Rapid Mixing of Several Markov Chains for a Hard-Core Model. - Ravi Kannan; Michael W. Mahoney; Ravi Montenegro (ISAAC, 2003)

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

Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms. - Petros Drineas; Ravi Kannan; Michael W. Mahoney (STACS, 2005)

On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. - Petros Drineas; Michael W. Mahoney (J. Mach. Learn. Res., 2005)

Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods. - Petros Drineas; Michael W. Mahoney; S. Muthukrishnan 001 (APPROX-RANDOM, 2006)

Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods. - Petros Drineas; Michael W. Mahoney; S. Muthukrishnan 001 (ESA, 2006)

Sampling algorithms for l2 regression and applications. - Petros Drineas; Michael W. Mahoney; S. Muthukrishnan 001 (SODA, 2006)

Randomized Algorithms for Matrices and Massive Data Sets. - Petros Drineas; Michael W. Mahoney (VLDB, 2006)

Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication. - Petros Drineas; Ravi Kannan; Michael W. Mahoney (SIAM J. Comput., 2006)

Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix. - Petros Drineas; Ravi Kannan; Michael W. Mahoney (SIAM J. Comput., 2006)

Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition. - Petros Drineas; Ravi Kannan; Michael W. Mahoney (SIAM J. Comput., 2006)

07071 Report on Dagstuhl Seminar – Web Information Retrieval and Linear Algebra Algorithms. - Andreas Frommer; Michael W. Mahoney; Daniel B. Szyld (Web Information Retrieval and Linear Algebra Algorithms, 2007)

07071 Abstracts Collection – Web Information Retrieval and Linear Algebra Algorithms. - Andreas Frommer; Michael W. Mahoney; Daniel B. Szyld (Web Information Retrieval and Linear Algebra Algorithms, 2007)

Feature selection methods for text classification. - Anirban Dasgupta 001; Petros Drineas; Boulos Harb; Vanja Josifovski; Michael W. Mahoney (KDD, 2007)

Sampling Algorithms and Coresets for Lp Regression - Anirban Dasgupta 001; Petros Drineas; Boulos Harb; Ravi Kumar 001; Michael W. Mahoney (CoRR, 2007)

Unsupervised feature selection for principal components analysis. - Christos Boutsidis; Michael W. Mahoney; Petros Drineas (KDD, 2008)

Sampling algorithms and coresets for ℓp regression. - Anirban Dasgupta 001; Petros Drineas; Boulos Harb; Ravi Kumar 001; Michael W. Mahoney (SODA, 2008)

Statistical properties of community structure in large social and information networks. - Jure Leskovec; Kevin J. Lang; Anirban Dasgupta 001; Michael W. Mahoney (WWW, 2008)

Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008 - Michael W. Mahoney; Lek-Heng Lim; Gunnar E. Carlsson (CoRR, 2008)

Sampling subproblems of heterogeneous Max-Cut problems and approximation algorithms. - Petros Drineas; Ravi Kannan; Michael W. Mahoney (Random Struct. Algorithms, 2008)

Relative-Error CUR Matrix Decompositions. - Petros Drineas; Michael W. Mahoney; S. Muthukrishnan 001 (SIAM J. Matrix Anal. Appl., 2008)

Tensor-CUR Decompositions for Tensor-Based Data. - Michael W. Mahoney; Mauro Maggioni; Petros Drineas (SIAM J. Matrix Anal. Appl., 2008)

Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008. - Michael W. Mahoney; Lek-Heng Lim; Gunnar E. Carlsson (SIGKDD Explor., 2008)

Unsupervised Feature Selection for the $k$-means Clustering Problem. - Christos Boutsidis; Michael W. Mahoney; Petros Drineas (NIPS, 2009)

An improved approximation algorithm for the column subset selection problem. - Christos Boutsidis; Michael W. Mahoney; Petros Drineas (SODA, 2009)

Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow. - Kevin J. Lang; Michael W. Mahoney; Lorenzo Orecchia (SEA, 2009)

Learning with Spectral Kernels and Heavy-Tailed Data - Michael W. Mahoney; Hariharan Narayanan (CoRR, 2009)

A Spectral Algorithm for Improving Graph Partitions - Michael W. Mahoney; Lorenzo Orecchia; Nisheeth K. Vishnoi (CoRR, 2009)

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. - Jure Leskovec; Kevin J. Lang; Anirban Dasgupta 001; Michael W. Mahoney (Internet Math., 2009)

CUR matrix decompositions for improved data analysis. - Michael W. Mahoney; Petros Drineas (Proc. Natl. Acad. Sci. USA, 2009)

Sampling Algorithms and Coresets for $ ellp Regression. - Anirban Dasgupta 001; Petros Drineas; Boulos Harb; Ravi Kumar 001; Michael W. Mahoney (SIAM J. Comput., 2009)

CUR from a Sparse Optimization Viewpoint. - Jacob Bien; Ya Xu; Michael W. Mahoney (NIPS, 2010)

Approximating Higher-Order Distances Using Random Projections. - Ping Li 001; Michael W. Mahoney; Yiyuan She (UAI, 2010)

Empirical comparison of algorithms for network community detection. - Jure Leskovec; Kevin J. Lang; Michael W. Mahoney (WWW, 2010)

Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving - Petros Drineas; Michael W. Mahoney (CoRR, 2010)

Algorithmic and Statistical Perspectives on Large-Scale Data Analysis - Michael W. Mahoney (CoRR, 2010)

SIGACT news algorithms column: computation in large-scale scientific and internet data applications is a focus of MMDS 2010. - Samir Khuller; Michael W. Mahoney (SIGACT News, 2010)

Computation in large-scale scientific and internet data applications is a focus of MMDS 2010. - Michael W. Mahoney (SIGKDD Explor., 2010)

Implementing regularization implicitly via approximate eigenvector computation. - Michael W. Mahoney; Lorenzo Orecchia (ICML, 2011)

Regularized Laplacian Estimation and Fast Eigenvector Approximation. - Patrick O. Perry; Michael W. Mahoney (NIPS, 2011)

Localization on low-order eigenvectors of data matrices - Mihai Cucuringu; Michael W. Mahoney (CoRR, 2011)

LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems - Xiangrui Meng; Michael A. Saunders; Michael W. Mahoney (CoRR, 2011)

Stochastic Dimensionality Reduction for K-means Clustering - Christos Boutsidis; Anastasios Zouzias; Michael W. Mahoney; Petros Drineas (CoRR, 2011)

Randomized Algorithms for Matrices and Data. - Michael W. Mahoney (Found. Trends Mach. Learn., 2011)

Faster least squares approximation. - Petros Drineas; Michael W. Mahoney; S. Muthukrishnan 001; Tamás Sarlós (Numerische Mathematik, 2011)

On the Hyperbolicity of Small-World and Tree-Like Random Graphs. - Wei Chen 013; Wenjie Fang; Guangda Hu; Michael W. Mahoney (ISAAC, 2012)

Semi-supervised Eigenvectors for Locally-biased Learning. - Toke Jansen Hansen; Michael W. Mahoney (NIPS, 2012)

Approximate computation and implicit regularization for very large-scale data analysis. - Michael W. Mahoney (PODS, 2012)

rCUR: an R package for CUR matrix decomposition. - András Bodor; István Csabai; Michael W. Mahoney; Norbert Solymosi (BMC Bioinform., 2012)

On the Hyperbolicity of Small-World Networks and Tree-Like Graphs - Wei Chen 013; Wenjie Fang; Guangda Hu; Michael W. Mahoney (CoRR, 2012)

The Fast Cauchy Transform: with Applications to Basis Construction, Regression, and Subspace Approximation in L1 - Kenneth L. Clarkson; Petros Drineas; Malik Magdon-Ismail; Michael W. Mahoney; Xiangrui Meng; David P. Woodruff (CoRR, 2012)

Fast approximation of matrix coherence and statistical leverage. - Petros Drineas; Malik Magdon-Ismail; Michael W. Mahoney; David P. Woodruff (J. Mach. Learn. Res., 2012)

A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally. - Michael W. Mahoney; Lorenzo Orecchia; Nisheeth K. Vishnoi (J. Mach. Learn. Res., 2012)

Tree-Like Structure in Large Social and Information Networks. - Aaron B. Adcock; Blair D. Sullivan; Michael W. Mahoney (ICDM, 2013)

Robust Regression on MapReduce. - Xiangrui Meng; Michael W. Mahoney (ICML (3), 2013)

Evaluating OpenMP Tasking at Scale for the Computation of Graph Hyperbolicity. - Aaron B. Adcock; Blair D. Sullivan; Oscar R. Hernandez; Michael W. Mahoney (IWOMP, 2013)

Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression. - Xiangrui Meng; Michael W. Mahoney (STOC, 2013)

On the Hyperbolicity of Small-World and Treelike Random Graphs. - Wei Chen 013; Wenjie Fang; Guangda Hu; Michael W. Mahoney (Internet Math., 2013)

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

Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow. - David F. Gleich; Michael W. Mahoney (ICML, 2014)

A new spin on an old algorithm: technical perspective. - Michael W. Mahoney (Commun. ACM, 2014)

Fast Randomized Kernel Methods With Statistical Guarantees. - Ahmed El Alaoui; Michael W. Mahoney (CoRR, 2014)

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks. - Lucas G. S. Jeub; Prakash Balachandran; Mason A. Porter; Peter J. Mucha; Michael W. Mahoney (CoRR, 2014)

Semi-supervised eigenvectors for large-scale locally-biased learning. - Toke Jansen Hansen; Michael W. Mahoney (J. Mach. Learn. Res., 2014)

LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems. - Xiangrui Meng; Michael A. Saunders; Michael W. Mahoney (SIAM J. Sci. Comput., 2014)

Quantile Regression for Large-Scale Applications. - Jiyan Yang; Xiangrui Meng; Michael W. Mahoney (SIAM J. Sci. Comput., 2014)

Signal Processing for Big Data [From the Guest Editors]. - Georgios B. Giannakis; Francis R. Bach; Raphael Cendrillon; Michael W. Mahoney; Jennifer Neville (IEEE Signal Process. Mag., 2014)

Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method. - David G. Anderson; Simon S. Du; Michael W. Mahoney; Christopher Melgaard; Kunming Wu; Ming Gu 002 (AISTATS, 2015)

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares. - Garvesh Raskutti; Michael W. Mahoney (ICML, 2015)

Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms. - David F. Gleich; Michael W. Mahoney (KDD, 2015)

Fast Randomized Kernel Ridge Regression with Statistical Guarantees. - Ahmed El Alaoui; Michael W. Mahoney (NIPS, 2015)

Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation. - Ruoxi Wang; Yingzhou Li; Michael W. Mahoney; Eric Darve (CoRR, 2015)

Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent. - Di Wang 005; Michael W. Mahoney; Nishanth Mohan; Satish Rao (CoRR, 2015)

A statistical perspective on algorithmic leveraging. - Ping Ma 001; Michael W. Mahoney; Bin Yu 001 (J. Mach. Learn. Res., 2015)

Randomized Dimensionality Reduction for k-Means Clustering. - Christos Boutsidis; Anastasios Zouzias; Michael W. Mahoney; Petros Drineas (IEEE Trans. Inf. Theory, 2015)

Mining Large Graphs. - David F. Gleich; Michael W. Mahoney (Handbook of Big Data, 2016)

Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms. - Michael W. Mahoney; Petros Drineas (Handbook of Big Data, 2016)

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (IEEE BigData, 2016)

Approximating the Solution to Mixed Packing and Covering LPs in Parallel O˜(epsilon^{-3}) Time. - Michael W. Mahoney; Satish Rao; Di Wang 005; Peng Zhang 052 (ICALP, 2016)

Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction. - Di Wang 005; Satish Rao; Michael W. Mahoney (ICALP, 2016)

A Simple and Strongly-Local Flow-Based Method for Cut Improvement. - Nate Veldt; David F. Gleich; Michael W. Mahoney (ICML, 2016)

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. - 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; et al. (IPDPS Workshops, 2016)

A Discriminative and Compact Audio Representation for Event Detection. - Liping Jing; Bo Liu 050; Jaeyoung Choi 002; Adam Janin; Julia Bernd; Michael W. Mahoney; Gerald Friedland (ACM Multimedia, 2016)

Sub-sampled Newton Methods with Non-uniform Sampling. - Peng Xu; Jiyan Yang; Farbod Roosta-Khorasani; Christopher Ré; Michael W. Mahoney (NIPS, 2016)

Feature-distributed sparse regression: a screen-and-clean approach. - Jiyan Yang; Michael W. Mahoney; Michael A. Saunders; Yuekai Sun (NIPS, 2016)

Weighted SGD for ℓp Regression with Randomized Preconditioning. - Jiyan Yang; Yinlam Chow; Christopher Ré; Michael W. Mahoney (SODA, 2016)

RandNLA: randomized numerical linear algebra. - Petros Drineas; Michael W. Mahoney (Commun. ACM, 2016)

FLAG: Fast Linearly-Coupled Adaptive Gradient Method. - Xiang Cheng 006; Farbod Roosta-Khorasani; Peter L. Bartlett; Michael W. Mahoney (CoRR, 2016)

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (CoRR, 2016)

DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection. - Liping Jing; Bo Liu 050; Jaeyoung Choi 002; Adam Janin; Julia Bernd; Michael W. Mahoney; Gerald Friedland (CoRR, 2016)

Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data. - David Lawlor; Tamás Budavári; Michael W. Mahoney (CoRR, 2016)

Lecture Notes on Randomized Linear Algebra. - Michael W. Mahoney (CoRR, 2016)

Lecture Notes on Spectral Graph Methods. - Michael W. Mahoney (CoRR, 2016)

Sub-Sampled Newton Methods I: Globally Convergent Algorithms. - Farbod Roosta-Khorasani; Michael W. Mahoney (CoRR, 2016)

Sub-Sampled Newton Methods II: Local Convergence Rates. - Farbod Roosta-Khorasani; Michael W. Mahoney (CoRR, 2016)

Tree decompositions and social graphs. - Aaron B. Adcock; Blair D. Sullivan; Michael W. Mahoney (Internet Math., 2016)

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. - Haim Avron; Vikas Sindhwani; Jiyan Yang; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

Revisiting the Nystrom Method for Improved Large-scale Machine Learning. - Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares. - Garvesh Raskutti; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments. - Jiyan Yang; Xiangrui Meng; Michael W. Mahoney (Proc. IEEE, 2016)

Parallel Local Graph Clustering. - Julian Shun; Farbod Roosta-Khorasani; Kimon Fountoulakis; Michael W. Mahoney (Proc. VLDB Endow., 2016)

Recent Advances in Randomized Numerical Linear Algebra (NII Shonan Meeting 2016-10). - Ravindran Kannan; Michael W. Mahoney; David P. Woodruff (NII Shonan Meet. Rep., 2016)

The Fast Cauchy Transform and Faster Robust Linear Regression. - Kenneth L. Clarkson; Petros Drineas; Malik Magdon-Ismail; Michael W. Mahoney; Xiangrui Meng; David P. Woodruff (SIAM J. Comput., 2016)

Skip-Gram - Zipf + Uniform = Vector Additivity. - Alex Gittens; Dimitris Achlioptas; Michael W. Mahoney (ACL (1), 2017)

Capacity Releasing Diffusion for Speed and Locality. - Di Wang 005; Kimon Fountoulakis; Monika Henzinger; Michael W. Mahoney; Satish Rao (ICML, 2017)

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. - 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 (NIPS, 2017)

Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds. - Shusen Wang; Alex Gittens; Michael W. Mahoney (CoRR, 2017)

Social Discrete Choice Models. - Danqing Zhang; Kimon Fountoulakis; Junyu Cao; Mogeng Yin; Michael W. Mahoney; Alexei Pozdnoukhov (CoRR, 2017)

Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior. - Charles H. Martin; Michael W. Mahoney (CoRR, 2017)

A Berkeley View of Systems Challenges for AI. - Ion Stoica; Dawn Song; Raluca Ada Popa; David A. Patterson 001; Michael W. Mahoney; Randy H. Katz; Anthony D. Joseph; Michael I. Jordan; Joseph M. Hellerstein; Joseph E. Gonzalez; Ken Goldberg; Ali Ghodsi 002; et al. (CoRR, 2017)

Lectures on Randomized Numerical Linear Algebra. - Petros Drineas; Michael W. Mahoney (CoRR, 2017)

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. - Shusen Wang; Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2017)

Weighted SGD for $ ell_p$ Regression with Randomized Preconditioning. - Jiyan Yang; Yin-Lam Chow; Christopher Ré; Michael W. Mahoney (J. Mach. Learn. Res., 2017)

A local perspective on community structure in multilayer networks. - Lucas G. S. Jeub; Michael W. Mahoney; Peter J. Mucha; Mason A. Porter (Netw. Sci., 2017)

Principles and Applications of Science of Information. - Thomas A. Courtade; Ananth Grama; Michael W. Mahoney; Tsachy Weissman (Proc. IEEE, 2017)

An Optimization Approach to Locally-Biased Graph Algorithms. - Kimon Fountoulakis; David F. Gleich; Michael W. Mahoney (Proc. IEEE, 2017)

DCAR: A Discriminative and Compact Audio Representation for Audio Processing. - Liping Jing; Bo Liu 050; Jaeyoung Choi 002; Adam Janin; Julia Bernd; Michael W. Mahoney; Gerald Friedland (IEEE Trans. Multim., 2017)

FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. - Xiang Cheng 006; Fred (Farbod) Roosta; Stefan Palombo; Peter L. Bartlett; Michael W. Mahoney (AISTATS, 2018)

Out-of-sample extension of graph adjacency spectral embedding. - Keith D. Levin; Farbod Roosta-Khorasani; Michael W. Mahoney; Carey E. Priebe (ICML, 2018)

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. - Miles E. Lopes; Shusen Wang; Michael W. Mahoney (ICML, 2018)

Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization. - Aditya Devarakonda; Kimon Fountoulakis; James Demmel; Michael W. Mahoney (IPDPS, 2018)

Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Lisa Gerhardt; Prabhat; Jey Kottalam; Michael F. Ringenburg; Kristyn J. Maschhoff (KDD, 2018)

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. - Shusen Wang; Farbod Roosta-Khorasani; Peng Xu; Michael W. Mahoney (NeurIPS, 2018)

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

LASAGNE: Locality and Structure Aware Graph Node Embedding. - Evgheniy Faerman; Felix Borutta; Kimon Fountoulakis; Michael W. Mahoney (WI, 2018)

GPU Accelerated Sub-Sampled Newton textsf{'}s Method. - Sudhir B. Kylasa; Farbod Roosta-Khorasani; Michael W. Mahoney; Ananth Grama (CoRR, 2018)

Distributed Second-order Convex Optimization. - Chih-Hao Fang; Sudhir B. Kylasa; Farbod Roosta-Khorasani; Michael W. Mahoney; Ananth Grama (CoRR, 2018)

Newton-MR: Newton's Method Without Smoothness or Convexity. - Fred (Farbod) Roosta; Yang Liu 310; Peng Xu; Michael W. Mahoney (CoRR, 2018)

Large batch size training of neural networks with adversarial training and second-order information. - Zhewei Yao; Amir Gholami; Kurt Keutzer; Michael W. Mahoney (CoRR, 2018)

A Short Introduction to Local Graph Clustering Methods and Software. - Kimon Fountoulakis; David F. Gleich; Michael W. Mahoney (CoRR, 2018)

On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent. - Noah Golmant; Nikita Vemuri; Zhewei Yao; Vladimir Feinberg; Amir Gholami; Kai Rothauge; Michael W. Mahoney; Joseph Gonzalez 001 (CoRR, 2018)

Parameter Re-Initialization through Cyclical Batch Size Schedules. - Norman Mu; Zhewei Yao; Amir Gholami; Kurt Keutzer; Michael W. Mahoney (CoRR, 2018)

Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. - Michal Derezinski; Kenneth L. Clarkson; Michael W. Mahoney; Manfred K. Warmuth (COLT, 2019)

Trust Region Based Adversarial Attack on Neural Networks. - Zhewei Yao; Amir Gholami; Peng Xu; Kurt Keutzer; Michael W. Mahoney (CVPR, 2019)

HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. - Zhen Dong 003; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (ICCV, 2019)

Traditional and Heavy Tailed Self Regularization in Neural Network Models. - Michael W. Mahoney; Charles H. Martin (ICML, 2019)

Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. - Charles H. Martin; Michael W. Mahoney (KDD, 2019)

Distributed estimation of the inverse Hessian by determinantal averaging. - Michal Derezinski; Michael W. Mahoney (NeurIPS, 2019)

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

GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. - Sudhir B. Kylasa; Fred (Farbod) Roosta; Michael W. Mahoney; Ananth Grama (SDM, 2019)

Alchemist: An Apache Spark ⇔ MPI interface. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Jey Kottalam; Lisa Gerhardt; Prabhat; Michael F. Ringenburg; Kristyn J. Maschhoff (Concurr. Comput. Pract. Exp., 2019)

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. - N. Benjamin Erichson; Lionel Mathelin; Zhewei Yao; Steven L. Brunton; Michael W. Mahoney; J. Nathan Kutz (CoRR, 2019)

Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction. - N. Benjamin Erichson; Michael Muehlebach; Michael W. Mahoney (CoRR, 2019)

Parallel and Communication Avoiding Least Angle Regression. - Swapnil Das; Jim Demmel; Kimon Fountoulakis; Laura Grigori; Michael W. Mahoney (CoRR, 2019)

Residual Networks as Nonlinear Systems: Stability Analysis using Linearization. - Kai Rothauge; Zhewei Yao; Zixi Hu; Michael W. Mahoney (CoRR, 2019)

ANODEV2: A Coupled Neural ODE Evolution Framework. - Tianjun Zhang; Zhewei Yao; Amir Gholami; Kurt Keutzer; Joseph Gonzalez 001; George Biros; Michael W. Mahoney (CoRR, 2019)

On Linear Convergence of Weighted Kernel Herding. - Rajiv Khanna; Michael W. Mahoney (CoRR, 2019)

The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science. - Michael W. Mahoney (CoRR, 2019)

Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching. - Miles E. Lopes; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2019)

Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times. - Kai Rothauge; Haripriya Ayyalasomayajula; Kristyn J. Maschhoff; Michael F. Ringenburg; Michael W. Mahoney (CoRR, 2019)

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. - Zhen Dong 003; Zhewei Yao; Yaohui Cai; Daiyaan Arfeen; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (CoRR, 2019)

Group Collaborative Representation for Image Set Classification. - Bo Liu 050; Liping Jing; Jia Li 002; Jian Yu 001; Alex Gittens; Michael W. Mahoney (Int. J. Comput. Vis., 2019)

A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication. - Miles E. Lopes; Shusen Wang; Michael W. Mahoney (J. Mach. Learn. Res., 2019)

Scalable Kernel K-Means Clustering with Nystr "om Approximation: Relative-Error Bounds. - Shusen Wang; Alex Gittens; Michael W. Mahoney (J. Mach. Learn. Res., 2019)

Variational perspective on local graph clustering. - Kimon Fountoulakis; Farbod Roosta-Khorasani; Julian Shun; Xiang Cheng 006; Michael W. Mahoney (Math. Program., 2019)

Sub-sampled Newton methods. - Farbod Roosta-Khorasani; Michael W. Mahoney (Math. Program., 2019)

Block Basis Factorization for Scalable Kernel Evaluation. - Ruoxi Wang; Yingzhou Li; Michael W. Mahoney; Eric Darve (SIAM J. Matrix Anal. Appl., 2019)

Avoiding Communication in Primal and Dual Block Coordinate Descent Methods. - Aditya Devarakonda; Kimon Fountoulakis; James Demmel; Michael W. Mahoney (SIAM J. Sci. Comput., 2019)

The Difficulties of Addressing Interdisciplinary Challenges at theFoundations of Data Science. - Michael W. Mahoney (SIGACT News, 2019)

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

Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. - Sheng Shen 001; Zhen Dong 003; Jiayu Ye; Linjian Ma; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (AAAI, 2020)

Bayesian experimental design using regularized determinantal point processes. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (AISTATS, 2020)

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. - Ping Ma 001; Xinlian Zhang; Xin Xing; Jingyi Ma; Michael W. Mahoney (AISTATS, 2020)

OverSketched Newton: Fast Convex Optimization for Serverless Systems. - Vipul Gupta; Swanand Kadhe; Thomas A. Courtade; Michael W. Mahoney; Kannan Ramchandran (IEEE BigData, 2020)

PyHessian: Neural Networks Through the Lens of the Hessian. - Zhewei Yao; Amir Gholami; Kurt Keutzer; Michael W. Mahoney (IEEE BigData, 2020)

ZeroQ: A Novel Zero Shot Quantization Framework. - Yaohui Cai; Zhewei Yao; Zhen Dong 003; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (CVPR, 2020)

MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. - Qinxin Wang; Hao Tan 002; Sheng Shen 001; Michael W. Mahoney; Zhewei Yao (EMNLP (1), 2020)

Forecasting Sequential Data Using Consistent Koopman Autoencoders. - Omri Azencot; N. Benjamin Erichson; Vanessa Lin; Michael W. Mahoney (ICML, 2020)

Error Estimation for Sketched SVD via the Bootstrap. - Miles E. Lopes; N. Benjamin Erichson; Michael W. Mahoney (ICML, 2020)

PowerNorm: Rethinking Batch Normalization in Transformers. - Sheng Shen 001; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (ICML, 2020)

JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. - N. Benjamin Erichson; Zhewei Yao; Michael W. Mahoney (ICPRAM, 2020)

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

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

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (NeurIPS, 2020)

Precise expressions for random projections: Low-rank approximation and randomized Newton. - Michal Derezinski; Feynman T. Liang; Zhenyu Liao 001; Michael W. Mahoney (NeurIPS, 2020)

Exact expressions for double descent and implicit regularization via surrogate random design. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (NeurIPS, 2020)

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. - Zhen Dong 003; Zhewei Yao; Daiyaan Arfeen; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (NeurIPS, 2020)

A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. - Zhenyu Liao 001; Romain Couillet; Michael W. Mahoney (NeurIPS, 2020)

Boundary thickness and robustness in learning models. - Yaoqing Yang; Rajiv Khanna; Yaodong Yu; Amir Gholami; Kurt Keutzer; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2020)

Newton-ADMM: a distributed GPU-accelerated optimizer for multiclass classification problems. - Chih-Hao Fang; Sudhir B. Kylasa; Fred Roosta; Michael W. Mahoney; Ananth Grama (SC, 2020)

Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. - Charles H. Martin; Michael W. Mahoney (SDM, 2020)

Second-order Optimization for Non-convex Machine Learning: an Empirical Study. - Peng Xu; Fred Roosta; Michael W. Mahoney (SDM, 2020)

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data. - Charles H. Martin; Tongsu Peng; Michael W. Mahoney (CoRR, 2020)

Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Stochastic Normalizing Flows. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (CoRR, 2020)

Rethinking Batch Normalization in Transformers. - Sheng Shen 001; Zhewei Yao; Amir Gholami; Michael W. Mahoney; Kurt Keutzer (CoRR, 2020)

Determinantal Point Processes in Randomized Numerical Linear Algebra. - Michal Derezinski; Michael W. Mahoney (CoRR, 2020)

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. - Zhewei Yao; Amir Gholami; Sheng Shen 001; Kurt Keutzer; Michael W. Mahoney (CoRR, 2020)

Lipschitz Recurrent Neural Networks. - N. Benjamin Erichson; Omri Azencot; Alejandro F. Queiruga; Michael W. Mahoney (CoRR, 2020)

Good linear classifiers are abundant in the interpolating regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (CoRR, 2020)

Adversarially-Trained Deep Nets Transfer Better. - Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. - N. Benjamin Erichson; Dane Taylor; Qixuan Wu; Michael W. Mahoney (CoRR, 2020)

Continuous-in-Depth Neural Networks. - Alejandro F. Queiruga; N. Benjamin Erichson; Dane Taylor; Michael W. Mahoney (CoRR, 2020)

Benchmarking Semi-supervised Federated Learning. - Zhengming Zhang 001; Zhewei Yao; Yaoqing Yang; Yujun Yan; Joseph E. Gonzalez; Michael W. Mahoney (CoRR, 2020)

Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism. - Vipul Gupta; Dhruv Choudhary; Ping Tak Peter Tang; Xiaohan Wei; Xing Wang; Yuzhen Huang; Arun Kejariwal; Kannan Ramchandran; Michael W. Mahoney (CoRR, 2020)

HAWQV3: Dyadic Neural Network Quantization. - Zhewei Yao; Zhen Dong 003; Zhangcheng Zheng; Amir Gholami; Jiali Yu; Eric Tan; Leyuan Wang; Qijing Huang 001; Yida Wang; Michael W. Mahoney; Kurt Keutzer (CoRR, 2020)

Newton-type methods for non-convex optimization under inexact Hessian information. - Peng Xu; Fred Roosta; Michael W. Mahoney (Math. Program., 2020)

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. - Zhewei Yao; Amir Gholami; Sheng Shen 001; Mustafa Mustafa; Kurt Keutzer; Michael W. Mahoney (AAAI, 2021)

Good Classifiers are Abundant in the Interpolating Regime. - Ryan Theisen; Jason M. Klusowski; Michael W. Mahoney (AISTATS, 2021)

Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. - Zhengming Zhang 001; Yaoqing Yang; Zhewei Yao; Yujun Yan; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (IEEE BigData, 2021)

Sparse sketches with small inversion bias. - Michal Derezinski; Zhenyu Liao 001; Edgar Dobriban; Michael W. Mahoney (COLT, 2021)

What's Hidden in a One-layer Randomly Weighted Transformer? - Sheng Shen 001; Zhewei Yao; Douwe Kiela; Kurt Keutzer; Michael W. Mahoney (EMNLP (1), 2021)

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

Sparse Quantized Spectral Clustering. - Zhenyu Liao 001; Romain Couillet; Michael W. Mahoney (ICLR, 2021)

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

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. - Jianfei Chen; Lianmin Zheng; Zhewei Yao; Dequan Wang; Ion Stoica; Michael W. Mahoney; Joseph Gonzalez 001 (ICML, 2021)

Multiplicative Noise and Heavy Tails in Stochastic Optimization. - Liam Hodgkinson; Michael W. Mahoney (ICML, 2021)

I-BERT: Integer-only BERT Quantization. - Sehoon Kim 001; Amir Gholami; Zhewei Yao; Michael W. Mahoney; Kurt Keutzer (ICML, 2021)

HAWQ-V3: Dyadic Neural Network Quantization. - Zhewei Yao; Zhen Dong 003; Zhangcheng Zheng; Amir Gholami; Jiali Yu; Eric Tan; Leyuan Wang; Qijing Huang 001; Yida Wang; Michael W. Mahoney; Kurt Keutzer (ICML, 2021)

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

Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism. - Vipul Gupta; Dhruv Choudhary; Ping Tak Peter Tang; Xiaohan Wei; Xing Wang; Yuzhen Huang; Arun Kejariwal; Kannan Ramchandran; Michael W. Mahoney (KDD, 2021)

Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. - Michal Derezinski; Jonathan Lacotte; Mert Pilanci; Michael W. Mahoney (NeurIPS, 2021)

Characterizing possible failure modes in physics-informed neural networks. - Aditi S. Krishnapriyan; Amir Gholami; Shandian Zhe; Robert M. Kirby; Michael W. Mahoney (NeurIPS, 2021)

Hessian Eigenspectra of More Realistic Nonlinear Models. - Zhenyu Liao 001; Michael W. Mahoney (NeurIPS, 2021)

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

Stateful ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2021)

Taxonomizing local versus global structure in neural network loss landscapes. - Yaoqing Yang; Liam Hodgkinson; Ryan Theisen; Joe Zou; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2021)

Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. - N. Benjamin Erichson; Dane Taylor; Qixuan Wu; Michael W. Mahoney (SDM, 2021)

LocalNewton: Reducing communication rounds for distributed learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (UAI, 2021)

Stochastic continuous normalizing flows: training SDEs as ODEs. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (UAI, 2021)

Geometric rates of convergence for kernel-based sampling algorithms. - Rajiv Khanna; Liam Hodgkinson; Michael W. Mahoney (UAI, 2021)

Hessian-Aware Pruning and Optimal Neural Implant. - Shixing Yu; Zhewei Yao; Amir Gholami; Zhen Dong 003; Michael W. Mahoney; Kurt Keutzer (CoRR, 2021)

A Differential Geometry Perspective on Orthogonal Recurrent Models. - Omri Azencot; N. Benjamin Erichson; Mirela Ben-Chen; Michael W. Mahoney (CoRR, 2021)

A Survey of Quantization Methods for Efficient Neural Network Inference. - Amir Gholami; Sehoon Kim 001; Zhen Dong 003; Zhewei Yao; Michael W. Mahoney; Kurt Keutzer (CoRR, 2021)

Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition. - Sehoon Kim 001; Amir Gholami; Zhewei Yao; Aniruddha Nrusimha; Bohan Zhai; Tianren Gao; Michael W. Mahoney; Kurt Keutzer (CoRR, 2021)

LocalNewton: Reducing Communication Bottleneck for Distributed Learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (CoRR, 2021)

MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models. - Zhewei Yao; Linjian Ma; Sheng Shen 001; Kurt Keutzer; Michael W. Mahoney (CoRR, 2021)

Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics. - Charles H. Martin; Michael W. Mahoney (CoRR, 2021)

Compressing Deep ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (CoRR, 2021)

Generalization Properties of Stochastic Optimizers via Trajectory Analysis. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (CoRR, 2021)

Learning from learning machines: a new generation of AI technology to meet the needs of science. - 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; et al. (CoRR, 2021)

Statistical guarantees for local graph clustering. - Wooseok Ha; Kimon Fountoulakis; Michael W. Mahoney (J. Mach. Learn. Res., 2021)

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. - Keith D. Levin; Fred Roosta; Minh Tang; Michael W. Mahoney; Carey E. Priebe (J. Mach. Learn. Res., 2021)

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. - Charles H. Martin; Michael W. Mahoney (J. Mach. Learn. Res., 2021)

Parallel and Communication Avoiding Least Angle Regression. - Swapnil Das; James Demmel; Kimon Fountoulakis; Laura Grigori; Michael W. Mahoney; Shenghao Yang 002 (SIAM J. Sci. Comput., 2021)

Inexact Nonconvex Newton-Type Methods. - Zhewei Yao; Peng Xu; Fred Roosta; Michael W. Mahoney (INFORMS J. Optim., 2021)

Integer-Only Zero-Shot Quantization for Efficient Speech Recognition. - Sehoon Kim 001; Amir Gholami; Zhewei Yao; Nicholas Lee; Patrick Wang; Aniruddha Nrusimha; Bohan Zhai; Tianren Gao; Michael W. Mahoney; Kurt Keutzer (ICASSP, 2022)

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. - Majid Jahani; Sergey Rusakov 001; Zheng Shi; Peter Richtárik; Michael W. Mahoney; Martin Takác 001 (ICLR, 2022)

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

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

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (ICML, 2022)

Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. - Feynman T. Liang; Michael W. Mahoney; Liam Hodgkinson (ICML, 2022)

GACT: Activation Compressed Training for Generic Network Architectures. - Xiaoxuan Liu; Lianmin Zheng; Dequan Wang; Yukuo Cen; Weize Chen; Xu Han 007; Jianfei Chen; Zhiyuan Liu 001; Jie Tang 001; Joey Gonzalez; Michael W. Mahoney; Alvin Cheung (ICML, 2022)

AutoIP: A United Framework to Integrate Physics into Gaussian Processes. - Da Long; Zheng Wang 042; Aditi S. Krishnapriyan; Robert M. Kirby; Shandian Zhe; Michael W. Mahoney (ICML, 2022)

Neurotoxin: Durable Backdoors in Federated Learning. - Zhengming Zhang 001; Ashwinee Panda; Linyue Song; Yaoqing Yang; Michael W. Mahoney; Prateek Mittal; Kannan Ramchandran; Joseph Gonzalez 001 (ICML, 2022)

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition. - Sehoon Kim 001; Amir Gholami; Albert E. Shaw; Nicholas Lee; Karttikeya Mangalam; Jitendra Malik; Michael W. Mahoney; Kurt Keutzer (NeurIPS, 2022)

A Fast Post-Training Pruning Framework for Transformers. - Woosuk Kwon; Sehoon Kim 001; Michael W. Mahoney; Joseph Hassoun; Kurt Keutzer; Amir Gholami (NeurIPS, 2022)

Hessian-Aware Pruning and Optimal Neural Implant. - Shixing Yu; Zhewei Yao; Amir Gholami; Zhen Dong 003; Sehoon Kim 001; Michael W. Mahoney; Kurt Keutzer (WACV, 2022)

NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. - N. Benjamin Erichson; Soon Hoe Lim; Francisco Utrera; Winnie Xu; Ziang Cao; Michael W. Mahoney (CoRR, 2022)

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (CoRR, 2022)

Learning continuous models for continuous physics. - Aditi S. Krishnapriyan; Alejandro F. Queiruga; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2022)

The Sky Above The Clouds. - Sarah E. Chasins; Alvin Cheung; Natacha Crooks; Ali Ghodsi 002; Ken Goldberg; Joseph E. Gonzalez; Joseph M. Hellerstein; Michael I. Jordan; Anthony D. Joseph; Michael W. Mahoney; Aditya G. Parameswaran; David A. Patterson 001; et al. (CoRR, 2022)

Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. - Sen Na; Michael W. Mahoney (CoRR, 2022)

GACT: Activation Compressed Training for General Architectures. - Xiaoxuan Liu; Lianmin Zheng; Dequan Wang; Yukuo Cen; Weize Chen; Xu Han 007; Jianfei Chen; Zhiyuan Liu 001; Jie Tang 001; Joey Gonzalez; Michael W. Mahoney; Alvin Cheung (CoRR, 2022)

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver. - Fred Roosta; Yang Liu 310; Peng Xu; Michael W. Mahoney (EURO J. Comput. Optim., 2022)

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. - Ali Eshragh; Fred Roosta; Asef Nazari; Michael W. Mahoney (J. Mach. Learn. Res., 2022)

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. - Ping Ma 001; Yongkai Chen; Xinlian Zhang; Xin Xing; Jingyi Ma; Michael W. Mahoney (J. Mach. Learn. Res., 2022)

Fast Feature Selection with Fairness Constraints. - Francesco Quinzan; Rajiv Khanna; Moshik Hershcovitch; Sarel Cohen; Daniel G. Waddington; Tobias Friedrich 001; Michael W. Mahoney (AISTATS, 2023)

Adaptive Self-Supervision Algorithms for Physics-Informed Neural Networks. - Shashank Subramanian; Robert M. Kirby; Michael W. Mahoney; Amir Gholami (ECAI, 2023)

Learning differentiable solvers for systems with hard constraints. - Geoffrey Négiar; Michael W. Mahoney; Aditi S. Krishnapriyan (ICLR, 2023)

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

Learning Physical Models that Can Respect Conservation Laws. - Derek Hansen; Danielle C. Maddix; Shima Alizadeh; Gaurav Gupta; Michael W. Mahoney (ICML, 2023)

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (ICML, 2023)

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

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

Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (KDD, 2023)

Speculative Decoding with Big Little Decoder. - Sehoon Kim 001; Karttikeya Mangalam; Suhong Moon; Jitendra Malik; Michael W. Mahoney; Amir Gholami; Kurt Keutzer (NeurIPS, 2023)

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

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

When are ensembles really effective? - Ryan Theisen; Hyunsuk Kim; Yaoqing Yang; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2023)

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

Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. - Burlen Loring; E. Wes Bethel; Gunther H. Weber; Michael W. Mahoney (SC Workshops, 2023)

Big Little Transformer Decoder. - Sehoon Kim 001; Karttikeya Mangalam; Jitendra Malik; Michael W. Mahoney; Amir Gholami; Kurt Keutzer (CoRR, 2023)

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. - 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; et al. (CoRR, 2023)

Full Stack Optimization of Transformer Inference: a Survey. - Sehoon Kim 001; Coleman Hooper; Thanakul Wattanawong; Minwoo Kang; Ruohan Yan; Hasan Genc; Grace Dinh; Qijing Huang 001; Kurt Keutzer; Michael W. Mahoney; Yakun Sophia Shao; Amir Gholami (CoRR, 2023)

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs. - Javier Campos; Zhen Dong 003; Javier M. Duarte; Amir Gholami; Michael W. Mahoney; Jovan Mitrevski; Nhan Tran (CoRR, 2023)

SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning. - Pu Ren; N. Benjamin Erichson; Shashank Subramanian; Omer San; Zarija Lukic; Michael W. Mahoney (CoRR, 2023)

GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting. - Sitan Yang; Malcolm Wolff; Shankar Ramasubramanian; Vincent Quenneville-Bélair; Ronak Metha; Michael W. Mahoney (CoRR, 2023)

The Interpolating Information Criterion for Overparameterized Models. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

Probabilistic Forecasting with Coherent Aggregation. - Geoffrey Négiar; Ruijun Ma; O. Nganba Meetei; Mengfei Cao; Michael W. Mahoney (CoRR, 2023)

A PAC-Bayesian Perspective on the Interpolating Information Criterion. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

DMLR: Data-centric Machine Learning Research - Past, Present and Future. - Luis Oala; Manil Maskey; Lilith Bat-Leah; Alicia Parrish; Nezihe Merve Gürel; Tzu-Sheng Kuo; Yang Liu 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 2023)

Multi-scale Local Network Structure Critically Impacts Epidemic Spread and Interventions. - Omar Eldaghar; Michael W. Mahoney; David F. Gleich (CoRR, 2023)

Hessian averaging in stochastic Newton methods achieves superlinear convergence. - Sen Na; Michal Derezinski; Michael W. Mahoney (Math. Program., 2023)

Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance. - Kimon Fountoulakis; Meng Liu 027; David F. Gleich; Michael W. Mahoney (SIAM Rev., 2023)

LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. - Nicholas Lee; Thanakul Wattanawong; Sehoon Kim 001; Karttikeya Mangalam; Sheng Shen 001; Gopala Anumanchipalli; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (ACL (Findings), 2024)

NoisyMix: Boosting Model Robustness to Common Corruptions. - N. Benjamin Erichson; Soon Hoe Lim; Winnie Xu; Francisco Utrera; Ziang Cao; Michael W. Mahoney (AISTATS, 2024)

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

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

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

SqueezeLLM: Dense-and-Sparse Quantization. - Sehoon Kim 001; Coleman Hooper; Amir Gholami; Zhen Dong 003; Xiuyu Li; Sheng Shen 001; Michael W. Mahoney; Kurt Keutzer (ICML, 2024)

An LLM Compiler for Parallel Function Calling. - Sehoon Kim 001; Suhong Moon; Ryan Tabrizi; Nicholas Lee; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (ICML, 2024)

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. - S. Chandra Mouli; Danielle C. Maddix; Shima Alizadeh; Gaurav Gupta; Andrew Stuart; Michael W. Mahoney; Bernie Wang 001 (ICML, 2024)

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

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. - Michal Derezinski; Michael W. Mahoney (KDD, 2024)

Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning. - Wuyang Chen 001; Jialin Song; Pu Ren; Shashank Subramanian; Dmitriy Morozov; Michael W. Mahoney (NeurIPS, 2024)

KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization. - Coleman Hooper; Sehoon Kim 001; Hiva Mohammadzadeh; Michael W. Mahoney; Yakun Sophia Shao; Kurt Keutzer; Amir Gholami (NeurIPS, 2024)

How many classifiers do we need? - Hyunsuk Kim; Liam Hodgkinson; Ryan Theisen; Michael W. Mahoney (NeurIPS, 2024)

Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance. - Haiquan Lu; Xiaotian Liu; Yefan Zhou; Qunli Li; Kurt Keutzer; Michael W. Mahoney; Yujun Yan; Huanrui Yang; Yaoqing Yang (NeurIPS, 2024)

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. - Haiquan Lu; Yefan Zhou; Shiwei Liu 003; Zhangyang Wang; Michael W. Mahoney; Yaoqing Yang (NeurIPS, 2024)

Reliable edge machine learning hardware for scientific applications. - Tommaso Baldi; Javier Campos; Benjamin Hawks; Jennifer Ngadiuba; Nhan Tran; Daniel Diaz 003; Javier M. Duarte; Ryan Kastner; Andres Meza 001; Melissa Quinnan; Olivia Weng; Caleb Geniesse; et al. (VTS, 2024)

SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data. - Ali Eshragh; Luke Yerbury; Asef Nazari; Fred Roosta; Michael W. Mahoney (CoRR, 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (CoRR, 2024)

There is HOPE to Avoid HiPPOs for Long-memory State Space Models. - Annan Yu; Michael W. Mahoney; N. Benjamin Erichson (CoRR, 2024)

WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning. - Dongwei Lyu; Rie Nakata; Pu Ren; Michael W. Mahoney; Arben Pitarka; Nori Nakata; N. Benjamin Erichson (CoRR, 2024)

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. - Matthias Karlbauer; Danielle C. Maddix; Abdul Fatir Ansari; Boran Han; Gaurav Gupta; Yuyang Wang 001; Andrew Stuart; Michael W. Mahoney (CoRR, 2024)

Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. - 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 (CoRR, 2024)

Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (CoRR, 2024)

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

Squeezed Attention: Accelerating Long Context Length LLM Inference. - Coleman Hooper; Sehoon Kim 001; Hiva Mohammadzadeh; Monishwaran Maheswaran; June Paik; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (CoRR, 2024)

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

Visualizing Loss Functions as Topological Landscape Profiles. - Caleb Geniesse; Jiaqing Chen; Tiankai Xie; Ge Shi 007; Yaoqing Yang; Dmitriy Morozov; Talita Perciano; Michael W. Mahoney; Ross Maciejewski; Gunther H. Weber (CoRR, 2024)

Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2024)

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

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. - Luca Masserano; Abdul Fatir Ansari; Boran Han; Xiyuan Zhang; Christos Faloutsos; Michael W. Mahoney; Andrew Gordon Wilson; Youngsuk Park; Syama Sundar Rangapuram; Danielle C. Maddix; Yuyang Wang 001 (CoRR, 2024)

AI and Memory Wall. - Amir Gholami; Zhewei Yao; Sehoon Kim 001; Coleman Hooper; Michael W. Mahoney; Kurt Keutzer (IEEE Micro, 2024)

Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (SIAM J. Optim., 2024)

Chronos: Learning the Language of Time Series. - 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; et al. (Trans. Mach. Learn. Res., 2024)

♣ CLOVER ♣: Probabilistic Forecasting with Coherent Learning Objective Reparameterization. - Kin G. Olivares; Geoffrey Négiar; Ruijun Ma; Oinam Nganba Meetei; Mengfei Cao; Michael W. Mahoney (Trans. Mach. Learn. Res., 2024)

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs. - Javier Campos; Jovan Mitrevski; Nhan Tran; Zhen Dong 003; Amir Gholaminejad; Michael W. Mahoney; Javier M. Duarte (ACM Trans. Reconfigurable Technol. Syst., 2024)

Squeezed Attention: Accelerating Long Context Length LLM Inference. - Coleman Richard Charles Hooper; Sehoon Kim 001; Hiva Mohammadzadeh; Monishwaran Maheswaran; Sebastian Zhao; June Paik; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (ACL (1), 2025)

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

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

Gradient-Free Generation for Hard-Constrained Systems. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Bernie Wang 001 (ICLR, 2025)

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

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

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

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics. - Tiankai Xie; Jiaqing Chen; Yaoqing Yang; Caleb Geniesse; Ge Shi 007; Ajinkya Chaudhari; John Kevin Cava; Michael W. Mahoney; Talita Perciano; Gunther H. Weber; Ross Maciejewski (IEEE Computer Graphics and Applications, 2025)

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems. - Younghyun Cho; James Demmel; Michal Derezinski; Haoyun Li; Hengrui Luo; Michael W. Mahoney; Riley Murray (SIAM J. Matrix Anal. Appl., 2025)

CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT). - Maksim Melnichenko; Oleg Balabanov; Riley Murray; James Demmel; Michael W. Mahoney; Piotr Luszczek (SIAM J. Matrix Anal. Appl., 2025)

Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. - Soon Hoe Lim; Yijin Wang; Annan Yu; Emma Hart; Michael W. Mahoney; Xiaoye Sherry Li; N. Benjamin Erichson (Trans. Mach. Learn. Res., 2025)

Using Pre-trained LLMs for Multivariate Time Series Forecasting. - Malcolm Wolff; Shenghao Yang 002; Kari Torkkola; Michael W. Mahoney (CoRR, 2025)

Neural equilibria for long-term prediction of nonlinear conservation laws. - Jose Antonio Lara Benitez; Junyi Guo; Kareem Hegazy; Ivan Dokmanic; Michael W. Mahoney; Maarten V. de Hoop (CoRR, 2025)

Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting. - Kareem Hegazy; Michael W. Mahoney; N. Benjamin Erichson (CoRR, 2025)

QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache. - Rishabh Tiwari; Haocheng Xi; Aditya Tomar; Coleman Hooper; Sehoon Kim 001; Maxwell Horton; Mahyar Najibi; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (CoRR, 2025)

MatterChat: A Multi-Modal LLM for Material Science. - Yingheng Tang; Wenbin Xu; Jie Cao 010; Jianzhu Ma; Weilu Gao; Steve Farrell; N. Benjamin Erichson; Michael W. Mahoney; Andy Nonaka; Zhi Yao (CoRR, 2025)

ETS: Efficient Tree Search for Inference-Time Scaling. - Coleman Hooper; Sehoon Kim 001; Suhong Moon; Kerem Dilmen; Monishwaran Maheswaran; Nicholas Lee; Michael W. Mahoney; Yakun Sophia Shao; Kurt Keutzer; Amir Gholami (CoRR, 2025)

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. - Chengmei Niu; Zhenyu Liao 001; Zenan Ling; Michael W. Mahoney (CoRR, 2025)

Determinant Estimation under Memory Constraints and Neural Scaling Laws. - Siavash Ameli; Chris van der Heide; Liam Hodgkinson; Fred Roosta; Michael W. Mahoney (CoRR, 2025)

Paving the way for scientific foundation models: enhancing generalization and robustness in PDEs with constraint-aware pre-training. - Amin Totounferoush; Serge Kotchourko; Michael W. Mahoney; Steffen Staab (CoRR, 2025)

Removing Watermarks with Partial Regeneration using Semantic Information. - Krti Tallam; John Kevin Cava; Caleb Geniesse; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2025)

FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems. - N. Benjamin Erichson; Vinicius Mikuni; Dongwei Lyu; Yang Gao; Omri Azencot; Soon Hoe Lim; Michael W. Mahoney (CoRR, 2025)

Models of Heavy-Tailed Mechanistic Universality. - Liam Hodgkinson; Zhichao Wang; Michael W. Mahoney (CoRR, 2025)

End-to-End Probabilistic Framework for Learning with Hard Constraints. - Utkarsh Utkarsh; Danielle C. Maddix; Ruijun Ma; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2025)

Spectral Estimation with Free Decompression. - Siavash Ameli; Chris van der Heide; Liam Hodgkinson; Michael W. Mahoney (CoRR, 2025)

Multipole Attention for Efficient Long Context Reasoning. - Coleman Hooper; Sebastian Zhao; Luca Manolache; Sehoon Kim 001; Michael W. Mahoney; Yakun Sophia Shao; Kurt Keutzer; Amir Gholami (CoRR, 2025)

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models. - Zhenyu Liao 001; Michael W. Mahoney (CoRR, 2025)

Does Multimodality Lead to Better Time Series Forecasting? - Xiyuan Zhang; Boran Han; Haoyang Fang; Abdul Fatir Ansari; Shuai Zhang 007; Danielle C. Maddix; Cuixiong Hu; Andrew Gordon Wilson; Michael W. Mahoney; Hao Wang 014; Yan Liu; Huzefa Rangwala; et al. (CoRR, 2025)

Anatomy of High-Performance Column-Pivoted QR Decomposition. - Maksim Melnichenko; Riley Murray; William Killian; James Demmel; Michael W. Mahoney; Piotr Luszczek; Mark Gates (CoRR, 2025)

TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting. - Zhiyuan Zhao 002; Sitan Yang; Kin G. Olivares; Boris N. Oreshkin; Stan Vitebsky; Michael W. Mahoney; B. Aditya Prakash; Dmitry Efimov (CoRR, 2025)

XQuant: Breaking the Memory Wall for LLM Inference with KV Cache Rematerialization. - Aditya Tomar; Coleman Hooper; Minjae Lee; Haocheng Xi; Rishabh Tiwari; Wonjun Kang; Luca Manolache; Michael W. Mahoney; Kurt Keutzer; Amir Gholami (CoRR, 2025)