Uniform Sampling for Matrix Approximation. - Michael B. Cohen; Yin Tat Lee; Cameron Musco; Christopher Musco; Richard Peng; Aaron Sidford (ITCS, 2015)

Principled Sampling for Anomaly Detection. - Brendan Juba; Christopher Musco; Fan Long; Stelios Sidiroglou-Douskos; Martin C. Rinard (NDSS, 2015)

Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition. - Cameron Musco; Christopher Musco (NIPS, 2015)

Dimensionality Reduction for k-Means Clustering and Low Rank Approximation. - Michael B. Cohen; Sam Elder; Cameron Musco; Christopher Musco; Madalina Persu (STOC, 2015)

Ridge Leverage Scores for Low-Rank Approximation. - Michael B. Cohen; Cameron Musco; Christopher Musco (CoRR, 2015)

Stronger Approximate Singular Value Decomposition via the Block Lanczos and Power Methods. - Cameron Musco; Christopher Musco (CoRR, 2015)

Principal Component Projection Without Principal Component Analysis. - Roy Frostig; Cameron Musco; Christopher Musco; Aaron Sidford (ICML, 2016)

Provably Useful Kernel Matrix Approximation in Linear Time. - Cameron Musco; Christopher Musco (CoRR, 2016)

Determining Tournament Payout Structures for Daily Fantasy Sports. - Christopher Musco; Maxim Sviridenko; Justin Thaler (ALENEX, 2017)

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. - Haim Avron; Michael Kapralov; Cameron Musco; Christopher Musco; Ameya Velingker; Amir Zandieh (ICML, 2017)

Recursive Sampling for the Nystrom Method. - Cameron Musco; Christopher Musco (NIPS, 2017)

Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling. - Michael B. Cohen; Cameron Musco; Christopher Musco (SODA, 2017)

Single Pass Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Yin Tat Lee; Cameron Musco; Christopher Musco; Aaron Sidford (SIAM J. Comput., 2017)

Eigenvector Computation and Community Detection in Asynchronous Gossip Models. - Frederik Mallmann-Trenn; Cameron Musco; Christopher Musco (ICALP, 2018)

Inferring Networks From Random Walk-Based Node Similarities. - Jeremy G. Hoskins; Cameron Musco; Christopher Musco; Babis Tsourakakis (NeurIPS, 2018)

Stability of the Lanczos Method for Matrix Function Approximation. - Cameron Musco; Christopher Musco; Aaron Sidford (SODA, 2018)

Minimizing Polarization and Disagreement in Social Networks. - Cameron Musco; Christopher Musco; Charalampos E. Tsourakakis (WWW, 2018)

Learning Networks from Random Walk-Based Node Similarities. - Jeremy G. Hoskins; Cameron Musco; Christopher Musco; Charalampos E. Tsourakakis (CoRR, 2018)

Faster linear algebra for data analysis and machine learning. - Christopher Musco (2018)

A universal sampling method for reconstructing signals with simple Fourier transforms. - Haim Avron; Michael Kapralov; Cameron Musco; Christopher Musco; Ameya Velingker; Amir Zandieh (STOC, 2019)

Faster Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Aida Mousavifar; Cameron Musco; Christopher Musco; Navid Nouri (CoRR, 2019)

Low-Rank Approximation from Communication Complexity. - Cameron Musco; Christopher Musco; David P. Woodruff (CoRR, 2019)

Understanding Filter Bubbles and Polarization in Social Networks. - Uthsav Chitra; Christopher Musco (CoRR, 2019)

Finding the Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (CoRR, 2019)

Near Optimal Linear Algebra in the Online and Sliding Window Models. - Vladimir Braverman; Petros Drineas; Cameron Musco; Christopher Musco; Jalaj Upadhyay; David P. Woodruff; Samson Zhou (FOCS, 2020)

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. - Hannah Lawrence; Jerry Li 001; Cameron Musco; Christopher Musco (ICASSP, 2020)

Fourier Sparse Leverage Scores and Approximate Kernel Learning. - Tamás Erdélyi; Cameron Musco; Christopher Musco (NeurIPS, 2020)

The Statistical Cost of Robust Kernel Hyperparameter Turning. - Raphael A. Meyer; Christopher Musco (NeurIPS, 2020)

Sample Efficient Toeplitz Covariance Estimation. - Yonina C. Eldar; Jerry Li 001; Cameron Musco; Christopher Musco (SODA, 2020)

Fast and Space Efficient Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Aida Mousavifar; Cameron Musco; Christopher Musco; Navid Nouri; Aaron Sidford; Jakab Tardos (SODA, 2020)

Analyzing the Impact of Filter Bubbles on Social Network Polarization. - Uthsav Chitra; Christopher Musco (WSDM, 2020)

Projection-Cost-Preserving Sketches: Proof Strategies and Constructions. - Cameron Musco; Christopher Musco (CoRR, 2020)

The Statistical Cost of Robust Kernel Hyperparameter Tuning. - Raphael A. Meyer; Christopher Musco (CoRR, 2020)

Graph Learning for Inverse Landscape Genetics. - Prathamesh Dharangutte; Christopher Musco (AAAI, 2021)

Finding an Approximate Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (ESA, 2021)

Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation. - Cameron Musco; Christopher Musco; David P. Woodruff (ITCS, 2021)

Dynamic Trace Estimation. - Prathamesh Dharangutte; Christopher Musco (NeurIPS, 2021)

Correlation Sketches for Approximate Join-Correlation Queries. - Aécio S. R. Santos; Aline Bessa; Fernando Chirigati; Christopher Musco; Juliana Freire (SIGMOD Conference, 2021)

Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand. - Sheng Wang 007; Yuan Sun 003; Christopher Musco; Zhifeng Bao (SIGMOD Conference, 2021)

Hutch++: Optimal Stochastic Trace Estimation. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff (SOSA, 2021)

Linear and Sublinear Time Spectral Density Estimation. - Vladimir Braverman; Aditya Krishnan 001; Christopher Musco (CoRR, 2021)

How to Quantify Polarization in Models of Opinion Dynamics. - Christopher Musco; Indu Ramesh; Johan Ugander; R. Teal Witter (CoRR, 2021)

Active Sampling for Linear Regression Beyond the $ ell_2$ Norm. - Cameron Musco; Christopher Musco; David P. Woodruff; Taisuke Yasuda 002 (CoRR, 2021)

Active Linear Regression for ℓp Norms and Beyond. - Cameron Musco; Christopher Musco; David P. Woodruff; Taisuke Yasuda 002 (FOCS, 2022)

A Sketch-based Index for Correlated Dataset Search. - Aécio S. R. Santos; Aline Bessa; Christopher Musco; Juliana Freire (ICDE, 2022)

Fast Regression for Structured Inputs. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (ICLR, 2022)

Sublinear time spectral density estimation. - Vladimir Braverman; Aditya Krishnan 001; Christopher Musco (STOC, 2022)

Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. - Mengxi Wu; Yi-Jen Chiang; Christopher Musco (Comput. Graph. Forum, 2022)

Near-Linear Sample Complexity for Lp Polynomial Regression. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (CoRR, 2022)

Error Bounds for Lanczos-Based Matrix Function Approximation. - Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (SIAM J. Matrix Anal. Appl., 2022)

Active Learning for Single Neuron Models with Lipschitz Non-Linearities. - Aarshvi Gajjar; Christopher Musco; Chinmay Hegde (AISTATS, 2023)

Moments, Random Walks, and Limits for Spectrum Approximation. - Yujia Jin; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (COLT, 2023)

Efficient Block Approximate Matrix Multiplication. - Chuhan Yang; Christopher Musco (ESA, 2023)

Dimensionality Reduction for General KDE Mode Finding. - Xinyu Luo; Christopher Musco; Cas Widdershoven (ICML, 2023)

Structured Semidefinite Programming for Recovering Structured Preconditioners. - Arun Jambulapati; Jerry Li 001; Christopher Musco; Kirankumar Shiragur; Aaron Sidford; Kevin Tian (NeurIPS, 2023)

Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation. - Aline Bessa; Majid Daliri; Juliana Freire; Cameron Musco; Christopher Musco; Aécio S. R. Santos; Haoxiang Zhang 003 (PODS, 2023)

Near-Linear Sample Complexity for Lp Polynomial Regression. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (SODA, 2023)

A Tight Analysis of Hutchinson's Diagonal Estimator. - Prathamesh Dharangutte; Christopher Musco (SOSA, 2023)

Near-Optimality Guarantees for Approximating Rational Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (CoRR, 2023)

Low-Memory Krylov Subspace Methods for Optimal Rational Matrix Function Approximation. - Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (SIAM J. Matrix Anal. Appl., 2023)

A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy. - Lucas Rosenblatt; Julia Stoyanovich; Christopher Musco (AAAI, 2024)

Agnostic Active Learning of Single Index Models with Linear Sample Complexity. - Aarshvi Gajjar; Wai Ming Tai; Xingyu Xu 001; Chinmay Hegde; Christopher Musco; Yi Li (COLT, 2024)

Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract). - Yujia Jin; Ishani Karmarkar; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (COLT, 2024)

Improved Active Learning via Dependent Leverage Score Sampling. - Atsushi Shimizu; Xiaoou Cheng 001; Christopher Musco; Jonathan Weare (ICLR, 2024)

Nearly Optimal Approximation of Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (NeurIPS, 2024)

Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits. - Haya Diwan; Jinrui Gou; Cameron Musco; Christopher Musco; Torsten Suel (NeurIPS, 2024)

Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm. - R. Teal Witter; Christopher Musco (NeurIPS, 2024)

On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation. - Raphael A. Meyer; Cameron Musco; Christopher Musco (SODA, 2024)

Simple Analysis of Priority Sampling. - Majid Daliri; Juliana Freire; Christopher Musco; Aécio S. R. Santos; Haoxiang Zhang 003 (SOSA, 2024)

Fixed-sparsity matrix approximation from matrix-vector products. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco (CoRR, 2024)

Faster Spectral Density Estimation and Sparsification in the Nuclear Norm. - Yujia Jin; Ishani Karmarkar; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (CoRR, 2024)

Coupling without Communication and Drafter-Invariant Speculative Decoding. - Majid Daliri; Christopher Musco; Ananda Theertha Suresh (CoRR, 2024)

Sharper Bounds for Chebyshev Moment Matching with Applications to Differential Privacy and Beyond. - Cameron Musco; Christopher Musco; Lucas Rosenblatt; Apoorv Vikram Singh (CoRR, 2024)

Sampling Methods for Inner Product Sketching. - Majid Daliri; Juliana Freire; Christopher Musco; Aécio S. R. Santos; Haoxiang Zhang 003 (Proc. VLDB Endow., 2024)

Sharper Bounds for Chebyshev Moment Matching, with Applications. - Cameron Musco; Christopher Musco; Lucas Rosenblatt; Apoorv Vikram Singh (COLT, 2025)

Matrix Product Sketching via Coordinated Sampling. - Majid Daliri; Juliana Freire; Danrong Li; Christopher Musco (ICLR, 2025)

Provably Accurate Shapley Value Estimation via Leverage Score Sampling. - Christopher Musco; R. Teal Witter (ICLR, 2025)

Improved Spectral Density Estimation via Explicit and Implicit Deflation. - Rajarshi Bhattacharjee; Rajesh Jayaram; Cameron Musco; Christopher Musco; Archan Ray (SODA, 2025)

Near-optimal hierarchical matrix approximation from matrix-vector products. - Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (SODA, 2025)

Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning. - Michal Derezinski; Christopher Musco; Jiaming Yang (SODA, 2025)

Algorithm-Agnostic Low-Rank Approximation of Operator Monotone Matrix Functions. - David Persson; Raphael A. Meyer; Christopher Musco (SIAM J. Matrix Anal. Appl., 2025)

Randomized block-Krylov subspace methods for low-rank approximation of matrix functions. - David Persson; Tyler Chen; Christopher Musco (CoRR, 2025)

Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search. - Yousef Al-Jazzazi; Haya Diwan; Jinrui Gou; Cameron Musco; Christopher Musco; Torsten Suel (CoRR, 2025)

The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm. - Noah Amsel; David Persson; Christopher Musco; Robert Gower (CoRR, 2025)

Quasi-optimal hierarchically semi-separable matrix approximation. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)

Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values. - R. Teal Witter; Yurong Liu; Christopher Musco (CoRR, 2025)

Efficiently Constructing Sparse Navigable Graphs. - Alexander Conway 001; Laxman Dhulipala; Martin Farach-Colton; Rob Johnson 001; Ben Landrum; Christopher Musco; Yarin Shechter; Torsten Suel; Richard Wen (CoRR, 2025)

Query Efficient Structured Matrix Learning. - Noah Amsel; Pratyush Avi; Tyler Chen; Feyza Duman Keles; Chinmay Hegde; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)