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ORCIDCameron Musco
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- affiliation: University of Massachusetts Amherst, MA, USA
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2020 – today
- 2024
[j6]Rajarshi Bhattacharjee, Gregory Dexter, Petros Drineas, Cameron Musco, Archan Ray:
Sublinear Time Eigenvalue Approximation via Random Sampling. Algorithmica 86(6): 1764-1829 (2024)- [c67]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
On the Role of Edge Dependency in Graph Generative Models. ICML 2024 - [c66]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. ITCS 2024: 13:1-13:24 - [c65]Raphael A. Meyer, Cameron Musco, Christopher Musco:
On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation. SODA 2024: 811-845 - [c64]Cameron Musco, Kshiteej Sheth:
Sublinear Time Low-Rank Approximation of Toeplitz Matrices. SODA 2024: 5084-5117 - [i80]Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco:
Fixed-sparsity matrix approximation from matrix-vector products. CoRR abs/2402.09379 (2024) - [i79]Cameron Musco, Kshiteej Sheth:
Sublinear Time Low-Rank Approximation of Toeplitz Matrices. CoRR abs/2404.13757 (2024) - [i78]Haya Diwan, Jinrui Gou, Cameron Musco, Christopher Musco, Torsten Suel:
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits. CoRR abs/2405.18680 (2024) - [i77]Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz, Bo Sun, Cameron Musco, Mohammad Hajiesmaili:
Competitive Algorithms for Online Knapsack with Succinct Predictions. CoRR abs/2406.18752 (2024) - [i76]Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson:
Near-optimal hierarchical matrix approximation from matrix-vector products. CoRR abs/2407.04686 (2024) - [i75]Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh:
Sharper Bounds for Chebyshev Moment Matching with Applications to Differential Privacy and Beyond. CoRR abs/2408.12385 (2024) - [i74]Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel Sheldon:
Efficient and Private Marginal Reconstruction with Local Non-Negativity. CoRR abs/2410.01091 (2024) - [i73]Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco, Christopher Musco, Archan Ray:
Improved Spectral Density Estimation via Explicit and Implicit Deflation. CoRR abs/2410.21690 (2024) - 2023
- [j5]Tyler Chen
, Anne Greenbaum, Cameron Musco, Christopher Musco:
Low-Memory Krylov Subspace Methods for Optimal Rational Matrix Function Approximation. SIAM J. Matrix Anal. Appl. 44(2): 670-692 (2023) - [c63]Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316 - [c62]Rajarshi Bhattacharjee, Gregory Dexter, Petros Drineas, Cameron Musco, Archan Ray:
Sublinear Time Eigenvalue Approximation via Random Sampling. ICALP 2023: 21:1-21:18 - [c61]Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, Cameron Musco:
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs. ICLR 2023 - [c60]Sudhanshu Chanpuriya, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco:
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings. NeurIPS 2023 - [c59]Mehrdad Ghadiri, David Arbour, Tung Mai, Cameron Musco, Anup B. Rao:
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation. NeurIPS 2023 - [c58]Abhishek Sinha, Ativ Joshi, Rajarshi Bhattacharjee, Cameron Musco, Mohammad Hajiesmaili:
No-regret Algorithms for Fair Resource Allocation. NeurIPS 2023 - [c57]Aline Bessa, Majid Daliri, Juliana Freire, Cameron Musco, Christopher Musco, Aécio S. R. Santos, Haoxiang Zhang:
Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation. PODS 2023: 169-181 - [c56]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Near-Linear Sample Complexity for Lp Polynomial Regression. SODA 2023: 3959-4025 - [c55]Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco, Kshiteej Sheth:
Toeplitz Low-Rank Approximation with Sublinear Query Complexity. SODA 2023: 4127-4158 - [c54]Nikita Bhalla, Adam Lechowicz, Cameron Musco:
Local Edge Dynamics and Opinion Polarization. WSDM 2023: 6-14 - [i72]Aline Bessa, Majid Daliri, Juliana Freire, Cameron Musco, Christopher Musco, Aécio S. R. Santos, Haoxiang Zhang:
Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation. CoRR abs/2301.05811 (2023) - [i71]Noah Amsel, Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Near-Optimality Guarantees for Approximating Rational Matrix Functions by the Lanczos Method. CoRR abs/2303.03358 (2023) - [i70]Abhishek Sinha, Ativ Joshi, Rajarshi Bhattacharjee, Cameron Musco, Mohammad H. Hajiesmaili:
No-regret Algorithms for Fair Resource Allocation. CoRR abs/2303.06396 (2023) - [i69]Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. CoRR abs/2304.02261 (2023) - [i68]Raphael A. Meyer, Cameron Musco, Christopher Musco:
On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation. CoRR abs/2305.02535 (2023) - [i67]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. CoRR abs/2305.05826 (2023) - [i66]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Kernel Interpolation with Sparse Grids. CoRR abs/2305.14451 (2023) - [i65]Sudhanshu Chanpuriya, Cameron Musco:
Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More. CoRR abs/2308.06448 (2023) - [i64]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
On the Role of Edge Dependency in Graph Generative Models. CoRR abs/2312.03691 (2023) - 2022
- [j4]Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Error Bounds for Lanczos-Based Matrix Function Approximation. SIAM J. Matrix Anal. Appl. 43(2): 787-811 (2022) - [c53]Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco:
Sublinear Time Approximation of Text Similarity Matrices. AAAI 2022: 8072-8080 - [c52]Nancy A. Lynch, Cameron Musco:
A Basic Compositional Model for Spiking Neural Networks. A Journey from Process Algebra via Timed Automata to Model Learning 2022: 403-449 - [c51]Raghavendra Addanki, Andrew McGregor, Cameron Musco:
Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries. ESA 2022: 2:1-2:16 - [c50]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Linear Regression for ℓp Norms and Beyond. FOCS 2022: 744-753 - [c49]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. ICLR 2022 - [c48]Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. NeurIPS 2022 - [c47]Sudhanshu Chanpuriya, Cameron Musco:
Simplified Graph Convolution with Heterophily. NeurIPS 2022 - [c46]Mohit Yadav, Daniel R. Sheldon, Cameron Musco:
Kernel Interpolation with Sparse Grids. NeurIPS 2022 - [c45]Dongxu Zhang, Michael Boratko, Cameron Musco, Andrew McCallum:
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings. NeurIPS 2022 - [i63]Sudhanshu Chanpuriya, Cameron Musco:
Simplified Graph Convolution with Heterophily. CoRR abs/2202.04139 (2022) - [i62]Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Low-memory Krylov subspace methods for optimal rational matrix function approximation. CoRR abs/2202.11251 (2022) - [i61]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. CoRR abs/2203.07557 (2022) - [i60]Raghavendra Addanki, Andrew McGregor, Cameron Musco:
Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries. CoRR abs/2207.02817 (2022) - [i59]Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, Cameron Musco:
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs. CoRR abs/2210.00032 (2022) - [i58]Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. CoRR abs/2210.06594 (2022) - [i57]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Near-Linear Sample Complexity for Lp Polynomial Regression. CoRR abs/2211.06790 (2022) - [i56]Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco, Kshiteej Sheth:
Toeplitz Low-Rank Approximation with Sublinear Query Complexity. CoRR abs/2211.11328 (2022) - 2021
- [c44]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Faster Kernel Interpolation for Gaussian Processes. AISTATS 2021: 2971-2979 - [c43]Raghavendra Addanki, Andrew McGregor, Cameron Musco:
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. ALT 2021: 151-184 - [c42]Aarshvi Gajjar, Cameron Musco:
Subspace Embeddings under Nonlinear Transformations. ALT 2021: 656-672 - [c41]Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner:
Faster Kernel Matrix Algebra via Density Estimation. ICML 2021: 500-510 - [c40]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
DeepWalking Backwards: From Embeddings Back to Graphs. ICML 2021: 1473-1483 - [c39]Cameron Musco, Christopher Musco, David P. Woodruff:
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation. ITCS 2021: 6:1-6:20 - [c38]Tung Mai, Cameron Musco, Anup Rao:
Coresets for Classification - Simplified and Strengthened. NeurIPS 2021: 11643-11654 - [c37]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
On the Power of Edge Independent Graph Models. NeurIPS 2021: 24418-24429 - [c36]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. SOSA 2021: 142-155 - [i55]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Faster Kernel Interpolation for Gaussian Processes. CoRR abs/2101.11751 (2021) - [i54]Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner:
Faster Kernel Matrix Algebra via Density Estimation. CoRR abs/2102.08341 (2021) - [i53]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
DeepWalking Backwards: From Embeddings Back to Graphs. CoRR abs/2102.08532 (2021) - [i52]Tung Mai, Anup B. Rao, Cameron Musco:
Coresets for Classification - Simplified and Strengthened. CoRR abs/2106.04254 (2021) - [i51]Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Error bounds for Lanczos-based matrix function approximation. CoRR abs/2106.09806 (2021) - [i50]Rajarshi Bhattacharjee, Cameron Musco, Archan Ray:
Sublinear Time Eigenvalue Approximation via Random Sampling. CoRR abs/2109.07647 (2021) - [i49]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
On the Power of Edge Independent Graph Models. CoRR abs/2111.00048 (2021) - [i48]Sudhanshu Chanpuriya, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco:
An Interpretable Graph Generative Model with Heterophily. CoRR abs/2111.03030 (2021) - [i47]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Sampling for Linear Regression Beyond the $\ell_2$ Norm. CoRR abs/2111.04888 (2021) - [i46]Nikita Bhalla, Adam Lechowicz, Cameron Musco:
Local Edge Dynamics and Opinion Polarization. CoRR abs/2111.14020 (2021) - [i45]Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco:
Sublinear Time Approximation of Text Similarity Matrices. CoRR abs/2112.09631 (2021) - 2020
- [j3]Michael B. Cohen, Cameron Musco, Jakub Pachocki:
Online Row Sampling. Theory Comput. 16: 1-25 (2020) - [c35]Anant Raj, Cameron Musco, Lester Mackey:
Importance Sampling via Local Sensitivity. AISTATS 2020: 3099-3109 - [c34]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. FOCS 2020: 517-528 - [c33]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. ICASSP 2020: 4796-4800 - [c32]Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco:
Efficient Intervention Design for Causal Discovery with Latents. ICML 2020: 63-73 - [c31]Yael Hitron, Nancy A. Lynch, Cameron Musco, Merav Parter:
Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks. ITCS 2020: 23:1-23:31 - [c30]Sudhanshu Chanpuriya, Cameron Musco:
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity. KDD 2020: 1325-1333 - [c29]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
Node Embeddings and Exact Low-Rank Representations of Complex Networks. NeurIPS 2020 - [c28]Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. NeurIPS 2020 - [c27]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. SODA 2020: 378-397 - [c26]Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, Aaron Sidford, Jakab Tardos:
Fast and Space Efficient Spectral Sparsification in Dynamic Streams. SODA 2020: 1814-1833 - [c25]Yael Hitron, Cameron Musco, Merav Parter:
Spiking Neural Networks Through the Lens of Streaming Algorithms. DISC 2020: 10:1-10:18 - [i44]Cameron Musco, Christopher Musco:
Projection-Cost-Preserving Sketches: Proof Strategies and Constructions. CoRR abs/2004.08434 (2020) - [i43]Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco:
Efficient Intervention Design for Causal Discovery with Latents. CoRR abs/2005.11736 (2020) - [i42]Sudhanshu Chanpuriya, Cameron Musco:
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity. CoRR abs/2006.00094 (2020) - [i41]Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
Node Embeddings and Exact Low-Rank Representations of Complex Networks. CoRR abs/2006.05592 (2020) - [i40]Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. CoRR abs/2006.07340 (2020) - [i39]Yael Hitron, Cameron Musco, Merav Parter:
Spiking Neural Networks Through the Lens of Streaming Algorithms. CoRR abs/2010.01423 (2020) - [i38]Aarshvi Gajjar, Cameron Musco:
Subspace Embeddings Under Nonlinear Transformations. CoRR abs/2010.02264 (2020) - [i37]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. CoRR abs/2010.09649 (2020) - [i36]Anant Raj, Cameron Musco, Lester Mackey, Nicoló Fusi:
Model-specific Data Subsampling with Influence Functions. CoRR abs/2010.10218 (2020) - [i35]Raj Kumar Maity, Cameron Musco:
Estimation of Shortest Path Covariance Matrices. CoRR abs/2011.09986 (2020) - [i34]Raghavendra Addanki, Andrew McGregor, Cameron Musco:
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. CoRR abs/2012.13976 (2020)
2010 – 2019
- 2019
- [c24]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. COLT 2019: 30-33 - [c23]Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. NeurIPS 2019: 7235-7244 - [c22]Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
A universal sampling method for reconstructing signals with simple Fourier transforms. STOC 2019: 1051-1063 - [i33]Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri:
Faster Spectral Sparsification in Dynamic Streams. CoRR abs/1903.12165 (2019) - [i32]Cameron Musco, Christopher Musco, David P. Woodruff:
Low-Rank Approximation from Communication Complexity. CoRR abs/1904.09841 (2019) - [i31]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. CoRR abs/1904.11875 (2019) - [i30]Nancy A. Lynch, Cameron Musco, Merav Parter:
Winner-Take-All Computation in Spiking Neural Networks. CoRR abs/1904.12591 (2019) - [i29]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. CoRR abs/1905.05643 (2019) - [i28]Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. CoRR abs/1906.02652 (2019) - [i27]Anant Raj, Cameron Musco, Lester Mackey:
Importance Sampling via Local Sensitivity. CoRR abs/1911.01575 (2019) - [i26]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. CoRR abs/1911.08015 (2019) - 2018
- [b1]Cameron Musco:
The power of randomized algorithms: from numerical linear algebra to biological systems. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [c21]Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco:
Eigenvector Computation and Community Detection in Asynchronous Gossip Models. ICALP 2018: 159:1-159:14 - [c20]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS 2018: 8:1-8:21 - [c19]Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis:
Inferring Networks From Random Walk-Based Node Similarities. NeurIPS 2018: 3708-3719 - [c18]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. SODA 2018: 1605-1624 - [c17]Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Minimizing Polarization and Disagreement in Social Networks. WWW 2018: 369-378 - [i25]Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Learning Networks from Random Walk-Based Node Similarities. CoRR abs/1801.07386 (2018) - [i24]Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco:
Eigenvector Computation and Community Detection in Asynchronous Gossip Models. CoRR abs/1804.08548 (2018) - [i23]Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. CoRR abs/1804.09893 (2018) - [i22]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. CoRR abs/1805.03765 (2018) - [i21]Nancy A. Lynch, Cameron Musco:
A Basic Compositional Model for Spiking Neural Networks. CoRR abs/1808.03884 (2018) - [i20]Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms. CoRR abs/1812.08723 (2018) - 2017
- [j2]Cameron Musco, Hsin-Hao Su, Nancy A. Lynch:
Ant-inspired density estimation via random walks. Proc. Natl. Acad. Sci. USA 114(40): 10534-10541 (2017) - [j1]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. SIAM J. Comput. 46(1): 456-477 (2017) - [c16]Cameron Musco, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. FOCS 2017: 672-683 - [c15]Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. ICML 2017: 253-262 - [c14]