Michael W. Mahoney

525 papers A* 116A 24B 4C 1Misc 2Journal 348Unranked 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Agentic Test-Time Scaling for WebAgents. CoRR Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John, Surya Krishnapillai, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2026	J	`jnl`	FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction. CoRR Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte, Rochishnu Chowdhury, Rushil Paresh Desai, Wuyang Chen, Daniel Martin, Michael W. Mahoney
2026	J	`jnl`	Is Flow Matching Just Trajectory Replay for Sequential Data? CoRR Soon Hoe Lim, Shizheng Lin, Michael W. Mahoney, N. Benjamin Erichson
2026	J	`jnl`	Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis. CoRR Jiaqing Chen, Nicholas Hadler, Tiankai Xie, Rostyslav Hnatyshyn, Caleb Geniesse, Yaoqing Yang, Michael W. Mahoney, Talita Perciano, John F. Hartwig, Ross Maciejewski, Gunther H. Weber
2026	J	`jnl`	Learning to Discover Iterative Spectral Algorithms. CoRR Zihang Liu, Oleg Balabanov, Yaoqing Yang, Michael W. Mahoney
2026	J	`jnl`	On Neural Scaling Laws for Weather Emulation through Continual Training. CoRR Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov, Amir Gholami, Dmitriy Morozov, Michael W. Mahoney
2026	J	`jnl`	PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training. CoRR Shenghao Yang, Zhichao Wang, Oleg Balabanov, N. Benjamin Erichson, Michael W. Mahoney
2026	J	`jnl`	Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models [Special Issue on the Mathematics of Deep Learning]. IEEE Signal Process. Mag. Zhenyu Liao, Michael W. Mahoney
2026	J	`jnl`	Residual Context Diffusion Language Models. CoRR Yuezhou Hu, Harman Singh, Monishwaran Maheswaran, Haocheng Xi, Coleman Hooper, Jintao Zhang, Aditya Tomar, Michael W. Mahoney, Sewon Min, Mehrdad Farajtabar, Kurt Keutzer, Amir Gholami, Chenfeng Xu
2026	J	`jnl`	Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models. CoRR Rishabh Tiwari, Aditya Tomar, Udbhav Bamba, Monishwaran Maheswaran, Heng Yang, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2026	J	`jnl`	Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables. CoRR Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Shankar Ramasubramanian, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Mengfei Cao, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov
2026	J	`jnl`	Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials. CoRR Alex Morehead, Miruna T. Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan Perez, Samuel M. Blau, Steven Farrell, Wahid Bhimji, Anubhav Jain, Hrushikesh Sahasrabuddhe, Pietro Lio, Tommi S. Jaakkola, Rafael Gómez-Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney
2026	J	`jnl`	Zero-shot Forecasting by Simulation Alone. CoRR Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam, Malcolm Wolff, Wenhao Pan, Shankar Ramasubramanian, Kin G. Olivares, Tatiana Konstantinova, Andres Potapczynski, Mengfei Cao, Dmitry Efimov, Michael W. Mahoney, Andrew Gordon Wilson
2025	A*	`conf`	A Statistical Framework for Ranking LLM-based Chatbots. ICLR Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney
2025	J	`jnl`	Accelerating scientific discovery with the common task framework. CoRR J. Nathan Kutz, Peter W. Battaglia, Michael P. Brenner, Kevin Carlberg, Aric A. Hagberg, Shirley Ho, Stephan Hoyer, Henning Lange, Hod Lipson, Michael W. Mahoney, Frank Noé, Max Welling, Laure Zanna, Francis Zhu, Steven L. Brunton
2025	J	`jnl`	Advancing Data-Driven Broadband Seismic Wavefield Simulation With Multiconditional Diffusion Model. IEEE Trans. Geosci. Remote. Sens. Zhengfa Bi, Nori Nakata, Rie Nakata, Pu Ren, Xinming Wu, Michael W. Mahoney
2025	J	`jnl`	Anatomy of High-Performance Column-Pivoted QR Decomposition. CoRR Maksim Melnichenko, Riley Murray, William Killian, James Demmel, Michael W. Mahoney, Piotr Luszczek, Mark Gates
2025	J	`jnl`	Arbitrage: Efficient Reasoning via Advantage-Aware Speculation. CoRR Monishwaran Maheswaran, Rishabh Tiwari, Yuezhou Hu, Kerem Dilmen, Coleman Hooper, Haocheng Xi, Nicholas Lee, Mehrdad Farajtabar, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models. CoRR Minseo Kim, Coleman Hooper, Aditya Tomar, Chenfeng Xu, Mehrdad Farajtabar, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT). SIAM J. Matrix Anal. Appl. Maksim Melnichenko, Oleg Balabanov, Riley Murray, James Demmel, Michael W. Mahoney, Piotr Luszczek
2025	A*	`conf`	Determinant Estimation under Memory Constraints and Neural Scaling Laws. ICML Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney
2025	J	`jnl`	Determinant Estimation under Memory Constraints and Neural Scaling Laws. CoRR Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney
2025	J	`jnl`	Does Multimodality Lead to Better Time Series Forecasting? CoRR Xiyuan Zhang, Boran Han, Haoyang Fang, Abdul Fatir Ansari, Shuai Zhang, Danielle C. Maddix, Cuixiong Hu, Andrew Gordon Wilson, Michael W. Mahoney, Hao Wang, Yan Liu, Huzefa Rangwala, George Karypis, Bernie Wang
2025	J	`jnl`	ETS: Efficient Tree Search for Inference-Time Scaling. CoRR Coleman Hooper, Sehoon Kim, Suhong Moon, Kerem Dilmen, Monishwaran Maheswaran, Nicholas Lee, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models. CoRR Ethan Baron, Boris N. Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
2025	J	`jnl`	Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. Trans. Mach. Learn. Res. Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney, Xiaoye Sherry Li, N. Benjamin Erichson
2025	J	`jnl`	End-to-End Probabilistic Framework for Learning with Hard Constraints. CoRR Utkarsh Utkarsh, Danielle C. Maddix, Ruijun Ma, Michael W. Mahoney, Yuyang Wang
2025	A*	`conf`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. ICML 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	J	`jnl`	FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems. CoRR N. Benjamin Erichson, Vinicius Mikuni, Dongwei Lyu, Yang Gao, Omri Azencot, Soon Hoe Lim, Michael W. Mahoney
2025	J	`jnl`	Forking-Sequences. CoRR Willa Potosnak, Malcolm Wolff, Boris N. Oreshkin, Mengfei Cao, Michael W. Mahoney, Dmitry Efimov, Kin G. Olivares
2025	J	`jnl`	Foundation Models for Discovery and Exploration in Chemical Space. CoRR Alexius Wadell, Anoushka Bhutani, Victor Azumah, Austin R. Ellis-Mohr, Celia Kelly, Hancheng Zhao, Anuj K. Nayak, Kareem Hegazy, Alexander Brace, Hongyi Lin, Murali Emani, Venkatram Vishwanath, Kevin Gering, Melisa Alkan, Tom Gibbs, Jack C. Wells, Lav R. Varshney, Bharath Ramsundar, Karthik Duraisamy, Michael W. Mahoney, Arvind Ramanathan, Venkatasubramanian Viswanathan
2025	A*	`conf`	Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. ICML Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney
2025	J	`jnl`	Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. CoRR Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney
2025	A	`conf`	Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. AISTATS N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney
2025	A*	`conf`	Gradient-Free Generation for Hard-Constrained Systems. ICLR Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Bernie Wang
2025	A*	`conf`	HOPE for a Robust Parameterization of Long-memory State Space Models. ICLR Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
2025	J	`jnl`	HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems. CoRR Jialin Song, Yingheng Tang, Pu Ren, Shintaro Takayoshi, Saurabh Sawant, Yujie Zhu, Jia-Mian Hu, Andy Nonaka, Michael W. Mahoney, N. Benjamin Erichson, Zhi Yao
2025	J	`jnl`	LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics. IEEE Computer Graphics and Applications Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Chaudhari, John Kevin Cava, Michael W. Mahoney, Talita Perciano, Gunther H. Weber, Ross Maciejewski
2025	J	`jnl`	MatterChat: A Multi-Modal LLM for Material Science. CoRR Yingheng Tang, Wenbin Xu, Jie Cao, Jianzhu Ma, Weilu Gao, Steven Farrell, N. Benjamin Erichson, Michael W. Mahoney, Andy Nonaka, Zhi Yao
2025	A*	`conf`	Mitigating Memorization in Language Models. ICLR Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C. Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney
2025	J	`jnl`	Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models. CoRR 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
2025	J	`jnl`	Modeling Non-Ergodic Path Effects Using Conditional Generative Model for Fourier Amplitude Spectra. CoRR Maxime Lacour, Pu Ren, Rie Nakata, Nori Nakata, Michael W. Mahoney
2025	A*	`conf`	Models of Heavy-Tailed Mechanistic Universality. ICML Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney
2025	J	`jnl`	Models of Heavy-Tailed Mechanistic Universality. CoRR Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney
2025	J	`jnl`	Multipole Attention for Efficient Long Context Reasoning. CoRR Coleman Hooper, Sebastian Zhao, Luca Manolache, Sehoon Kim, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	Neural equilibria for long-term prediction of nonlinear conservation laws. CoRR Jose Antonio Lara Benitez, Junyi Guo, Kareem Hegazy, Ivan Dokmanic, Michael W. Mahoney, Maarten V. de Hoop
2025	J	`jnl`	Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming. CoRR Yihang Gao, Michael K. Ng, Michael W. Mahoney, Sen Na
2025	J	`jnl`	Paving the way for scientific foundation models: enhancing generalization and robustness in PDEs with constraint-aware pre-training. CoRR Amin Totounferoush, Serge Kotchourko, Michael W. Mahoney, Steffen Staab
2025	J	`jnl`	Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting. CoRR Kareem Hegazy, Michael W. Mahoney, N. Benjamin Erichson
2025	J	`jnl`	Preconditioning via Randomized Range Deflation (RandRAND). CoRR Oleg Balabanov, Caleb Ju, Kaiwen He, Aryaman Jeendgar, Michael W. Mahoney
2025	A*	`conf`	QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache. ICML Rishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Richard Charles Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache. CoRR Rishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models. CoRR Zhenyu Liao, Michael W. Mahoney
2025	J	`jnl`	Removing Watermarks with Partial Regeneration using Semantic Information. CoRR Krti Tallam, John Kevin Cava, Caleb Geniesse, N. Benjamin Erichson, Michael W. Mahoney
2025	J	`jnl`	SciML Agents: Write the Solver, Not the Solution. CoRR Saarth Gaonkar, Xiang Zheng, Haocheng Xi, Rishabh Tiwari, Kurt Keutzer, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami
2025	J	`jnl`	Spectral Estimation with Free Decompression. CoRR Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Michael W. Mahoney
2025	—	`conf`	Squeezed Attention: Accelerating Long Context Length LLM Inference. ACL (1) Coleman Richard Charles Hooper, Sehoon Kim, Hiva Mohammadzadeh, Monishwaran Maheswaran, Sebastian Zhao, June Paik, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2025	J	`jnl`	Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming. J. Mach. Learn. Res. Sen Na, Michael W. Mahoney
2025	J	`jnl`	SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning. J. Data-centric Mach. Learn. Res. Pu Ren, N. Benjamin Erichson, Junyi Guo, Shashank Subramanian, Omer San, Zarija Lukic, Michael W. Mahoney
2025	J	`jnl`	Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems. SIAM J. Matrix Anal. Appl. Younghyun Cho, James Demmel, Michal Derezinski, Haoyun Li, Hengrui Luo, Michael W. Mahoney, Riley Murray
2025	J	`jnl`	TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting. CoRR Zhiyuan Zhao, Sitan Yang, Kin G. Olivares, Boris N. Oreshkin, Stan Vitebsky, Michael W. Mahoney, B. Aditya Prakash, Dmitry Efimov
2025	J	`jnl`	The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators. CoRR Mansi Sakarvadia, Kareem Hegazy, Amin Totounferoush, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney
2025	J	`jnl`	The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification. CoRR Luke Bhan, Hanyu Zhang, Andrew Gordon Wilson, Michael W. Mahoney, Chuck Arvin
2025	A*	`conf`	Tuning Frequency Bias of State Space Models. ICLR Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson
2025	J	`jnl`	Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning. CoRR Mara Daniels, Liam Hodgkinson, Michael W. Mahoney
2025	J	`jnl`	Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility. CoRR Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang
2025	J	`jnl`	Understanding the Implicit Biases of Design Choices for Time Series Foundation Models. CoRR Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang
2025	J	`jnl`	Using Pre-trained LLMs for Multivariate Time Series Forecasting. CoRR Malcolm Wolff, Shenghao Yang, Kari Torkkola, Michael W. Mahoney
2025	J	`jnl`	XQuant: Breaking the Memory Wall for LLM Inference with KV Cache Rematerialization. CoRR Aditya Tomar, Coleman Hooper, Minjae Lee, Haocheng Xi, Rishabh Tiwari, Wonjun Kang, Luca Manolache, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	A Statistical Framework for Ranking LLM-Based Chatbots. CoRR Siavash Ameli, Siyuan Zhuang, Ion Stoica, Michael W. Mahoney
2024	J	`jnl`	AI and Memory Wall. CoRR Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer
2024	J	`jnl`	AI and Memory Wall. IEEE Micro Amir Gholami, Zhewei Yao, Sehoon Kim, Coleman Hooper, Michael W. Mahoney, Kurt Keutzer
2024	A*	`conf`	AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. NeurIPS Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang
2024	J	`jnl`	AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. CoRR Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang
2024	A*	`conf`	An LLM Compiler for Parallel Function Calling. ICML Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	Chronos: Learning the Language of Time Series. CoRR 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
2024	J	`jnl`	Chronos: Learning the Language of Time Series. Trans. Mach. Learn. Res. 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, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang
2024	J	`jnl`	Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. CoRR Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney
2024	J	`jnl`	DMLR: Data-centric Machine Learning Research - Past, Present and Future. J. Data-centric Mach. Learn. Res. 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 Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
2024	A*	`conf`	Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning. NeurIPS Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney
2024	J	`jnl`	Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning. CoRR Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney
2024	J	`jnl`	Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. CoRR Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney, Xiaoye S. Li, N. Benjamin Erichson
2024	J	`jnl`	End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. ACM Trans. Reconfigurable Technol. Syst. Javier Campos, Jovan Mitrevski, Nhan Tran, Zhen Dong, Amir Gholaminejad, Michael W. Mahoney, Javier M. Duarte
2024	J	`jnl`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR 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, Yuyang Wang
2024	A	`conf`	Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2024	J	`jnl`	Evaluating Loss Landscapes from a Topology Perspective. CoRR Tiankai Xie, Caleb Geniesse, Jiaqing Chen, Yaoqing Yang, Dmitriy Morozov, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber
2024	J	`jnl`	Forecasting high-dimensional spatio-temporal systems from sparse measurements. Mach. Learn. Sci. Technol. Jialin Song, Zezheng Song, Pu Ren, N. Benjamin Erichson, Michael W. Mahoney, Xiaoye S. Li
2024	J	`jnl`	Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. SIAM J. Optim. Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar
2024	A*	`conf`	Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. ICLR Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot
2024	J	`jnl`	Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. CoRR Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Yuyang Wang
2024	A*	`conf`	How many classifiers do we need? NeurIPS Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. Mahoney
2024	J	`jnl`	How many classifiers do we need? CoRR Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. Mahoney
2024	A*	`conf`	KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization. NeurIPS Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization. CoRR Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami
2024	—	`conf`	LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. ACL (Findings) Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. CoRR Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data. CoRR Hanyu Zhang, Chuck Arvin, Dmitry Efimov, Michael W. Mahoney, Dominique Perrault-Joncas, Shankar Ramasubramanian, Andrew Gordon Wilson, Malcolm Wolff
2024	J	`jnl`	Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. CoRR 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
2024	J	`jnl`	LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics. CoRR Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Chaudhari, John Kevin Cava, Michael W. Mahoney, Talita Perciano, Gunther H. Weber, Ross Maciejewski
2024	J	`jnl`	Mitigating Memorization In Language Models. CoRR Mansi Sakarvadia, Aswathy Ajith, Arham Khan, Nathaniel Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney
2024	A	`conf`	NoisyMix: Boosting Model Robustness to Common Corruptions. AISTATS N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney
2024	A*	`conf`	Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. KDD Michal Derezinski, Michael W. Mahoney
2024	J	`jnl`	Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. CoRR Michal Derezinski, Michael W. Mahoney
2024	Misc	`conf`	Reliable edge machine learning hardware for scientific applications. VTS 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
2024	J	`jnl`	Reliable edge machine learning hardware for scientific applications. CoRR 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
2024	A*	`conf`	Robustifying State-space Models for Long Sequences via Approximate Diagonalization. ICLR Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson
2024	J	`jnl`	SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. CoRR Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney
2024	A*	`conf`	Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance. NeurIPS Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang
2024	J	`jnl`	Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance. CoRR Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang
2024	A*	`conf`	SqueezeLLM: Dense-and-Sparse Quantization. ICML Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer
2024	J	`jnl`	Squeezed Attention: Accelerating Long Context Length LLM Inference. CoRR Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Monishwaran Maheswaran, June Paik, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024	J	`jnl`	There is HOPE to Avoid HiPPOs for Long-memory State Space Models. CoRR Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
2024	A*	`conf`	Towards Scalable and Versatile Weight Space Learning. ICML Konstantin Schürholt, Michael W. Mahoney, Damian Borth
2024	J	`jnl`	Towards Scalable and Versatile Weight Space Learning. CoRR Konstantin Schürholt, Michael W. Mahoney, Damian Borth
2024	J	`jnl`	Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. CoRR Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar
2024	J	`jnl`	Tuning Frequency Bias of State Space Models. CoRR Annan Yu, Dongwei Lyu, Soon Hoe Lim, Michael W. Mahoney, N. Benjamin Erichson
2024	A*	`conf`	Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. ICML S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang
2024	J	`jnl`	Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. CoRR S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang
2024	J	`jnl`	Visualizing Loss Functions as Topological Landscape Profiles. CoRR Caleb Geniesse, Jiaqing Chen, Tiankai Xie, Ge Shi, Yaoqing Yang, Dmitriy Morozov, Talita Perciano, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber
2024	J	`jnl`	WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning. CoRR Dongwei Lyu, Rie Nakata, Pu Ren, Michael W. Mahoney, Arben Pitarka, Nori Nakata, N. Benjamin Erichson
2024	J	`jnl`	♠ SPADE ♠ Split Peak Attention DEcomposition. CoRR 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-Bélair
2024	J	`jnl`	♣ CLOVER ♣: Probabilistic Forecasting with Coherent Learning Objective Reparameterization. Trans. Mach. Learn. Res. Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, Oinam Nganba Meetei, Mengfei Cao, Michael W. Mahoney
2023	A*	`conf`	A Heavy-Tailed Algebra for Probabilistic Programming. NeurIPS Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
2023	J	`jnl`	A Heavy-Tailed Algebra for Probabilistic Programming. CoRR Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
2023	J	`jnl`	A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
2023	A*	`conf`	A Three-regime Model of Network Pruning. ICML Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney
2023	J	`jnl`	A Three-regime Model of Network Pruning. CoRR Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney
2023	A	`conf`	Adaptive Self-Supervision Algorithms for Physics-Informed Neural Networks. ECAI Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami
2023	J	`jnl`	An LLM Compiler for Parallel Function Calling. CoRR Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2023	J	`jnl`	Big Little Transformer Decoder. CoRR Sehoon Kim, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer
2023	J	`jnl`	CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT). CoRR Maksim Melnichenko, Oleg Balabanov, Riley Murray, James Demmel, Michael W. Mahoney, Piotr Luszczek
2023	A*	`conf`	Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. ICML Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar
2023	J	`jnl`	Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. CoRR Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar
2023	J	`jnl`	DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR 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 Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
2023	J	`jnl`	End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. CoRR Javier Campos, Zhen Dong, Javier M. Duarte, Amir Gholami, Michael W. Mahoney, Jovan Mitrevski, Nhan Tran
2023	J	`jnl`	Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2023	—	`conf`	Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. SC Workshops Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney
2023	J	`jnl`	Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. CoRR Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney
2023	A	`conf`	Fast Feature Selection with Fairness Constraints. AISTATS Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
2023	J	`jnl`	Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance. SIAM Rev. Kimon Fountoulakis, Meng Liu, David F. Gleich, Michael W. Mahoney
2023	J	`jnl`	Full Stack Optimization of Transformer Inference: a Survey. CoRR 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
2023	J	`jnl`	GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting. CoRR Sitan Yang, Malcolm Wolff, Shankar Ramasubramanian, Vincent Quenneville-Bélair, Ronak Metha, Michael W. Mahoney
2023	J	`jnl`	Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. CoRR Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot
2023	A*	`conf`	Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2023	J	`jnl`	Hessian averaging in stochastic Newton methods achieves superlinear convergence. Math. Program. Sen Na, Michal Derezinski, Michael W. Mahoney
2023	A*	`conf`	Learning Physical Models that Can Respect Conservation Laws. ICML Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney
2023	J	`jnl`	Learning Physical Models that Can Respect Conservation Laws. CoRR Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney
2023	A*	`conf`	Learning differentiable solvers for systems with hard constraints. ICLR Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
2023	A*	`conf`	Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2023	J	`jnl`	Multi-scale Local Network Structure Critically Impacts Epidemic Spread and Interventions. CoRR Omar Eldaghar, Michael W. Mahoney, David F. Gleich
2023	J	`jnl`	Probabilistic Forecasting with Coherent Aggregation. CoRR Geoffrey Négiar, Ruijun Ma, O. Nganba Meetei, Mengfei Cao, Michael W. Mahoney
2023	J	`jnl`	Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. CoRR 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
2023	J	`jnl`	Robustifying State-space Models for Long Sequences via Approximate Diagonalization. CoRR Annan Yu, Arnur Nigmetov, Dmitriy Morozov, Michael W. Mahoney, N. Benjamin Erichson
2023	A*	`conf`	Speculative Decoding with Big Little Decoder. NeurIPS Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer
2023	J	`jnl`	SqueezeLLM: Dense-and-Sparse Quantization. CoRR Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer
2023	J	`jnl`	SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning. CoRR Pu Ren, N. Benjamin Erichson, Shashank Subramanian, Omer San, Zarija Lukic, Michael W. Mahoney
2023	J	`jnl`	Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems. CoRR Younghyun Cho, James Weldon Demmel, Michal Derezinski, Haoyun Li, Hengrui Luo, Michael W. Mahoney, Riley J. Murray
2023	A*	`conf`	Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. NeurIPS Yefan Zhou, Tianyu Pang, Keqin Liu, Charles H. Martin, Michael W. Mahoney, Yaoqing Yang
2023	J	`jnl`	Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. CoRR Yefan Zhou, Tianyu Pang, Keqin Liu, Charles H. Martin, Michael W. Mahoney, Yaoqing Yang
2023	A*	`conf`	Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. KDD Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney
2023	J	`jnl`	The Interpolating Information Criterion for Overparameterized Models. CoRR Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
2023	A*	`conf`	Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. NeurIPS Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami
2023	J	`jnl`	Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. CoRR Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami
2023	A*	`conf`	When are ensembles really effective? NeurIPS Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney
2023	J	`jnl`	When are ensembles really effective? CoRR Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney
2022	A*	`conf`	A Fast Post-Training Pruning Framework for Transformers. NeurIPS Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami
2022	J	`jnl`	A Fast Post-Training Pruning Framework for Transformers. CoRR Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami
2022	J	`jnl`	Adaptive Self-supervision Algorithms for Physics-informed Neural Networks. CoRR Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami
2022	J	`jnl`	Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. J. Mach. Learn. Res. Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney
2022	J	`jnl`	Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. CoRR Sen Na, Michael W. Mahoney
2022	A*	`conf`	AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2022	J	`jnl`	AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2022	A*	`conf`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2022	J	`jnl`	Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. CoRR Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney
2022	J	`jnl`	Fast Feature Selection with Fairness Constraints. CoRR Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen, Daniel G. Waddington, Tobias Friedrich, Michael W. Mahoney
2022	A*	`conf`	Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. ICML Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson
2022	J	`jnl`	Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. CoRR Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
2022	J	`jnl`	GACT: Activation Compressed Training for General Architectures. CoRR 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	A*	`conf`	GACT: Activation Compressed Training for Generic Network Architectures. ICML 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	J	`jnl`	Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. CoRR N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney
2022	A*	`conf`	Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
2022	J	`jnl`	Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2022	J	`jnl`	Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence. CoRR Sen Na, Michal Derezinski, Michael W. Mahoney
2022	A	`conf`	Hessian-Aware Pruning and Optimal Neural Implant. WACV Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Sehoon Kim, Michael W. Mahoney, Kurt Keutzer
2022	Misc	`conf`	Integer-Only Zero-Shot Quantization for Efficient Speech Recognition. ICASSP Sehoon Kim, Amir Gholami, Zhewei Yao, Nicholas Lee, Patrick Wang, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W. Mahoney, Kurt Keutzer
2022	J	`jnl`	LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. J. Mach. Learn. Res. Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney
2022	J	`jnl`	Learning continuous models for continuous physics. CoRR Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney
2022	J	`jnl`	Learning differentiable solvers for systems with hard constraints. CoRR Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
2022	A*	`conf`	Long Expressive Memory for Sequence Modeling. ICLR T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
2022	J	`jnl`	Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2022	A*	`conf`	Neurotoxin: Durable Backdoors in Federated Learning. ICML Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Prateek Mittal, Kannan Ramchandran, Joseph Gonzalez
2022	J	`jnl`	Neurotoxin: Durable Backdoors in Federated Learning. CoRR Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, Prateek Mittal
2022	J	`jnl`	Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. EURO J. Comput. Optim. Fred Roosta, Yang Liu, Peng Xu, Michael W. Mahoney
2022	A*	`conf`	Noisy Feature Mixup. ICLR Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney
2022	J	`jnl`	NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. CoRR N. Benjamin Erichson, Soon Hoe Lim, Francisco Utrera, Winnie Xu, Ziang Cao, Michael W. Mahoney
2022	A*	`conf`	Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. NeurIPS Sehoon Kim, Amir Gholami, Albert E. Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer
2022	J	`jnl`	Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. CoRR Sehoon Kim, Amir Gholami, Albert E. Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer
2022	J	`jnl`	The Sky Above The Clouds. CoRR 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
2021	J	`jnl`	A Differential Geometry Perspective on Orthogonal Recurrent Models. CoRR Omri Azencot, N. Benjamin Erichson, Mirela Ben-Chen, Michael W. Mahoney
2021	J	`jnl`	A Survey of Quantization Methods for Efficient Neural Network Inference. CoRR Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer
2021	A*	`conf`	ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. AAAI Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney
2021	A*	`conf`	ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. ICML Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph Gonzalez
2021	J	`jnl`	ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. CoRR Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph E. Gonzalez
2021	A*	`conf`	Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. ICLR Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
2021	A*	`conf`	Characterizing possible failure modes in physics-informed neural networks. NeurIPS Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
2021	J	`jnl`	Characterizing possible failure modes in physics-informed neural networks. CoRR Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
2021	J	`jnl`	Compressing Deep ODE-Nets using Basis Function Expansions. CoRR Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
2021	J	`jnl`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2021	J	`jnl`	Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
2021	A	`conf`	Geometric rates of convergence for kernel-based sampling algorithms. UAI Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney
2021	A	`conf`	Good Classifiers are Abundant in the Interpolating Regime. AISTATS Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney
2021	A*	`conf`	HAWQ-V3: Dyadic Neural Network Quantization. ICML Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer
2021	A*	`conf`	Hessian Eigenspectra of More Realistic Nonlinear Models. NeurIPS Zhenyu Liao, Michael W. Mahoney
2021	J	`jnl`	Hessian Eigenspectra of More Realistic Nonlinear Models. CoRR Zhenyu Liao, Michael W. Mahoney
2021	J	`jnl`	Hessian-Aware Pruning and Optimal Neural Implant. CoRR Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Michael W. Mahoney, Kurt Keutzer
2021	A*	`conf`	I-BERT: Integer-only BERT Quantization. ICML Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer
2021	J	`jnl`	I-BERT: Integer-only BERT Quantization. CoRR Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer
2021	J	`jnl`	Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. J. Mach. Learn. Res. Charles H. Martin, Michael W. Mahoney
2021	A*	`conf`	Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). IJCAI Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2021	—	`conf`	Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. IEEE BigData Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2021	J	`jnl`	Inexact Nonconvex Newton-Type Methods. INFORMS J. Optim. Zhewei Yao, Peng Xu, Fred Roosta, Michael W. Mahoney
2021	J	`jnl`	Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR 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
2021	J	`jnl`	Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. J. Mach. Learn. Res. Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
2021	A*	`conf`	Lipschitz Recurrent Neural Networks. ICLR N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Liam Hodgkinson, Michael W. Mahoney
2021	J	`jnl`	LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
2021	A	`conf`	LocalNewton: Reducing communication rounds for distributed learning. UAI Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
2021	J	`jnl`	Long Expressive Memory for Sequence Modeling. CoRR T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
2021	J	`jnl`	MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models. CoRR Zhewei Yao, Linjian Ma, Sheng Shen, Kurt Keutzer, Michael W. Mahoney
2021	A*	`conf`	Multiplicative Noise and Heavy Tails in Stochastic Optimization. ICML Liam Hodgkinson, Michael W. Mahoney
2021	A*	`conf`	Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. NeurIPS Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney
2021	J	`jnl`	Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. CoRR Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney
2021	A	`conf`	Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. SDM N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney
2021	J	`jnl`	Noisy Feature Mixup. CoRR Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney
2021	A*	`conf`	Noisy Recurrent Neural Networks. NeurIPS Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
2021	J	`jnl`	Noisy Recurrent Neural Networks. CoRR Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
2021	J	`jnl`	Parallel and Communication Avoiding Least Angle Regression. SIAM J. Sci. Comput. Swapnil Das, James Demmel, Kimon Fountoulakis, Laura Grigori, Michael W. Mahoney, Shenghao Yang
2021	J	`jnl`	Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics. CoRR Charles H. Martin, Michael W. Mahoney
2021	—	`ed.`	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. AAAI Spring Symposium: MLPS Jonghyun Lee, Eric F. Darve, Peter K. Kitanidis, Michael W. Mahoney, Anuj Karpatne, Matthew W. Farthing, Tyler J. Hesser
2021	J	`jnl`	Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition. CoRR Sehoon Kim, Amir Gholami, Zhewei Yao, Aniruddha Nrusimha, Bohan Zhai, Tianren Gao, Michael W. Mahoney, Kurt Keutzer
2021	A*	`conf`	Sparse Quantized Spectral Clustering. ICLR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2021	A*	`conf`	Sparse sketches with small inversion bias. COLT Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
2021	A*	`conf`	Stateful ODE-Nets using Basis Function Expansions. NeurIPS Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
2021	J	`jnl`	Statistical guarantees for local graph clustering. J. Mach. Learn. Res. Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney
2021	A	`conf`	Stochastic continuous normalizing flows: training SDEs as ODEs. UAI Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2021	A*	`conf`	Taxonomizing local versus global structure in neural network loss landscapes. NeurIPS Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2021	J	`jnl`	Taxonomizing local versus global structure in neural network loss landscapes. CoRR Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2021	A*	`conf`	Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism. KDD Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney
2021	—	`conf`	What's Hidden in a One-layer Randomly Weighted Transformer? EMNLP (1) Sheng Shen, Zhewei Yao, Douwe Kiela, Kurt Keutzer, Michael W. Mahoney
2021	J	`jnl`	What's Hidden in a One-layer Randomly Weighted Transformer? CoRR Sheng Shen, Zhewei Yao, Douwe Kiela, Kurt Keutzer, Michael W. Mahoney
2020	A*	`conf`	A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. NeurIPS Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez
2020	J	`jnl`	A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. CoRR Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez
2020	A*	`conf`	A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. NeurIPS Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. CoRR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. CoRR Zhewei Yao, Amir Gholami, Sheng Shen, Kurt Keutzer, Michael W. Mahoney
2020	J	`jnl`	Adversarially-Trained Deep Nets Transfer Better. CoRR Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, Michael W. Mahoney
2020	A	`conf`	Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. AISTATS Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney
2020	J	`jnl`	Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. CoRR Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney
2020	A	`conf`	Bayesian experimental design using regularized determinantal point processes. AISTATS Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2020	J	`jnl`	Benchmarking Semi-supervised Federated Learning. CoRR Zhengming Zhang, Zhewei Yao, Yaoqing Yang, Yujun Yan, Joseph E. Gonzalez, Michael W. Mahoney
2020	A*	`conf`	Boundary thickness and robustness in learning models. NeurIPS Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020	J	`jnl`	Boundary thickness and robustness in learning models. CoRR Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020	J	`jnl`	Continuous-in-Depth Neural Networks. CoRR Alejandro F. Queiruga, N. Benjamin Erichson, Dane Taylor, Michael W. Mahoney
2020	A*	`conf`	Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. NeurIPS Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney
2020	J	`jnl`	Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. CoRR Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney
2020	J	`jnl`	Determinantal Point Processes in Randomized Numerical Linear Algebra. CoRR Michal Derezinski, Michael W. Mahoney
2020	A*	`conf`	Error Estimation for Sketched SVD via the Bootstrap. ICML Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
2020	J	`jnl`	Error Estimation for Sketched SVD via the Bootstrap. CoRR Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
2020	A*	`conf`	Exact expressions for double descent and implicit regularization via surrogate random design. NeurIPS Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2020	J	`jnl`	Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism. CoRR Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney
2020	J	`jnl`	Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance. CoRR Kimon Fountoulakis, Meng Liu, David F. Gleich, Michael W. Mahoney
2020	A*	`conf`	Forecasting Sequential Data Using Consistent Koopman Autoencoders. ICML Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney
2020	J	`jnl`	Forecasting Sequential Data using Consistent Koopman Autoencoders. CoRR Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney
2020	J	`jnl`	Good linear classifiers are abundant in the interpolating regime. CoRR Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney
2020	A*	`conf`	HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. NeurIPS Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2020	J	`jnl`	HAWQV3: Dyadic Neural Network Quantization. CoRR Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer
2020	A	`conf`	Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. SDM Charles H. Martin, Michael W. Mahoney
2020	A*	`conf`	Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2020	J	`jnl`	Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. CoRR Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2020	A*	`conf`	Inefficiency of K-FAC for Large Batch Size Training. AAAI Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2020	C	`conf`	JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. ICPRAM N. Benjamin Erichson, Zhewei Yao, Michael W. Mahoney
2020	J	`jnl`	Lipschitz Recurrent Neural Networks. CoRR N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Michael W. Mahoney
2020	—	`conf`	MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. EMNLP (1) Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao
2020	J	`jnl`	MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. CoRR Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao
2020	J	`jnl`	Multiplicative noise and heavy tails in stochastic optimization. CoRR Liam Hodgkinson, Michael W. Mahoney
2020	A	`conf`	Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. SC Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama
2020	J	`jnl`	Newton-type methods for non-convex optimization under inexact Hessian information. Math. Program. Peng Xu, Fred Roosta, Michael W. Mahoney
2020	J	`jnl`	Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. CoRR N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney
2020	—	`conf`	OverSketched Newton: Fast Convex Optimization for Serverless Systems. IEEE BigData Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran
2020	A*	`conf`	PowerNorm: Rethinking Batch Normalization in Transformers. ICML Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2020	A*	`conf`	Precise expressions for random projections: Low-rank approximation and randomized Newton. NeurIPS Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
2020	J	`jnl`	Precise expressions for random projections: Low-rank approximation and randomized Newton. CoRR Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
2020	J	`jnl`	Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data. CoRR Charles H. Martin, Tongsu Peng, Michael W. Mahoney
2020	—	`conf`	PyHessian: Neural Networks Through the Lens of the Hessian. IEEE BigData Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2020	A*	`conf`	Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. AAAI Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2020	J	`jnl`	Rethinking Batch Normalization in Transformers. CoRR Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2020	A	`conf`	Second-order Optimization for Non-convex Machine Learning: an Empirical Study. SDM Peng Xu, Fred Roosta, Michael W. Mahoney
2020	J	`jnl`	Sparse Quantized Spectral Clustering. CoRR Zhenyu Liao, Romain Couillet, Michael W. Mahoney
2020	J	`jnl`	Sparse sketches with small inversion bias. CoRR Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
2020	A	`conf`	Statistical guarantees for local graph clustering. AISTATS Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney
2020	J	`jnl`	Stochastic Normalizing Flows. CoRR Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
2020	A*	`conf`	ZeroQ: A Novel Zero Shot Quantization Framework. CVPR Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2020	J	`jnl`	ZeroQ: A Novel Zero Shot Quantization Framework. CoRR Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2019	J	`jnl`	A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. J. Mach. Learn. Res. Miles E. Lopes, Shusen Wang, Michael W. Mahoney
2019	J	`jnl`	ANODEV2: A Coupled Neural ODE Evolution Framework. CoRR Tianjun Zhang, Zhewei Yao, Amir Gholami, Kurt Keutzer, Joseph Gonzalez, George Biros, Michael W. Mahoney
2019	A*	`conf`	ANODEV2: A Coupled Neural ODE Framework. NeurIPS Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros
2019	J	`jnl`	Alchemist: An Apache Spark ⇔ MPI interface. Concurr. Comput. Pract. Exp. Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff
2019	J	`jnl`	Avoiding Communication in Primal and Dual Block Coordinate Descent Methods. SIAM J. Sci. Comput. Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
2019	J	`jnl`	Bayesian experimental design using regularized determinantal point processes. CoRR Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2019	J	`jnl`	Block Basis Factorization for Scalable Kernel Evaluation. SIAM J. Matrix Anal. Appl. Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve
2019	J	`jnl`	Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching. CoRR Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
2019	A*	`conf`	Distributed estimation of the inverse Hessian by determinantal averaging. NeurIPS Michal Derezinski, Michael W. Mahoney
2019	J	`jnl`	Distributed estimation of the inverse Hessian by determinantal averaging. CoRR Michal Derezinski, Michael W. Mahoney
2019	J	`jnl`	Exact expressions for double descent and implicit regularization via surrogate random design. CoRR Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2019	A	`conf`	GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. SDM Sudhir B. Kylasa, Fred (Farbod) Roosta, Michael W. Mahoney, Ananth Grama
2019	J	`jnl`	Group Collaborative Representation for Image Set Classification. Int. J. Comput. Vis. Bo Liu, Liping Jing, Jia Li, Jian Yu, Alex Gittens, Michael W. Mahoney
2019	J	`jnl`	HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. CoRR Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2019	A*	`conf`	HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. ICCV Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2019	J	`jnl`	HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision. CoRR Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2019	J	`jnl`	Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. CoRR Charles H. Martin, Michael W. Mahoney
2019	J	`jnl`	Inefficiency of K-FAC for Large Batch Size Training. CoRR Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2019	J	`jnl`	JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. CoRR N. Benjamin Erichson, Zhewei Yao, Michael W. Mahoney
2019	J	`jnl`	Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. CoRR Keith D. Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
2019	A*	`conf`	Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. COLT Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth
2019	J	`jnl`	Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. CoRR Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth
2019	J	`jnl`	On Linear Convergence of Weighted Kernel Herding. CoRR Rajiv Khanna, Michael W. Mahoney
2019	J	`jnl`	OverSketched Newton: Fast Convex Optimization for Serverless Systems. CoRR Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran
2019	J	`jnl`	Parallel and Communication Avoiding Least Angle Regression. CoRR Swapnil Das, Jim Demmel, Kimon Fountoulakis, Laura Grigori, Michael W. Mahoney
2019	J	`jnl`	Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction. CoRR N. Benjamin Erichson, Michael Muehlebach, Michael W. Mahoney
2019	J	`jnl`	PyHessian: Neural Networks Through the Lens of the Hessian. CoRR Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2019	J	`jnl`	Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. CoRR Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
2019	J	`jnl`	Residual Networks as Nonlinear Systems: Stability Analysis using Linearization. CoRR Kai Rothauge, Zhewei Yao, Zixi Hu, Michael W. Mahoney
2019	J	`jnl`	Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times. CoRR Kai Rothauge, Haripriya Ayyalasomayajula, Kristyn J. Maschhoff, Michael F. Ringenburg, Michael W. Mahoney
2019	J	`jnl`	Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds. J. Mach. Learn. Res. Shusen Wang, Alex Gittens, Michael W. Mahoney
2019	J	`jnl`	Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. CoRR N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz
2019	A*	`conf`	Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. KDD Charles H. Martin, Michael W. Mahoney
2019	J	`jnl`	Statistical guarantees for local graph clustering. CoRR Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney
2019	J	`jnl`	Sub-sampled Newton methods. Math. Program. Farbod Roosta-Khorasani, Michael W. Mahoney
2019	J	`jnl`	The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science. CoRR Michael W. Mahoney
2019	J	`jnl`	The Difficulties of Addressing Interdisciplinary Challenges at theFoundations of Data Science. SIGACT News Michael W. Mahoney
2019	A*	`conf`	Traditional and Heavy Tailed Self Regularization in Neural Network Models. ICML Michael W. Mahoney, Charles H. Martin
2019	J	`jnl`	Traditional and Heavy-Tailed Self Regularization in Neural Network Models. CoRR Charles H. Martin, Michael W. Mahoney
2019	A*	`conf`	Trust Region Based Adversarial Attack on Neural Networks. CVPR Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael W. Mahoney
2019	J	`jnl`	Variational perspective on local graph clustering. Math. Program. Kimon Fountoulakis, Farbod Roosta-Khorasani, Julian Shun, Xiang Cheng, Michael W. Mahoney
2018	J	`jnl`	A Short Introduction to Local Graph Clustering Methods and Software. CoRR Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney
2018	A*	`conf`	Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. KDD Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff
2018	J	`jnl`	Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. CoRR Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff
2018	J	`jnl`	Alchemist: An Apache Spark MPI Interface. CoRR Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff
2018	A	`conf`	Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization. IPDPS Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
2018	J	`jnl`	Distributed Second-order Convex Optimization. CoRR Chih-Hao Fang, Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama
2018	A*	`conf`	Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. ICML Miles E. Lopes, Shusen Wang, Michael W. Mahoney
2018	J	`jnl`	Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. CoRR Miles E. Lopes, Shusen Wang, Michael W. Mahoney
2018	A	`conf`	FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. AISTATS Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney
2018	A*	`conf`	GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. NeurIPS Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney
2018	J	`jnl`	GPU Accelerated Sub-Sampled Newton\textsf{'}s Method. CoRR Sudhir B. Kylasa, Farbod Roosta-Khorasani, Michael W. Mahoney, Ananth Grama
2018	A*	`conf`	Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. NeurIPS Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney
2018	J	`jnl`	Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. CoRR Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney
2018	J	`jnl`	Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. CoRR Charles H. Martin, Michael W. Mahoney
2018	B	`conf`	LASAGNE: Locality and Structure Aware Graph Node Embedding. WI Evgheniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney
2018	J	`jnl`	Large batch size training of neural networks with adversarial training and second-order information. CoRR Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2018	J	`jnl`	Newton-MR: Newton's Method Without Smoothness or Convexity. CoRR Fred (Farbod) Roosta, Yang Liu, Peng Xu, Michael W. Mahoney
2018	J	`jnl`	On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent. CoRR Noah Golmant, Nikita Vemuri, Zhewei Yao, Vladimir Feinberg, Amir Gholami, Kai Rothauge, Michael W. Mahoney, Joseph Gonzalez
2018	A*	`conf`	Out-of-sample extension of graph adjacency spectral embedding. ICML Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe
2018	J	`jnl`	Parameter Re-Initialization through Cyclical Batch Size Schedules. CoRR Norman Mu, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael W. Mahoney
2018	J	`jnl`	Trust Region Based Adversarial Attack on Neural Networks. CoRR Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael W. Mahoney
2017	J	`jnl`	A Berkeley View of Systems Challenges for AI. CoRR 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
2017	J	`jnl`	A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. CoRR Miles E. Lopes, Shusen Wang, Michael W. Mahoney
2017	J	`jnl`	A local perspective on community structure in multilayer networks. Netw. Sci. Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter
2017	J	`jnl`	An Optimization Approach to Locally-Biased Graph Algorithms. Proc. IEEE Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney
2017	J	`jnl`	Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization. CoRR Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
2017	A*	`conf`	Capacity Releasing Diffusion for Speed and Locality. ICML Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao
2017	J	`jnl`	Capacity Releasing Diffusion for Speed and Locality. CoRR Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao
2017	J	`jnl`	DCAR: A Discriminative and Compact Audio Representation for Audio Processing. IEEE Trans. Multim. Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland
2017	J	`jnl`	GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. CoRR Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney
2017	J	`jnl`	LASAGNE: Locality And Structure Aware Graph Node Embedding. CoRR Evgheniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney
2017	J	`jnl`	Lectures on Randomized Numerical Linear Algebra. CoRR Petros Drineas, Michael W. Mahoney
2017	J	`jnl`	Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information. CoRR Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
2017	J	`jnl`	Principles and Applications of Science of Information. Proc. IEEE Thomas A. Courtade, Ananth Grama, Michael W. Mahoney, Tsachy Weissman
2017	J	`jnl`	Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior. CoRR Charles H. Martin, Michael W. Mahoney
2017	J	`jnl`	Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds. CoRR Shusen Wang, Alex Gittens, Michael W. Mahoney
2017	J	`jnl`	Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study. CoRR Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
2017	A*	`conf`	Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. ICML Shusen Wang, Alex Gittens, Michael W. Mahoney
2017	J	`jnl`	Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. CoRR Shusen Wang, Alex Gittens, Michael W. Mahoney
2017	J	`jnl`	Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. J. Mach. Learn. Res. Shusen Wang, Alex Gittens, Michael W. Mahoney
2017	—	`conf`	Skip-Gram - Zipf + Uniform = Vector Additivity. ACL (1) Alex Gittens, Dimitris Achlioptas, Michael W. Mahoney
2017	J	`jnl`	Social Discrete Choice Models. CoRR Danqing Zhang, Kimon Fountoulakis, Junyu Cao, Mogeng Yin, Michael W. Mahoney, Alexei Pozdnoukhov
2017	—	`conf`	Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS 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
2017	J	`jnl`	Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning. J. Mach. Learn. Res. Jiyan Yang, Yin-Lam Chow, Christopher Ré, Michael W. Mahoney
2016	A*	`conf`	A Discriminative and Compact Audio Representation for Event Detection. ACM Multimedia Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland
2016	—	`conf`	A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. IPDPS Workshops 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
2016	A*	`conf`	A Simple and Strongly-Local Flow-Based Method for Cut Improvement. ICML Nate Veldt, David F. Gleich, Michael W. Mahoney
2016	J	`jnl`	A Simple and Strongly-Local Flow-Based Method for Cut Improvement. CoRR Nate Veldt, David F. Gleich, Michael W. Mahoney
2016	J	`jnl`	A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. J. Mach. Learn. Res. Garvesh Raskutti, Michael W. Mahoney
2016	J	`jnl`	An optimization approach to locally-biased graph algorithms. CoRR Kimon Fountoulakis, David F. Gleich, Michael W. Mahoney
2016	A*	`conf`	Approximating the Solution to Mixed Packing and Covering LPs in Parallel O˜(epsilon^{-3}) Time. ICALP Michael W. Mahoney, Satish Rao, Di Wang, Peng Zhang
2016	J	`jnl`	Avoiding communication in primal and dual block coordinate descent methods. CoRR Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
2016	J	`jnl`	DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection. CoRR Liping Jing, Bo Liu, Jaeyoung Choi, Adam Janin, Julia Bernd, Michael W. Mahoney, Gerald Friedland
2016	J	`jnl`	FLAG: Fast Linearly-Coupled Adaptive Gradient Method. CoRR Xiang Cheng, Farbod Roosta-Khorasani, Peter L. Bartlett, Michael W. Mahoney
2016	—	`conf`	Feature-distributed sparse regression: a screen-and-clean approach. NIPS Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun
2016	J	`jnl`	Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. Proc. IEEE Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
2016	J	`jnl`	Lecture Notes on Randomized Linear Algebra. CoRR Michael W. Mahoney
2016	J	`jnl`	Lecture Notes on Spectral Graph Methods. CoRR Michael W. Mahoney
2016	J	`jnl`	Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data. CoRR David Lawlor, Tamás Budavári, Michael W. Mahoney
2016	J	`jnl`	Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. CoRR 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
2016	—	`conf`	Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. IEEE BigData 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
2016	—	`ch.`	Mining Large Graphs. Handbook of Big Data David F. Gleich, Michael W. Mahoney
2016	J	`jnl`	Parallel Local Graph Clustering. CoRR Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney
2016	J	`jnl`	Parallel Local Graph Clustering. Proc. VLDB Endow. Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, Michael W. Mahoney
2016	J	`jnl`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney
2016	J	`jnl`	RandNLA: randomized numerical linear algebra. Commun. ACM Petros Drineas, Michael W. Mahoney
2016	J	`jnl`	Recent Advances in Randomized Numerical Linear Algebra (NII Shonan Meeting 2016-10). NII Shonan Meet. Rep. Ravindran Kannan, Michael W. Mahoney, David P. Woodruff
2016	J	`jnl`	Revisiting the Nystrom Method for Improved Large-scale Machine Learning. J. Mach. Learn. Res. Alex Gittens, Michael W. Mahoney
2016	—	`ch.`	Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms. Handbook of Big Data Michael W. Mahoney, Petros Drineas
2016	J	`jnl`	Sub-Sampled Newton Methods I: Globally Convergent Algorithms. CoRR Farbod Roosta-Khorasani, Michael W. Mahoney
2016	J	`jnl`	Sub-Sampled Newton Methods II: Local Convergence Rates. CoRR Farbod Roosta-Khorasani, Michael W. Mahoney
2016	—	`conf`	Sub-sampled Newton Methods with Non-uniform Sampling. NIPS Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney
2016	J	`jnl`	The Fast Cauchy Transform and Faster Robust Linear Regression. SIAM J. Comput. Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff
2016	J	`jnl`	Tree decompositions and social graphs. Internet Math. Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney
2016	A*	`conf`	Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. ICALP Di Wang, Satish Rao, Michael W. Mahoney
2016	A*	`conf`	Weighted SGD for SODA Jiyan Yang, Yinlam Chow, Christopher Ré, Michael W. Mahoney
2015	J	`jnl`	A Local Perspective on Community Structure in Multilayer Networks. CoRR Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter
2015	J	`jnl`	A statistical perspective on algorithmic leveraging. J. Mach. Learn. Res. Ping Ma, Michael W. Mahoney, Bin Yu
2015	—	`conf`	Fast Randomized Kernel Ridge Regression with Statistical Guarantees. NIPS Ahmed El Alaoui, Michael W. Mahoney
2015	J	`jnl`	Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent. CoRR Di Wang, Michael W. Mahoney, Nishanth Mohan, Satish Rao
2015	J	`jnl`	Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. CoRR Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
2015	J	`jnl`	Randomized Dimensionality Reduction for k-Means Clustering. IEEE Trans. Inf. Theory Christos Boutsidis, Anastasios Zouzias, Michael W. Mahoney, Petros Drineas
2015	A	`conf`	Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method. AISTATS David G. Anderson, Simon S. Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu
2015	A*	`conf`	Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. ICML Garvesh Raskutti, Michael W. Mahoney
2015	J	`jnl`	Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation. CoRR Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve
2015	J	`jnl`	Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. CoRR Di Wang, Satish Rao, Michael W. Mahoney
2015	A*	`conf`	Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. KDD David F. Gleich, Michael W. Mahoney
2014	A*	`conf`	A Statistical Perspective on Algorithmic Leveraging. ICML Ping Ma, Michael W. Mahoney, Bin Yu
2014	J	`jnl`	A new spin on an old algorithm: technical perspective. Commun. ACM Michael W. Mahoney
2014	A*	`conf`	Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow. ICML David F. Gleich, Michael W. Mahoney
2014	J	`jnl`	Fast Randomized Kernel Methods With Statistical Guarantees. CoRR Ahmed El Alaoui, Michael W. Mahoney
2014	J	`jnl`	LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems. SIAM J. Sci. Comput. Xiangrui Meng, Michael A. Saunders, Michael W. Mahoney
2014	J	`jnl`	Quantile Regression for Large-Scale Applications. SIAM J. Sci. Comput. Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
2014	A*	`conf`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
2014	J	`jnl`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
2014	A*	`conf`	Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney
2014	J	`jnl`	Semi-supervised eigenvectors for large-scale locally-biased learning. J. Mach. Learn. Res. Toke Jansen Hansen, Michael W. Mahoney
2014	J	`jnl`	Signal Processing for Big Data [From the Guest Editors]. IEEE Signal Process. Mag. Georgios B. Giannakis, Francis R. Bach, Raphael Cendrillon, Michael W. Mahoney, Jennifer Neville
2014	J	`jnl`	Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks. CoRR Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney
2014	J	`jnl`	Tree decompositions and social graphs. CoRR Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney
2013	J	`jnl`	A Statistical Perspective on Algorithmic Leveraging. CoRR Ping Ma, Michael W. Mahoney, Bin Yu
2013	—	`conf`	Evaluating OpenMP Tasking at Scale for the Computation of Graph Hyperbolicity. IWOMP Aaron B. Adcock, Blair D. Sullivan, Oscar R. Hernandez, Michael W. Mahoney
2013	A*	`conf`	Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression. STOC Xiangrui Meng, Michael W. Mahoney
2013	J	`jnl`	On the Hyperbolicity of Small-World and Treelike Random Graphs. Internet Math. Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney
2013	J	`jnl`	Quantile Regression for Large-scale Applications CoRR Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
2013	—	`conf`	Quantile Regression for Large-scale Applications. ICML (3) Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
2013	J	`jnl`	Revisiting the Nystrom Method for Improved Large-Scale Machine Learning CoRR Alex Gittens, Michael W. Mahoney
2013	—	`conf`	Revisiting the Nystrom method for improved large-scale machine learning. ICML (3) Alex Gittens, Michael W. Mahoney
2013	—	`conf`	Robust Regression on MapReduce. ICML (3) Xiangrui Meng, Michael W. Mahoney
2013	J	`jnl`	Semi-supervised Eigenvectors for Large-scale Locally-biased Learning CoRR Toke Jansen Hansen, Michael W. Mahoney
2013	A*	`conf`	The Fast Cauchy Transform and Faster Robust Linear Regression. SODA Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff
2013	A*	`conf`	Tree-Like Structure in Large Social and Information Networks. ICDM Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney
2012	J	`jnl`	A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally. J. Mach. Learn. Res. Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi
2012	J	`jnl`	Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis CoRR Michael W. Mahoney
2012	A*	`conf`	Approximate computation and implicit regularization for very large-scale data analysis. PODS Michael W. Mahoney
2012	J	`jnl`	Approximating Higher-Order Distances Using Random Projections CoRR Ping Li, Michael W. Mahoney, Yiyuan She
2012	A*	`conf`	Fast approximation of matrix coherence and statistical leverage. ICML Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff
2012	J	`jnl`	Fast approximation of matrix coherence and statistical leverage. J. Mach. Learn. Res. Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff
2012	J	`jnl`	Low-distortion Subspace Embeddings in Input-sparsity Time and Applications to Robust Linear Regression CoRR Xiangrui Meng, Michael W. Mahoney
2012	J	`jnl`	On the Hyperbolicity of Small-World Networks and Tree-Like Graphs CoRR Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney
2012	B	`conf`	On the Hyperbolicity of Small-World and Tree-Like Random Graphs. ISAAC Wei Chen, Wenjie Fang, Guangda Hu, Michael W. Mahoney
2012	—	`conf`	Semi-supervised Eigenvectors for Locally-biased Learning. NIPS Toke Jansen Hansen, Michael W. Mahoney
2012	J	`jnl`	The Fast Cauchy Transform: with Applications to Basis Construction, Regression, and Subspace Approximation in L1 CoRR Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff
2012	J	`jnl`	rCUR: an R package for CUR matrix decomposition. BMC Bioinform. András Bodor, István Csabai, Michael W. Mahoney, Norbert Solymosi
2011	J	`jnl`	Fast approximation of matrix coherence and statistical leverage CoRR Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff
2011	J	`jnl`	Faster least squares approximation. Numerische Mathematik Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós
2011	A*	`conf`	Implementing regularization implicitly via approximate eigenvector computation. ICML Michael W. Mahoney, Lorenzo Orecchia
2011	J	`jnl`	LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems CoRR Xiangrui Meng, Michael A. Saunders, Michael W. Mahoney
2011	J	`jnl`	Localization on low-order eigenvectors of data matrices CoRR Mihai Cucuringu, Michael W. Mahoney
2011	J	`jnl`	Randomized Algorithms for Matrices and Data. Found. Trends Mach. Learn. Michael W. Mahoney
2011	J	`jnl`	Randomized algorithms for matrices and data CoRR Michael W. Mahoney
2011	J	`jnl`	Regularized Laplacian Estimation and Fast Eigenvector Approximation CoRR Patrick O. Perry, Michael W. Mahoney
2011	—	`conf`	Regularized Laplacian Estimation and Fast Eigenvector Approximation. NIPS Patrick O. Perry, Michael W. Mahoney
2011	J	`jnl`	Stochastic Dimensionality Reduction for K-means Clustering CoRR Christos Boutsidis, Anastasios Zouzias, Michael W. Mahoney, Petros Drineas
2010	J	`jnl`	Algorithmic and Statistical Perspectives on Large-Scale Data Analysis CoRR Michael W. Mahoney
2010	A	`conf`	Approximating Higher-Order Distances Using Random Projections. UAI Ping Li, Michael W. Mahoney, Yiyuan She
2010	J	`jnl`	CUR from a Sparse Optimization Viewpoint CoRR Jacob Bien, Ya Xu, Michael W. Mahoney
2010	—	`conf`	CUR from a Sparse Optimization Viewpoint. NIPS Jacob Bien, Ya Xu, Michael W. Mahoney
2010	J	`jnl`	Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010 CoRR Michael W. Mahoney
2010	J	`jnl`	Computation in large-scale scientific and internet data applications is a focus of MMDS 2010. SIGKDD Explor. Michael W. Mahoney
2010	J	`jnl`	Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving CoRR Petros Drineas, Michael W. Mahoney
2010	J	`jnl`	Empirical Comparison of Algorithms for Network Community Detection CoRR Jure Leskovec, Kevin J. Lang, Michael W. Mahoney
2010	A*	`conf`	Empirical comparison of algorithms for network community detection. WWW Jure Leskovec, Kevin J. Lang, Michael W. Mahoney
2010	J	`jnl`	Implementing regularization implicitly via approximate eigenvector computation CoRR Michael W. Mahoney, Lorenzo Orecchia
2010	J	`jnl`	SIGACT news algorithms column: computation in large-scale scientific and internet data applications is a focus of MMDS 2010. SIGACT News Samir Khuller, Michael W. Mahoney
2009	J	`jnl`	A Spectral Algorithm for Improving Graph Partitions CoRR Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi
2009	A*	`conf`	An improved approximation algorithm for the column subset selection problem. SODA Christos Boutsidis, Michael W. Mahoney, Petros Drineas
2009	J	`jnl`	CUR matrix decompositions for improved data analysis. Proc. Natl. Acad. Sci. USA Michael W. Mahoney, Petros Drineas
2009	J	`jnl`	Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Math. Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney
2009	B	`conf`	Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow. SEA Kevin J. Lang, Michael W. Mahoney, Lorenzo Orecchia
2009	J	`jnl`	Learning with Spectral Kernels and Heavy-Tailed Data CoRR Michael W. Mahoney, Hariharan Narayanan
2009	J	`jnl`	Sampling Algorithms and Coresets for $\ell SIAM J. Comput. Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney
2009	—	`conf`	Unsupervised Feature Selection for the $k$-means Clustering Problem. NIPS Christos Boutsidis, Michael W. Mahoney, Petros Drineas
2008	J	`jnl`	Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008 CoRR Michael W. Mahoney, Lek-Heng Lim, Gunnar E. Carlsson
2008	J	`jnl`	Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008. SIGKDD Explor. Michael W. Mahoney, Lek-Heng Lim, Gunnar E. Carlsson
2008	J	`jnl`	An Improved Approximation Algorithm for the Column Subset Selection Problem CoRR Christos Boutsidis, Michael W. Mahoney, Petros Drineas
2008	J	`jnl`	Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters CoRR Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney
2008	J	`jnl`	Relative-Error CUR Matrix Decompositions. SIAM J. Matrix Anal. Appl. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
2008	A*	`conf`	Sampling algorithms and coresets for ℓ SODA Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney
2008	J	`jnl`	Sampling subproblems of heterogeneous Max-Cut problems and approximation algorithms. Random Struct. Algorithms Petros Drineas, Ravi Kannan, Michael W. Mahoney
2008	A*	`conf`	Statistical properties of community structure in large social and information networks. WWW Jure Leskovec, Kevin J. Lang, Anirban Dasgupta, Michael W. Mahoney
2008	J	`jnl`	Tensor-CUR Decompositions for Tensor-Based Data. SIAM J. Matrix Anal. Appl. Michael W. Mahoney, Mauro Maggioni, Petros Drineas
2008	A*	`conf`	Unsupervised feature selection for principal components analysis. KDD Christos Boutsidis, Michael W. Mahoney, Petros Drineas
2007	—	`conf`	07071 Abstracts Collection -- Web Information Retrieval and Linear Algebra Algorithms. Web Information Retrieval and Linear Algebra Algorithms Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld
2007	—	`conf`	07071 Report on Dagstuhl Seminar -- Web Information Retrieval and Linear Algebra Algorithms. Web Information Retrieval and Linear Algebra Algorithms Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld
2007	J	`jnl`	Faster Least Squares Approximation CoRR Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós
2007	A*	`conf`	Feature selection methods for text classification. KDD Anirban Dasgupta, Petros Drineas, Boulos Harb, Vanja Josifovski, Michael W. Mahoney
2007	J	`jnl`	Relative-Error CUR Matrix Decompositions CoRR Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
2007	J	`jnl`	Sampling Algorithms and Coresets for Lp Regression CoRR Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney
2007	—	`ed.`	Web Information Retrieval and Linear Algebra Algorithms, 11.02. - 16.02.2007 Web Information Retrieval and Linear Algebra Algorithms Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld
2006	J	`jnl`	Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication. SIAM J. Comput. Petros Drineas, Ravi Kannan, Michael W. Mahoney
2006	J	`jnl`	Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix. SIAM J. Comput. Petros Drineas, Ravi Kannan, Michael W. Mahoney
2006	J	`jnl`	Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition. SIAM J. Comput. Petros Drineas, Ravi Kannan, Michael W. Mahoney
2006	A*	`conf`	Randomized Algorithms for Matrices and Massive Data Sets. VLDB Petros Drineas, Michael W. Mahoney
2006	A*	`conf`	Sampling algorithms for SODA Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
2006	—	`conf`	Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods. APPROX-RANDOM Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
2006	A	`conf`	Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods. ESA Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
2006	A*	`conf`	Tensor-CUR decompositions for tensor-based data. KDD Michael W. Mahoney, Mauro Maggioni, Petros Drineas
2005	A*	`conf`	Approximating a Gram Matrix for Improved Kernel-Based Learning. COLT Petros Drineas, Michael W. Mahoney
2005	J	`jnl`	On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. J. Mach. Learn. Res. Petros Drineas, Michael W. Mahoney
2005	A	`conf`	Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms. STACS Petros Drineas, Ravi Kannan, Michael W. Mahoney
2003	B	`conf`	Rapid Mixing of Several Markov Chains for a Hard-Core Model. ISAAC Ravi Kannan, Michael W. Mahoney, Ravi Montenegro