Mahoney, Michael W.

121 publications

ICLR 2025 A Statistical Framework for Ranking LLM-Based Chatbots Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney

ICML 2025 Determinant Estimation Under Memory Constraints and Neural Scaling Laws Siavash Ameli, Chris Van Der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney

TMLR 2025 Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney, Sherry Li, N. Benjamin Erichson

ICML 2025 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

ICML 2025 Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-Sampled Newton Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney

AISTATS 2025 Gated Recurrent Neural Networks with Weighted Time-Delay Feedback N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney

ICLR 2025 Gradient-Free Generation for Hard-Constrained Systems Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Bernie Wang

ICLR 2025 HOPE for a Robust Parameterization of Long-Memory State Space Models Annan Yu, Michael W. Mahoney, N. Benjamin Erichson

ICLR 2025 Mitigating Memorization in Language Models Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian Foster, Michael W. Mahoney

NeurIPS 2025 Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models Xiyuan Zhang, Danielle C. Maddix, Junming Yin, Nick Erickson, Abdul Fatir Ansari, Boran Han, Shuai Zhang, Leman Akoglu, Christos Faloutsos, Michael W. Mahoney, Cuixiong Hu, Huzefa Rangwala, George Karypis, Bernie Wang

ICML 2025 Models of Heavy-Tailed Mechanistic Universality Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney

NeurIPS 2025 Multipole Attention for Efficient Long Context Reasoning Coleman Richard Charles Hooper, Sebastian Zhao, Luca Manolache, Sehoon Kim, Michael W. Mahoney, Sophia Shao, Kurt Keutzer, Amir Gholami

ICML 2025 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

NeurIPS 2025 Spectral Estimation with Free Decompression Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Michael W. Mahoney

DMLR 2025 SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning Pu Ren, N. Benjamin Erichson, Junyi Guo, Shashank Subramanian, Omer San, Zarija Lukic, Michael W. Mahoney

ICLR 2025 Tuning Frequency Bias of State Space Models Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson

TMLR 2024 $\clubsuit$ CLOVER $\clubsuit$: Probabilistic Forecasting with Coherent Learning Objective Reparameterization Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, Oinam Nganba Meetei, Mengfei Cao, Michael W. Mahoney

NeurIPS 2024 AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-Wise Pruning of Large Language Models Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang

ICML 2024 An LLM Compiler for Parallel Function Calling Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami

TMLR 2024 Chronos: Learning the Language of Time Series Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang

ICLRW 2024 Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes Dynamics Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Bernie Wang, Andrew Stuart, Michael W. Mahoney

DMLR 2024 DMLR: Data-Centric Machine Learning Research - Past, Present and Future 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 A 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 Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson

NeurIPS 2024 Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney

ICLRW 2024 Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney

AISTATS 2024 Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels Da Long, Wei Xing, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael W. Mahoney

NeurIPSW 2024 Evaluating Loss Landscapes from a Topology Perspective Tiankai Xie, Caleb Geniesse, Jiaqing Chen, Yaoqing Yang, Dmitriy Morozov, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber

ICLR 2024 Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot

NeurIPS 2024 How Many Classifiers Do We Need? Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. Mahoney

NeurIPS 2024 KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami

NeurIPSW 2024 LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Hanyu Zhang, Chuck Arvin, Dmitry Efimov, Michael W. Mahoney, Dominique Perrault-Joncas, Shankar Ramasubramanian, Andrew Gordon Wilson, Malcolm Wolff

ICLR 2024 Robustifying State-Space Models for Long Sequences via Approximate Diagonalization Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson

NeurIPS 2024 Sharpness-Diversity Tradeoff: Improving Flat Ensembles with SharpBalance Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang

ICML 2024 SqueezeLLM: Dense-and-Sparse Quantization Sehoon Kim, Coleman Richard Charles Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer

ICML 2024 Towards Scalable and Versatile Weight Space Learning Konstantin Schürholt, Michael W. Mahoney, Damian Borth

ICML 2024 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

NeurIPSW 2024 Visualizing Loss Functions as Topological Landscape Profiles Caleb Geniesse, Jiaqing Chen, Tiankai Xie, Ge Shi, Yaoqing Yang, Dmitriy Morozov, Talita Perciano, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber

NeurIPSW 2024 ♠ SPADE ♠ Split Peak Attention DEcomposition Malcolm Wolff, Kin G. Olivares, Boris N. Oreshkin, Sunny Ruan, Sitan Yang, Abhinav Katoch, Shankar Ramasubramanian, Youxin Zhang, Michael W. Mahoney, Dmitry Efimov, Vincent Quenneville-Belair

NeurIPS 2023 A Heavy-Tailed Algebra for Probabilistic Programming Feynman T Liang, Liam Hodgkinson, Michael W. Mahoney

ICML 2023 A Three-Regime Model of Network Pruning Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney

ICML 2023 Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar

NeurIPSW 2023 Does In-Context Operator Learning Generalize to Domain-Shifted Settings? Jerry Weihong Liu, N. Benjamin Erichson, Kush Bhatia, Michael W. Mahoney, Christopher Re

AISTATS 2023 Fast Feature Selection with Fairness Constraints Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel Waddington, Tobias Friedrich, Michael W. Mahoney

ICLR 2023 Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra

ICLR 2023 Learning Differentiable Solvers for Systems with Hard Constraints Geoffrey Négiar, Michael W. Mahoney, Aditi Krishnapriyan

ICML 2023 Learning Physical Models That Can Respect Conservation Laws Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney

ICLRW 2023 Learning Physical Models That Can Respect Conservation Laws Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney

ICML 2023 Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson, Chris Van Der Heide, Fred Roosta, Michael W. Mahoney

NeurIPSW 2023 Rapid Fitting of Band-Excitation Piezoresponse Force Microscopy Using Physics Constrained Unsupervised Neural Networks Alibek T Kaliyev, Ryan F Forelli, Shuyu Qin, Yichen Guo, Seda Memik, Michael W. Mahoney, Amir Gholami, Nhan Tran, Philip Harris, Martin Takáč, Joshua Agar

NeurIPS 2023 Speculative Decoding with Big Little Decoder Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer

NeurIPS 2023 Temperature Balancing, Layer-Wise Weight Analysis, and Neural Network Training Yefan Zhou, Tianyu Pang, Keqin Liu, Charles Martin, Michael W. Mahoney, Yaoqing Yang

NeurIPS 2023 Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami

NeurIPS 2023 When Are Ensembles Really Effective? Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney

NeurIPS 2022 A Fast Post-Training Pruning Framework for Transformers Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami

JMLR 2022 Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney

ICLR 2022 Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takac

WACV 2022 Hessian-Aware Pruning and Optimal Neural Implant Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Sehoon Kim, Michael W. Mahoney, Kurt Keutzer

JMLR 2022 LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney

ICLR 2022 Long Expressive Memory for Sequence Modeling T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney

ICLR 2022 Noisy Feature Mixup Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney

NeurIPS 2022 Squeezeformer: An Efficient Transformer for Automatic Speech Recognition Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer

AAAI 2021 ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney

ICLR 2021 Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney

NeurIPS 2021 Characterizing Possible Failure Modes in Physics-Informed Neural Networks Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, Michael W. Mahoney

UAI 2021 Geometric Rates of Convergence for Kernel-Based Sampling Algorithms Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney

NeurIPS 2021 Hessian Eigenspectra of More Realistic Nonlinear Models Zhenyu Liao, Michael W. Mahoney

ICML 2021 I-BERT: Integer-Only BERT Quantization Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

JMLR 2021 Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin, Michael W. Mahoney

IJCAI 2021 Improved Guarantees and a Multiple-Descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract) Michal Derezinski, Rajiv Khanna, Michael W. Mahoney

JMLR 2021 Limit Theorems for Out-of-Sample Extensions of the Adjacency and Laplacian Spectral Embeddings Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe

ICLR 2021 Lipschitz Recurrent Neural Networks N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

UAI 2021 LocalNewton: Reducing Communication Rounds for Distributed Learning Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney

NeurIPS 2021 Newton-LESS: Sparsification Without Trade-Offs for the Sketched Newton Update Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney

NeurIPS 2021 Noisy Recurrent Neural Networks Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney

ICLR 2021 Sparse Quantized Spectral Clustering Zhenyu Liao, Romain Couillet, Michael W. Mahoney

NeurIPS 2021 Stateful ODE-Nets Using Basis Function Expansions Alejandro Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney

JMLR 2021 Statistical Guarantees for Local Graph Clustering Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney

UAI 2021 Stochastic Continuous Normalizing Flows: Training SDEs as ODEs Liam Hodgkinson, Chris Heide, Fred Roosta, Michael W. Mahoney

NeurIPS 2021 Taxonomizing Local Versus Global Structure in Neural Network Loss Landscapes Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney

NeurIPS 2020 A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent Zhenyu Liao, Romain Couillet, Michael W. Mahoney

NeurIPS 2020 A Statistical Framework for Low-Bitwidth Training of Deep Neural Networks Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E Gonzalez

NeurIPS 2020 Boundary Thickness and Robustness in Learning Models Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney

NeurIPS 2020 Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney

NeurIPS 2020 Exact Expressions for Double Descent and Implicit Regularization via Surrogate Random Design Michal Derezinski, Feynman T Liang, Michael W. Mahoney

NeurIPS 2020 HAWQ-V2: Hessian Aware Trace-Weighted Quantization of Neural Networks Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer

NeurIPS 2020 Improved Guarantees and a Multiple-Descent Curve for Column Subset Selection and the Nystrom Method Michal Derezinski, Rajiv Khanna, Michael W. Mahoney

AAAI 2020 Inefficiency of K-FAC for Large Batch Size Training Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney

NeurIPS 2020 Precise Expressions for Random Projections: Low-Rank Approximation and Randomized Newton Michal Derezinski, Feynman T Liang, Zhenyu Liao, Michael W. Mahoney

AAAI 2020 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

JMLR 2019 A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication Miles E. Lopes, Shusen Wang, Michael W. Mahoney

NeurIPS 2019 ANODEV2: A Coupled Neural ODE Framework Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros

NeurIPS 2019 Distributed Estimation of the Inverse Hessian by Determinantal Averaging Michal Derezinski, Michael W. Mahoney

COLT 2019 Minimax Experimental Design: Bridging the Gap Between Statistical and Worst-Case Approaches to Least Squares Regression Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth

JMLR 2019 Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds Shusen Wang, Alex Gittens, Michael W. Mahoney

AISTATS 2018 FLAG N' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney

NeurIPS 2018 GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney

NeurIPS 2018 Hessian-Based Analysis of Large Batch Training and Robustness to Adversaries Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney

ICML 2017 Capacity Releasing Diffusion for Speed and Locality Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao

ICML 2017 Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging Shusen Wang, Alex Gittens, Michael W. Mahoney

NeurIPS 2017 Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya

JMLR 2016 A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares Garvesh Raskutti, Michael W. Mahoney

NeurIPS 2016 Feature-Distributed Sparse Regression: A Screen-and-Clean Approach Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun

JMLR 2016 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney

JMLR 2016 Revisiting the Nyström Method for Improved Large-Scale Machine Learning Alex Gittens, Michael W. Mahoney

NeurIPS 2016 Sub-Sampled Newton Methods with Non-Uniform Sampling Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney

JMLR 2015 A Statistical Perspective on Algorithmic Leveraging Ping Ma, Michael W. Mahoney, Bin Yu

NeurIPS 2015 Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney

AISTATS 2015 Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method David G. Anderson, Simon S. Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu

CVPR 2014 Random Laplace Feature Maps for Semigroup Kernels on Histograms Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

JMLR 2014 Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning Toke J. Hansen, Michael W. Mahoney

JMLR 2012 A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi

JMLR 2012 Fast Approximation of Matrix Coherence and Statistical Leverage Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff

ICML 2012 Fast Approximation of Matrix Coherence and Statistical Leverage Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff

NeurIPS 2012 Semi-Supervised Eigenvectors for Locally-Biased Learning Toke Hansen, Michael W. Mahoney

ICML 2011 Implementing Regularization Implicitly via Approximate Eigenvector Computation Michael W. Mahoney, Lorenzo Orecchia

FnTML 2011 Randomized Algorithms for Matrices and Data Michael W. Mahoney

NeurIPS 2011 Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry, Michael W. Mahoney

UAI 2010 Approximating Higher-Order Distances Using Random Projections Ping Li, Michael W. Mahoney, Yiyuan She

NeurIPS 2010 CUR from a Sparse Optimization Viewpoint Jacob Bien, Ya Xu, Michael W. Mahoney

NeurIPS 2009 Unsupervised Feature Selection for the $k$-Means Clustering Problem Christos Boutsidis, Petros Drineas, Michael W. Mahoney

COLT 2005 Approximating a Gram Matrix for Improved Kernel-Based Learning Petros Drineas, Michael W. Mahoney

JMLR 2005 On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning Petros Drineas, Michael W. Mahoney