Michael W. Mahoney
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
124
Venues
30
Active years
2003–2026
Best venue rank
A*
Where they publish
- A*ICML40 papers
- A*ICLR15 papers
- AAISTATS10 papers
- A*KDD9 papers
- A*SODA5 papers
- A*COLT4 papers
- AUAI4 papers
- ASDM4 papers
- A*ACL3 papers
- A*AAAI3 papers
- A*CVPR3 papers
- ASC2 papers
- A*EMNLP2 papers
- A*ICALP2 papers
- BISAAC2 papers
- A*WWW2 papers
- A*ICDE1 paper
- NationalVTS1 paper
- AECAI1 paper
- MulticonferenceICASSP1 paper
- AWACV1 paper
- A*IJCAI1 paper
- CICPRAM1 paper
- A*ICCV1 paper
- A*ICDM1 paper
- A*STOC1 paper
- A*PODS1 paper
- AESA1 paper
- A*VLDB1 paper
- ASTACS1 paper
Papers
124 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data. | Collin Cranston, Zhichao Wang, Todd Kemp, Michael W. Mahoney |
| 2026 | ICDE | TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting. | Zhiyuan Zhao, Sitan Yang, Kin G. Olivares, Boris N. Oreshkin, Stan Vitebsky, Michael W. Mahoney, B. Aditya Prakash, Dmitry Efimov |
| 2025 | ACL | Squeezed Attention: Accelerating Long Context Length LLM Inference. | Coleman Richard Charles Hooper, Sehoon Kim, Hiva Mohammadzadeh, Monishwaran Maheswaran, Sebastian Zhao, June Paik, Michael W. Mahoney, Kurt Keutzer, Amir Gholami |
| 2025 | AISTATS | Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. | N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney |
| 2025 | ICLR | A Statistical Framework for Ranking LLM-based Chatbots. | Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney |
| 2025 | ICLR | Gradient-Free Generation for Hard-Constrained Systems. | Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Bernie Wang |
| 2025 | ICLR | Mitigating Memorization in Language Models. | Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C. Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney |
| 2025 | ICLR | Tuning Frequency Bias of State Space Models. | Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson |
| 2025 | ICLR | HOPE for a Robust Parameterization of Long-memory State Space Models. | Annan Yu, Michael W. Mahoney, N. Benjamin Erichson |
| 2025 | ICML | Determinant Estimation under Memory Constraints and Neural Scaling Laws. | Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney |
| 2025 | ICML | Models of Heavy-Tailed Mechanistic Universality. | Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney |
| 2025 | ICML | Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. | Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang |
| 2025 | ICML | Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. | Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney |
| 2025 | ICML | QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache. | Rishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Richard Charles Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholami |
| 2024 | ACL | LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. | Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael W. Mahoney, Kurt Keutzer, Amir Gholami |
| 2024 | AISTATS | NoisyMix: Boosting Model Robustness to Common Corruptions. | N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney |
| 2024 | AISTATS | Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. | Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2024 | ICLR | Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. | Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot |
| 2024 | ICLR | Robustifying State-space Models for Long Sequences via Approximate Diagonalization. | Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson |
| 2024 | ICML | SqueezeLLM: Dense-and-Sparse Quantization. | Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer |
| 2024 | ICML | An LLM Compiler for Parallel Function Calling. | Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami |
| 2024 | ICML | Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. | S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang |
| 2024 | ICML | Towards Scalable and Versatile Weight Space Learning. | Konstantin Schrholt, Michael W. Mahoney, Damian Borth |
| 2024 | KDD | Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. | Michal Derezinski, Michael W. Mahoney |
| 2024 | VTS | Reliable edge machine learning hardware for scientific applications. | 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 |
| 2023 | AISTATS | Fast Feature Selection with Fairness Constraints. | Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney |
| 2023 | ECAI | Adaptive Self-Supervision Algorithms for Physics-Informed Neural Networks. | Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami |
| 2023 | ICLR | Learning differentiable solvers for systems with hard constraints. | Geoffrey Ngiar, Michael W. Mahoney, Aditi S. Krishnapriyan |
| 2023 | ICLR | Gradient Gating for Deep Multi-Rate Learning on Graphs. | T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra |
| 2023 | ICML | Learning Physical Models that Can Respect Conservation Laws. | Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney |
| 2023 | ICML | Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2023 | ICML | Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. | Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar |
| 2023 | ICML | A Three-regime Model of Network Pruning. | Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney |
| 2023 | KDD | Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. | Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney |
| 2023 | SC | Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. | Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney |
| 2022 | ICASSP | Integer-Only Zero-Shot Quantization for Efficient Speech Recognition. | Sehoon Kim, Amir Gholami, Zhewei Yao, Nicholas Lee, Patrick Wang, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W. Mahoney, Kurt Keutzer |
| 2022 | ICLR | Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. | Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtrik, Michael W. Mahoney, Martin Takc |
| 2022 | ICLR | Noisy Feature Mixup. | Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney |
| 2022 | ICLR | Long Expressive Memory for Sequence Modeling. | T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney |
| 2022 | ICML | Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. | Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney |
| 2022 | ICML | Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. | Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson |
| 2022 | ICML | GACT: Activation Compressed Training for Generic Network Architectures. | 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 |
| 2022 | ICML | AutoIP: A United Framework to Integrate Physics into Gaussian Processes. | Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2022 | ICML | Neurotoxin: Durable Backdoors in Federated Learning. | Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Prateek Mittal, Kannan Ramchandran, Joseph Gonzalez |
| 2022 | WACV | Hessian-Aware Pruning and Optimal Neural Implant. | Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Sehoon Kim, Michael W. Mahoney, Kurt Keutzer |
| 2021 | AAAI | ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. | Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney |
| 2021 | AISTATS | Good Classifiers are Abundant in the Interpolating Regime. | Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney |
| 2021 | COLT | Sparse sketches with small inversion bias. | Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney |
| 2021 | EMNLP | What's Hidden in a One-layer Randomly Weighted Transformer? | Sheng Shen, Zhewei Yao, Douwe Kiela, Kurt Keutzer, Michael W. Mahoney |
| 2021 | ICLR | Lipschitz Recurrent Neural Networks. | N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | ICLR | Sparse Quantized Spectral Clustering. | Zhenyu Liao, Romain Couillet, Michael W. Mahoney |
| 2021 | ICLR | Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. | Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney |
| 2021 | ICML | ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. | Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph Gonzalez |
| 2021 | ICML | Multiplicative Noise and Heavy Tails in Stochastic Optimization. | Liam Hodgkinson, Michael W. Mahoney |
| 2021 | ICML | I-BERT: Integer-only BERT Quantization. | Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer |
| 2021 | ICML | HAWQ-V3: Dyadic Neural Network Quantization. | Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer |
| 2021 | IJCAI | Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). | Michal Derezinski, Rajiv Khanna, Michael W. Mahoney |
| 2021 | KDD | Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism. | Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney |
| 2021 | UAI | LocalNewton: Reducing communication rounds for distributed learning. | Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney |
| 2021 | UAI | Stochastic continuous normalizing flows: training SDEs as ODEs. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2021 | UAI | Geometric rates of convergence for kernel-based sampling algorithms. | Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | SDM | Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. | N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney |
| 2020 | AAAI | Inefficiency of K-FAC for Large Batch Size Training. | Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney |
| 2020 | AAAI | Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. | Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer |
| 2020 | AISTATS | Bayesian experimental design using regularized determinantal point processes. | Michal Derezinski, Feynman T. Liang, Michael W. Mahoney |
| 2020 | AISTATS | Statistical guarantees for local graph clustering. | Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney |
| 2020 | AISTATS | Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. | Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney |
| 2020 | CVPR | ZeroQ: A Novel Zero Shot Quantization Framework. | Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer |
| 2020 | EMNLP | MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. | Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao |
| 2020 | ICML | Forecasting Sequential Data Using Consistent Koopman Autoencoders. | Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney |
| 2020 | ICML | Error Estimation for Sketched SVD via the Bootstrap. | Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney |
| 2020 | ICML | PowerNorm: Rethinking Batch Normalization in Transformers. | Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer |
| 2020 | ICPRAM | JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. | N. Benjamin Erichson, Zhewei Yao, Michael W. Mahoney |
| 2020 | SC | Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. | Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama |
| 2020 | SDM | Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. | Charles H. Martin, Michael W. Mahoney |
| 2020 | SDM | Second-order Optimization for Non-convex Machine Learning: an Empirical Study. | Peng Xu, Fred Roosta, Michael W. Mahoney |
| 2019 | COLT | Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. | Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth |
| 2019 | CVPR | Trust Region Based Adversarial Attack on Neural Networks. | Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael W. Mahoney |
| 2019 | ICCV | HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. | Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer |
| 2019 | ICML | Traditional and Heavy Tailed Self Regularization in Neural Network Models. | Michael W. Mahoney, Charles H. Martin |
| 2019 | KDD | Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. | Charles H. Martin, Michael W. Mahoney |
| 2019 | SDM | GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. | Sudhir B. Kylasa, Fred (Farbod) Roosta, Michael W. Mahoney, Ananth Grama |
| 2018 | AISTATS | FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. | Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney |
| 2018 | ICML | Out-of-sample extension of graph adjacency spectral embedding. | Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe |
| 2018 | ICML | Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. | Miles E. Lopes, Shusen Wang, Michael W. Mahoney |
| 2018 | KDD | Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. | Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff |
| 2017 | ACL | Skip-Gram - Zipf + Uniform = Vector Additivity. | Alex Gittens, Dimitris Achlioptas, Michael W. Mahoney |
| 2017 | ICML | Capacity Releasing Diffusion for Speed and Locality. | Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao |
| 2017 | ICML | Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. | Shusen Wang, Alex Gittens, Michael W. Mahoney |
| 2016 | ICALP | Approximating the Solution to Mixed Packing and Covering LPs in Parallel O˜(epsilon^{-3}) Time. | Michael W. Mahoney, Satish Rao, Di Wang, Peng Zhang |
| 2016 | ICALP | Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. | Di Wang, Satish Rao, Michael W. Mahoney |
| 2016 | ICML | A Simple and Strongly-Local Flow-Based Method for Cut Improvement. | Nate Veldt, David F. Gleich, Michael W. Mahoney |
| 2016 | SODA | Weighted SGD for | Jiyan Yang, Yinlam Chow, Christopher R, Michael W. Mahoney |
| 2015 | AISTATS | Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrm Method. | David G. Anderson, Simon S. Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu |
| 2015 | ICML | Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. | Garvesh Raskutti, Michael W. Mahoney |
| 2015 | KDD | Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. | David F. Gleich, Michael W. Mahoney |
| 2014 | CVPR | Random Laplace Feature Maps for Semigroup Kernels on Histograms. | Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney |
| 2014 | ICML | Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow. | David F. Gleich, Michael W. Mahoney |
| 2014 | ICML | A Statistical Perspective on Algorithmic Leveraging. | Ping Ma, Michael W. Mahoney, Bin Yu |
| 2014 | ICML | Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. | Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney |
| 2013 | ICDM | Tree-Like Structure in Large Social and Information Networks. | Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney |
| 2013 | ICML | Revisiting the Nystrom method for improved large-scale machine learning. | Alex Gittens, Michael W. Mahoney |
| 2013 | ICML | Robust Regression on MapReduce. | Xiangrui Meng, Michael W. Mahoney |
| 2013 | ICML | Quantile Regression for Large-scale Applications. | Jiyan Yang, Xiangrui Meng, Michael W. Mahoney |
| 2013 | SODA | The Fast Cauchy Transform and Faster Robust Linear Regression. | Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff |
| 2013 | STOC | Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression. | Xiangrui Meng, Michael W. Mahoney |
| 2012 | ICML | Fast approximation of matrix coherence and statistical leverage. | Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff |
| 2012 | ISAAC | On the Hyperbolicity of Small-World and Tree-Like Random Graphs. | Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney |
| 2012 | PODS | Approximate computation and implicit regularization for very large-scale data analysis. | Michael W. Mahoney |
| 2011 | ICML | Implementing regularization implicitly via approximate eigenvector computation. | Michael W. Mahoney, Lorenzo Orecchia |
| 2010 | WWW | Empirical comparison of algorithms for network community detection. | Jure Leskovec, Kevin J. Lang, Michael W. Mahoney |
| 2010 | UAI | Approximating Higher-Order Distances Using Random Projections. | Ping Li, Michael W. Mahoney, Yiyuan She |
| 2009 | SODA | An improved approximation algorithm for the column subset selection problem. | Christos Boutsidis, Michael W. Mahoney, Petros Drineas |
| 2008 | KDD | Unsupervised feature selection for principal components analysis. | Christos Boutsidis, Michael W. Mahoney, Petros Drineas |
| 2008 | WWW | Statistical properties of community structure in large social and information networks. | Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney |
| 2008 | SODA | Sampling algorithms and coresets for ℓ | Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney |
| 2007 | KDD | Feature selection methods for text classification. | Anirban Dasgupta, Petros Drineas, Boulos Harb, Vanja Josifovski, Michael W. Mahoney |
| 2006 | ESA | Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods. | Petros Drineas, Michael W. Mahoney, S. Muthukrishnan |
| 2006 | KDD | Tensor-CUR decompositions for tensor-based data. | Michael W. Mahoney, Mauro Maggioni, Petros Drineas |
| 2006 | SODA | Sampling algorithms for | Petros Drineas, Michael W. Mahoney, S. Muthukrishnan |
| 2006 | VLDB | Randomized Algorithms for Matrices and Massive Data Sets. | Petros Drineas, Michael W. Mahoney |
| 2005 | COLT | Approximating a Gram Matrix for Improved Kernel-Based Learning. | Petros Drineas, Michael W. Mahoney |
| 2005 | STACS | Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms. | Petros Drineas, Ravi Kannan, Michael W. Mahoney |
| 2003 | ISAAC | Rapid Mixing of Several Markov Chains for a Hard-Core Model. | Ravi Kannan, Michael W. Mahoney, Ravi Montenegro |