael W. Mahoney:
Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. ICALP 2016: 50:1-50:13 - [c46]Michael W. Mahoney, Satish Rao, Di Wang, Peng Zhang:
Approximating the Solution to Mixed Packing and Covering LPs in Parallel O˜(epsilon^{-3}) Time. ICALP 2016: 52:1-52:14 - [c45]Nate Veldt, David F. Gleich, Michael W. Mahoney:
A Simple and Strongly-Local Flow-Based Method for Cut Improvement. ICML 2016: 1938-1947 - [c44]Alex Gittens, Jey Kottalam, Jiyan Yang, Michael F. Ringenburg, Jatin Chhugani, Evan Racah, Mohitdeep Singh, Yushu Yao, Curt Fischer, Oliver Rübel, Benjamin P. Bowen, Norman G. Lewis, Michael W. Mahoney, Venkat Krishnamurthy, Prabhat:
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. IPDPS Workshops 2016: 1403-1412 - [c43]Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland:
A Discriminative and Compact Audio Representation for Event Detection. ACM Multimedia 2016: 57-61 - [c42]Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun:
Feature-distributed sparse regression: a screen-and-clean approach. NIPS 2016: 2712-2720 - [c41]Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney:
Sub-sampled Newton Methods with Non-uniform Sampling. NIPS 2016: 3000-3008 - [c40]Jiyan Yang, Yinlam Chow, Christopher Ré, Michael W. Mahoney:
Weighted SGD for ℓp Regression with Randomized Preconditioning. SODA 2016: 558-569 - [p2]Michael W. Mahoney, Petros Drineas:
Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms. Handbook of Big Data 2016: 137-154 - [p1]David F. Gleich, Michael W. Mahoney:
Mining Large Graphs. Handbook of Big Data 2016: 191-220 - [i53]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-Sampled Newton Methods I: Globally Convergent Algorithms. CoRR abs/1601.04737 (2016) - [i52]Farbod Roosta-Khorasani, Michael W. Mahoney:
Sub-Sampled Newton Methods II: Local Convergence Rates. CoRR abs/1601.04738 (2016) - [i51]Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney:
Parallel Local Graph Clustering. CoRR abs/1604.07515 (2016) - [i50]Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney:
FLAG: Fast Linearly-Coupled Adaptive Gradient Method. CoRR abs/1605.08108 (2016) - [i49]Nate Veldt, David F. Gleich, Michael W. Mahoney:
A Simple and Strongly-Local Flow-Based Method for Cut Improvement. CoRR abs/1605.08490 (2016) - [i48]Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat:
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. CoRR abs/1607.01335 (2016) - [i47]Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland:
DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection. CoRR abs/1607.04378 (2016) - [i46]Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney:
An optimization approach to locally-biased graph algorithms. CoRR abs/1607.04940 (2016) - [i45]Michael W. Mahoney:
Lecture Notes on Randomized Linear Algebra. CoRR abs/1608.04481 (2016) - [i44]Michael W. Mahoney:
Lecture Notes on Spectral Graph Methods. CoRR abs/1608.04845 (2016) - [i43]David Lawlor, Tamás Budavári, Michael W. Mahoney:
Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data. CoRR abs/1609.03932 (2016) - [i42]Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney:
Avoiding communication in primal and dual block coordinate descent methods. CoRR abs/1612.04003 (2016) - [i41]Ravindran Kannan, Michael W. Mahoney, David P. Woodruff:
Recent Advances in Randomized Numerical Linear Algebra (NII Shonan Meeting 2016-10). NII Shonan Meet. Rep. 2016 (2016) - 2015
- [j26]Ping Ma, Michael W. Mahoney, Bin Yu:
A statistical perspective on algorithmic leveraging. J. Mach. Learn. Res. 16: 861-911 (2015) - [j25]Christos Boutsidis, Anastasios Zouzias, Michael W. Mahoney, Petros Drineas:
Randomized Dimensionality Reduction for k-Means Clustering. IEEE Trans. Inf. Theory 61(2): 1045-1062 (2015) - [c39]David G. Anderson, Simon Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu:
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method. AISTATS 2015 - [c38]Garvesh Raskutti, Michael W. Mahoney:
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. ICML 2015: 617-625 - [c37]David F. Gleich, Michael W. Mahoney:
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. KDD 2015: 359-368 - [c36]Ahmed El Alaoui, Michael W. Mahoney:
Fast Randomized Kernel Ridge Regression with Statistical Guarantees. NIPS 2015: 775-783 - [i40]Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. CoRR abs/1502.03032 (2015) - [i39]Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve:
Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation. CoRR abs/1505.00398 (2015) - [i38]Di Wang, Satish Rao, Michael W. Mahoney:
Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. CoRR abs/1508.02439 (2015) - [i37]Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter:
A Local Perspective on Community Structure in Multilayer Networks. CoRR abs/1510.05185 (2015) - [i36]Di Wang, Michael W. Mahoney, Nishanth Mohan, Satish Rao:
Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent. CoRR abs/1511.06468 (2015) - 2014
- [j24]Michael W. Mahoney:
A new spin on an old algorithm: technical perspective. Commun. ACM 57(2): 106 (2014) - [j23]Toke Jansen Hansen, Michael W. Mahoney:
Semi-supervised eigenvectors for large-scale locally-biased learning. J. Mach. Learn. Res. 15(1): 3691-3734 (2014) - [j22]Xiangrui Meng, Michael A. Saunders, Michael W. Mahoney:
LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems. SIAM J. Sci. Comput. 36(2) (2014) - [j21]Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Quantile Regression for Large-Scale Applications. SIAM J. Sci. Comput. 36(5) (2014) - [j20]Georgios B. Giannakis, Francis R. Bach, Raphael Cendrillon, Michael W. Mahoney, Jennifer Neville:
Signal Processing for Big Data [From the Guest Editors]. IEEE Signal Process. Mag. 31(5): 15-16 (2014) - [c35]Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney:
Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR 2014: 971-978 - [c34]Ping Ma, Michael W. Mahoney, Bin Yu:
A Statistical Perspective on Algorithmic Leveraging. ICML 2014: 91-99 - [c33]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML 2014: 485-493 - [c32]David F. Gleich, Michael W. Mahoney:
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow. ICML 2014: 1018-1025 - [i35]Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney:
Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks. CoRR abs/1403.3795 (2014) - [i34]Ahmed El Alaoui, Michael W. Mahoney:
Fast Randomized Kernel Methods With Statistical Guarantees. CoRR abs/1411.0306 (2014) - [i33]Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney:
Tree decompositions and social graphs. CoRR abs/1411.1546 (2014) - [i32]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR abs/1412.8293 (2014) - 2013
- [j19]Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney:
On the Hyperbolicity of Small-World and Treelike Random Graphs. Internet Math. 9(4): 434-491 (2013) - [c31]Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney:
Tree-Like Structure in Large Social and Information Networks. ICDM 2013: 1-10 - [c30]Alex Gittens, Michael W. Mahoney:
Revisiting the Nystrom method for improved large-scale machine learning. ICML (3) 2013: 567-575 - [c29]Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Quantile Regression for Large-scale Applications. ICML (3) 2013: 881-887 - [c28]Xiangrui Meng, Michael W. Mahoney:
Robust Regression on MapReduce. ICML (3) 2013: 888-896 - [c27]Aaron B. Adcock, Blair D. Sullivan, Oscar R. Hernandez, Michael W. Mahoney:
Evaluating OpenMP Tasking at Scale for the Computation of Graph Hyperbolicity. IWOMP 2013: 71-83 - [c26]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform and Faster Robust Linear Regression. SODA 2013: 466-477 - [c25]Xiangrui Meng, Michael W. Mahoney:
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression. STOC 2013: 91-100 - [i31]Alex Gittens, Michael W. Mahoney:
Revisiting the Nystrom Method for Improved Large-Scale Machine Learning. CoRR abs/1303.1849 (2013) - [i30]Toke Jansen Hansen, Michael W. Mahoney:
Semi-supervised Eigenvectors for Large-scale Locally-biased Learning. CoRR abs/1304.7528 (2013) - [i29]Jiyan Yang, Xiangrui Meng, Michael W. Mahoney:
Quantile Regression for Large-scale Applications. CoRR abs/1305.0087 (2013) - [i28]Ping Ma, Michael W. Mahoney, Bin Yu:
A Statistical Perspective on Algorithmic Leveraging. CoRR abs/1306.5362 (2013) - 2012
- [j18]András Bodor, István Csabai, Michael W. Mahoney, Norbert Solymosi:
rCUR: an R package for CUR matrix decomposition. BMC Bioinform. 13: 103 (2012) - [j17]Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi:
A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally. J. Mach. Learn. Res. 13: 2339-2365 (2012) - [j16]Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. J. Mach. Learn. Res. 13: 3475-3506 (2012) - [c24]Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. ICML 2012 - [c23]Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney:
On the Hyperbolicity of Small-World and Tree-Like Random Graphs. ISAAC 2012: 278-288 - [c22]Toke Jansen Hansen, Michael W. Mahoney:
Semi-supervised Eigenvectors for Locally-biased Learning. NIPS 2012: 2537-2545 - [c21]Michael W. Mahoney:
Approximate computation and implicit regularization for very large-scale data analysis. PODS 2012: 143-154 - [i27]Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney:
On the Hyperbolicity of Small-World Networks and Tree-Like Graphs. CoRR abs/1201.1717 (2012) - [i26]Michael W. Mahoney:
Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis. CoRR abs/1203.0786 (2012) - [i25]Ping Li, Michael W. Mahoney, Yiyuan She:
Approximating Higher-Order Distances Using Random Projections. CoRR abs/1203.3492 (2012) - [i24]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform: with Applications to Basis Construction, Regression, and Subspace Approximation in L1. CoRR abs/1207.4684 (2012) - [i23]Xiangrui Meng, Michael W. Mahoney:
Low-distortion Subspace Embeddings in Input-sparsity Time and Applications to Robust Linear Regression. CoRR abs/1210.3135 (2012) - 2011
- [j15]Michael W. Mahoney:
Randomized Algorithms for Matrices and Data. Found. Trends Mach. Learn. 3(2): 123-224 (2011) - [j14]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós:
Faster least squares approximation. Numerische Mathematik 117(2): 219-249 (2011) - [c20]Michael W. Mahoney, Lorenzo Orecchia:
Implementing regularization implicitly via approximate eigenvector computation. ICML 2011: 121-128 - [c19]Patrick O. Perry, Michael W. Mahoney:
Regularized Laplacian Estimation and Fast Eigenvector Approximation. NIPS 2011: 2420-2428 - [i22]Michael W. Mahoney:
Randomized algorithms for matrices and data. CoRR abs/1104.5557 (2011) - [i21]Mihai Cucuringu, Michael W. Mahoney:
Localization on low-order eigenvectors of data matrices. CoRR abs/1109.1355 (2011) - [i20]Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. CoRR abs/1109.3843 (2011) - [i19]Xiangrui Meng, Michael A. Saunders, Michael W. Mahoney:
LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems. CoRR abs/1109.5981 (2011) - [i18]Patrick O. Perry, Michael W. Mahoney:
Regularized Laplacian Estimation and Fast Eigenvector Approximation. CoRR abs/1110.1757 (2011) - [i17]Christos Boutsidis, Anastasios Zouzias, Michael W. Mahoney, Petros Drineas:
Stochastic Dimensionality Reduction for K-means Clustering. CoRR abs/1110.2897 (2011) - 2010
- [j13]Samir Khuller, Michael W. Mahoney:
SIGACT news algorithms column: computation in large-scale scientific and internet data applications is a focus of MMDS 2010. SIGACT News 41(4): 65-72 (2010) - [j12]Michael W. Mahoney:
Computation in large-scale scientific and internet data applications is a focus of MMDS 2010. SIGKDD Explor. 12(2): 59-62 (2010) - [c18]Jacob Bien, Ya Xu, Michael W. Mahoney:
CUR from a Sparse Optimization Viewpoint. NIPS 2010: 217-225 - [c17]Ping Li, Michael W. Mahoney, Yiyuan She:
Approximating Higher-Order Distances Using Random Projections. UAI 2010: 312-321 - [c16]Jure Leskovec, Kevin J. Lang, Michael W. Mahoney:
Empirical comparison of algorithms for network community detection. WWW 2010: 631-640 - [i16]Jure Leskovec, Kevin J. Lang, Michael W. Mahoney:
Empirical Comparison of Algorithms for Network Community Detection. CoRR abs/1004.3539 (2010) - [i15]Petros Drineas, Michael W. Mahoney:
Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving. CoRR abs/1005.3097 (2010) - [i14]Michael W. Mahoney, Lorenzo Orecchia:
Implementing regularization implicitly via approximate eigenvector computation. CoRR abs/1010.0703 (2010) - [i13]Michael W. Mahoney:
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis. CoRR abs/1010.1609 (2010) - [i12]Jacob Bien, Ya Xu, Michael W. Mahoney:
CUR from a Sparse Optimization Viewpoint. CoRR abs/1011.0413 (2010) - [i11]Michael W. Mahoney:
Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010. CoRR abs/1012.4231 (2010)
2000 – 2009
- 2009
- [j11]Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney:
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Math. 6(1): 29-123 (2009) - [j10]Michael W. Mahoney, Petros Drineas:
CUR matrix decompositions for improved data analysis. Proc. Natl. Acad. Sci. USA 106(3): 697-702 (2009) - [j9]Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney:
Sampling Algorithms and Coresets for $\ellp Regression. SIAM J. Comput. 38(5): 2060-2078 (2009) - [c15]Christos Boutsidis, Michael W. Mahoney, Petros Drineas:
Unsupervised Feature Selection for the $k$-means Clustering Problem. NIPS 2009: 153-161 - [c14]Christos Boutsidis, Michael W. Mahoney, Petros Drineas:
An improved approximation algorithm for the column subset selection problem. SODA 2009: 968-977 - [c13]Kevin J. Lang, Michael W. Mahoney, Lorenzo Orecchia:
Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow. SEA 2009: 197-208 - [i10]Michael W. Mahoney, Hariharan Narayanan:
Learning with Spectral Kernels and Heavy-Tailed Data. CoRR abs/0906.4539 (2009) - [i9]Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi:
A Spectral Algorithm for Improving Graph Partitions. CoRR abs/0912.0681 (2009) - 2008
- [j8]Petros Drineas, Ravi Kannan, Michael W. Mahoney:
Sampling subproblems of heterogeneous Max-Cut problems and approximation algorithms. Random Struct. Algorithms 32(3): 307-333 (2008) - [j7]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan:
Relative-Error CUR Matrix Decompositions. SIAM J. Matrix Anal. Appl. 30(2): 844-881 (2008) - [j6]Michael W. Mahoney, Mauro Maggioni, Petros Drineas:
Tensor-CUR Decompositions for Tensor-Based Data. SIAM J. Matrix Anal. Appl. 30(3): 957-987 (2008) - [j5]Michael W. Mahoney, Lek-Heng Lim, Gunnar E. Carlsson:
Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008. SIGKDD Explor. 10(2): 57-60 (2008) - [c12]Christos Boutsidis, Michael W. Mahoney, Petros Drineas:
Unsupervised feature selection for principal components analysis. KDD 2008: 61-69 - [c11]Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney:
Sampling algorithms and coresets for ℓp regression. SODA 2008: 932-941 - [c10]Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney:
Statistical properties of community structure in large social and information networks. WWW 2008: 695-704 - [i8]Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney:
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. CoRR abs/0810.1355 (2008) - [i7]Michael W. Mahoney, Lek-Heng Lim, Gunnar E. Carlsson:
Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008. CoRR abs/0812.3702 (2008) - [i6]Christos Boutsidis, Michael W. Mahoney, Petros Drineas:
An Improved Approximation Algorithm for the Column Subset Selection Problem. CoRR abs/0812.4293 (2008) - 2007
- [c9]Anirban Dasgupta, Petros Drineas, Boulos Harb, Vanja Josifovski, Michael W. Mahoney:
Feature selection methods for text classification. KDD 2007: 230-239 - [e1]Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld:
Web Information Retrieval and Linear Algebra Algorithms, 11.02. - 16.02.2007. Dagstuhl Seminar Proceedings 07071, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2007 [contents] - [i5]Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld:
07071 Report on Dagstuhl Seminar -- Web Information Retrieval and Linear Algebra Algorithms. Web Information Retrieval and Linear Algebra Algorithms 2007 - [i4]Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld:
07071 Abstracts Collection -- Web Information Retrieval and Linear Algebra Algorithms. Web Information Retrieval and Linear Algebra Algorithms 2007 - [i3]Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney:
Sampling Algorithms and Coresets for Lp Regression. CoRR abs/0707.1714 (2007) - [i2]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan:
Relative-Error CUR Matrix Decompositions. CoRR abs/0708.3696 (2007) - [i1]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós:
Faster Least Squares Approximation. CoRR abs/0710.1435 (2007) - 2006
- [j4]Petros Drineas, Ravi Kannan, Michael W. Mahoney:
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication. SIAM J. Comput. 36(1): 132-157 (2006) - [j3]Petros Drineas, Ravi Kannan, Michael W. Mahoney:
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix. SIAM J. Comput. 36(1): 158-183 (2006) - [j2]Petros Drineas, Ravi Kannan, Michael W. Mahoney:
Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition. SIAM J. Comput. 36(1): 184-206 (2006) - [c8]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan:
Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods. APPROX-RANDOM 2006: 316-326 - [c7]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan:
Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods. ESA 2006: 304-314 - [c6]Michael W. Mahoney, Mauro Maggioni, Petros Drineas:
Tensor-CUR decompositions for tensor-based data. KDD 2006: 327-336 - [c5]Petros Drineas, Michael W. Mahoney, S. Muthukrishnan:
Sampling algorithms for l2 regression and applications. SODA 2006: 1127-1136 - [c4]Petros Drineas, Michael W. Mahoney:
Randomized Algorithms for Matrices and Massive Data Sets. VLDB 2006: 1269 - 2005
- [j1]Petros Drineas, Michael W. Mahoney:
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. J. Mach. Learn. Res. 6: 2153-2175 (2005) - [c3]Petros Drineas, Michael W. Mahoney:
Approximating a Gram Matrix for Improved Kernel-Based Learning. COLT 2005: 323-337 - [c2]Petros Drineas, Ravi Kannan, Michael W. Mahoney:
Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms. STACS 2005: 57-68 - 2003
- [c1]Ravi Kannan, Michael W. Mahoney, Ravi Montenegro:
Rapid Mixing of Several Markov Chains for a Hard-Core Model. ISAAC 2003: 663-675
Coauthor Index
Amir Gholami
aka: Amir Gholaminejad
aka: Amir Gholaminejad
[j63] [j61] [c154] [c149] [c148] [c144] [i215] [i211] [i210] [i205] [c142] [c134] [c132] [i196] [i193] [i192] [i188] [i187] [i172] [c128] [c119] [c118] [c117] [i164] [i160] [i157] [c116] [c106] [c105] [c97] [i152] [i151] [i147] [i146] [i137] [c92] [c91] [c86] [c85] [c81] [c74] [c72] [i131] [i124] [i121] [i114] [i105] [c67] [c66] [c63] [i99] [i96] [i90] [i86] [i82] [i80] [c54] [i79] [i72] [i69] [i68] [i67]
Joseph Gonzalez 0001
aka: Joseph E. Gonzalez
aka: Joseph E. Gonzalez
[j63] [c154] [c149] [c148] [i215] [i211] [i210] [i204] [c134] [c132] [i196] [i193] [i188] [i187] [i172] [c128] [c119] [c118] [c117] [i164] [i160] [c116] [c112] [c106] [c105] [i152] [i151] [i147] [i146] [i143] [i136] [c92] [c91] [c86] [c85] [c81] [c74] [c72] [i131] [i124] [i121] [i114] [i105] [c67] [c66] [c63] [i99] [i96] [i90] [i86] [i82] [i80] [c54] [i79] [i72] [i68] [i67]
[j63] [i211] [c128] [c117] [j55] [c116] [c114] [c112] [c108] [c106] [c105] [i152] [i151] [i147] [i146] [i145] [i143] [i136] [c92] [c91] [c86] [c85] [c84] [c81] [c80] [c79] [c74] [i131] [i124] [i121] [i110] [i108] [i106] [i105] [c67] [c66] [c63] [i100] [i99] [i97] [i96] [i92] [i90] [i86] [i82] [i80] [c54] [i79] [i72] [i69] [i68] [i67]
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