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

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

Distributed House-Hunting in Ant Colonies. - Mohsen Ghaffari 001; Cameron Musco; Tsvetomira Radeva; Nancy A. Lynch (PODC, 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)

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. - Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (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)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning. - Dan Garber; Elad Hazan; Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Ant-Inspired Density Estimation via Random Walks: Extended Abstract. - Cameron Musco; Hsin-Hao Su; Nancy A. Lynch (PODC, 2016)

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

Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. - Cameron Musco; David P. Woodruff (FOCS, 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)

Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. - Nancy A. Lynch; Cameron Musco; Merav Parter (ITCS, 2017)

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

Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? - Cameron Musco; David P. Woodruff (NIPS, 2017)

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

Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks. - Nancy A. Lynch; Cameron Musco; Merav Parter (DISC, 2017)

Ant-inspired density estimation via random walks. - Cameron Musco; Hsin-Hao Su; Nancy A. Lynch (Proc. Natl. Acad. Sci. USA, 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)

Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. - Cameron Musco; Praneeth Netrapalli; Aaron Sidford; Shashanka Ubaru; David P. Woodruff (ITCS, 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)

The power of randomized algorithms: from numerical linear algebra to biological systems. - Cameron Musco (2018)

Learning to Prune: Speeding up Repeated Computations. - Daniel Alabi; Adam Tauman Kalai; Katrina Ligett; Cameron Musco; Christos Tzamos; Ellen Vitercik (COLT, 2019)

Toward a Characterization of Loss Functions for Distribution Learning. - Nika Haghtalab; Cameron Musco; Bo Waggoner (NeurIPS, 2019)

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)

Winner-Take-All Computation in Spiking Neural Networks. - Nancy A. Lynch; Cameron Musco; Merav Parter (CoRR, 2019)

Importance Sampling via Local Sensitivity. - Anant Raj; Cameron Musco; Lester Mackey (AISTATS, 2020)

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)

Efficient Intervention Design for Causal Discovery with Latents. - Raghavendra Addanki; Shiva Prasad Kasiviswanathan; Andrew McGregor 001; Cameron Musco (ICML, 2020)

Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks. - Yael Hitron; Nancy A. Lynch; Cameron Musco; Merav Parter (ITCS, 2020)

InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity. - Sudhanshu Chanpuriya; Cameron Musco (KDD, 2020)

Node Embeddings and Exact Low-Rank Representations of Complex Networks. - Sudhanshu Chanpuriya; Cameron Musco; Konstantinos Sotiropoulos; Charalampos E. Tsourakakis (NeurIPS, 2020)

Fourier Sparse Leverage Scores and Approximate Kernel Learning. - Tamás Erdélyi; Cameron Musco; 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)

Spiking Neural Networks Through the Lens of Streaming Algorithms. - Yael Hitron; Cameron Musco; Merav Parter (DISC, 2020)

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

Model-specific Data Subsampling with Influence Functions. - Anant Raj; Cameron Musco; Lester Mackey; Nicoló Fusi (CoRR, 2020)

Estimation of Shortest Path Covariance Matrices. - Raj Kumar Maity; Cameron Musco (CoRR, 2020)

Online Row Sampling. - Michael B. Cohen; Cameron Musco; Jakub Pachocki (Theory Comput., 2020)

Faster Kernel Interpolation for Gaussian Processes. - Mohit Yadav; Daniel Sheldon; Cameron Musco (AISTATS, 2021)

Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. - Raghavendra Addanki; Andrew McGregor 001; Cameron Musco (ALT, 2021)

Subspace Embeddings under Nonlinear Transformations. - Aarshvi Gajjar; Cameron Musco (ALT, 2021)

Faster Kernel Matrix Algebra via Density Estimation. - Arturs Backurs; Piotr Indyk; Cameron Musco; Tal Wagner (ICML, 2021)

DeepWalking Backwards: From Embeddings Back to Graphs. - Sudhanshu Chanpuriya; Cameron Musco; Konstantinos Sotiropoulos; Charalampos E. Tsourakakis (ICML, 2021)

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

On the Power of Edge Independent Graph Models. - Sudhanshu Chanpuriya; Cameron Musco; Konstantinos Sotiropoulos; Charalampos E. Tsourakakis (NeurIPS, 2021)

Coresets for Classification - Simplified and Strengthened. - Tung Mai; Cameron Musco; Anup Rao 002 (NeurIPS, 2021)

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

Sublinear Time Eigenvalue Approximation via Random Sampling. - Rajarshi Bhattacharjee; Cameron Musco; Archan Ray (CoRR, 2021)

An Interpretable Graph Generative Model with Heterophily. - Sudhanshu Chanpuriya; Ryan A. Rossi; Anup B. Rao; Tung Mai; Nedim Lipka; Zhao Song 002; Cameron Musco (CoRR, 2021)

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

Sublinear Time Approximation of Text Similarity Matrices. - Archan Ray; Nicholas Monath; Andrew McCallum; Cameron Musco (AAAI, 2022)

A Basic Compositional Model for Spiking Neural Networks. - Nancy A. Lynch; Cameron Musco (A Journey from Process Algebra via Timed Automata to Model Learning, 2022)

Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries. - Raghavendra Addanki; Andrew McGregor 001; Cameron Musco (ESA, 2022)

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

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

Sample Constrained Treatment Effect Estimation. - Raghavendra Addanki; David Arbour; Tung Mai; Cameron Musco; Anup Rao 002 (NeurIPS, 2022)

Simplified Graph Convolution with Heterophily. - Sudhanshu Chanpuriya; Cameron Musco (NeurIPS, 2022)

Kernel Interpolation with Sparse Grids. - Mohit Yadav; Daniel R. Sheldon; Cameron Musco (NeurIPS, 2022)

Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings. - Dongxu Zhang; Michael Boratko; Cameron Musco; Andrew McCallum (NeurIPS, 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)

Optimal Sketching Bounds for Sparse Linear Regression. - Tung Mai; Alexander Munteanu; Cameron Musco; Anup Rao 002; Chris Schwiegelshohn; David P. Woodruff (AISTATS, 2023)

Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs. - Sudhanshu Chanpuriya; Ryan A. Rossi; Sungchul Kim; Tong Yu 001; Jane Hoffswell; Nedim Lipka; Shunan Guo; Cameron Musco (ICLR, 2023)

Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings. - Sudhanshu Chanpuriya; Ryan A. Rossi; Anup B. Rao; Tung Mai; Nedim Lipka; Zhao Song 002; Cameron Musco (NeurIPS, 2023)

Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation. - Mehrdad Ghadiri; David Arbour; Tung Mai; Cameron Musco; Anup B. Rao (NeurIPS, 2023)

No-regret Algorithms for Fair Resource Allocation. - Abhishek Sinha; Ativ Joshi; Rajarshi Bhattacharjee; Cameron Musco; Mohammad Hajiesmaili (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)

Toeplitz Low-Rank Approximation with Sublinear Query Complexity. - Michael Kapralov; Hannah Lawrence; Mikhail Makarov; Cameron Musco; Kshiteej Sheth (SODA, 2023)

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

Local Edge Dynamics and Opinion Polarization. - Nikita Bhalla; Adam Lechowicz; Cameron Musco (WSDM, 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)

Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More. - Sudhanshu Chanpuriya; Cameron 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)

On the Role of Edge Dependency in Graph Generative Models. - Sudhanshu Chanpuriya; Cameron Musco; Konstantinos Sotiropoulos; Charalampos E. Tsourakakis (ICML, 2024)

Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. - Rajarshi Bhattacharjee; Gregory Dexter; Cameron Musco; Archan Ray; Sushant Sachdeva; David P. Woodruff (ITCS, 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)

Efficient and Private Marginal Reconstruction with Local Non-Negativity. - Brett Mullins; Miguel Fuentes; Yingtai Xiao; Daniel Kifer; Cameron Musco; Daniel R. Sheldon (NeurIPS, 2024)

Gaussian Process Bandits for Top-k Recommendations. - Mohit Yadav; Cameron Musco; Daniel R. Sheldon (NeurIPS, 2024)

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

Sublinear Time Low-Rank Approximation of Toeplitz Matrices. - Cameron Musco; Kshiteej Sheth (SODA, 2024)

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

Competitive Algorithms for Online Knapsack with Succinct Predictions. - Mohammadreza Daneshvaramoli; Helia Karisani; Adam Lechowicz; Bo Sun 004; Cameron Musco; Mohammad Hajiesmaili (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)

Sublinear Time Eigenvalue Approximation via Random Sampling. - Rajarshi Bhattacharjee; Gregory Dexter; Petros Drineas; Cameron Musco; Archan Ray (Algorithmica, 2024)

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

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)

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