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2020 – today
- 2023
[j4]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)- [c40]Aarshvi Gajjar, Christopher Musco, Chinmay Hegde:
Active Learning for Single Neuron Models with Lipschitz Non-Linearities. AISTATS 2023: 4101-4113 - [c39]Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Moments, Random Walks, and Limits for Spectrum Approximation. COLT 2023: 5373-5394 - [c38]Chuhan Yang, Christopher Musco:
Efficient Block Approximate Matrix Multiplication. ESA 2023: 103:1-103:15 - [c37]Xinyu Luo, Christopher Musco, Cas Widdershoven:
Dimensionality Reduction for General KDE Mode Finding. ICML 2023: 23067-23082 - [c36]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 - [c35]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Near-Linear Sample Complexity for Lp Polynomial Regression. SODA 2023: 3959-4025 - [c34]Prathamesh Dharangutte, Christopher Musco:
A Tight Analysis of Hutchinson's Diagonal Estimator. SOSA 2023: 353-364 - [i48]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) - [i47]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) - [i46]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) - [i45]Xinyu Luo, Christopher Musco, Cas Widdershoven:
Dimensionality Reduction for General KDE Mode Finding. CoRR abs/2305.18755 (2023) - [i44]Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Moments, Random Walks, and Limits for Spectrum Approximation. CoRR abs/2307.00474 (2023) - [i43]Majid Daliri, Juliana Freire, Christopher Musco, Aécio S. R. Santos, Haoxiang Zhang:
Simple Analysis of Priority Sampling. CoRR abs/2308.05907 (2023) - [i42]Majid Daliri, Juliana Freire, Christopher Musco, Aécio S. R. Santos, Haoxiang Zhang:
Sampling Methods for Inner Product Sketching. CoRR abs/2309.16157 (2023) - [i41]Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare:
Improved Active Learning via Dependent Leverage Score Sampling. CoRR abs/2310.04966 (2023) - [i40]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. CoRR abs/2310.18265 (2023) - [i39]David Persson, Raphael A. Meyer, Christopher Musco:
Algorithm-agnostic low-rank approximation of operator monotone matrix functions. CoRR abs/2311.14023 (2023) - 2022
- [j3]Mengxi Wu, Yi-Jen Chiang, Christopher Musco:
Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data. Comput. Graph. Forum 41(3): 309-320 (2022) - [j2]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) - [c33]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Linear Regression for ℓp Norms and Beyond. FOCS 2022: 744-753 - [c32]Aécio S. R. Santos, Aline Bessa, Christopher Musco, Juliana Freire:
A Sketch-based Index for Correlated Dataset Search. ICDE 2022: 2928-2941 - [c31]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. ICLR 2022 - [c30]Vladimir Braverman, Aditya Krishnan, Christopher Musco:
Sublinear time spectral density estimation. STOC 2022: 1144-1157 - [i38]Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Low-memory Krylov subspace methods for optimal rational matrix function approximation. CoRR abs/2202.11251 (2022) - [i37]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. CoRR abs/2203.07557 (2022) - [i36]Prathamesh Dharangutte, Christopher Musco:
A Tight Analysis of Hutchinson's Diagonal Estimator. CoRR abs/2208.03268 (2022) - [i35]Aarshvi Gajjar, Chinmay Hegde, Christopher Musco:
Active Learning for Single Neuron Models with Lipschitz Non-Linearities. CoRR abs/2210.13601 (2022) - [i34]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) - 2021
- [c29]Prathamesh Dharangutte, Christopher Musco:
Graph Learning for Inverse Landscape Genetics. AAAI 2021: 14739-14747 - [c28]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding an Approximate Mode of a Kernel Density Estimate. ESA 2021: 61:1-61:19 - [c27]Cameron Musco, Christopher Musco, David P. Woodruff:
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation. ITCS 2021: 6:1-6:20 - [c26]Prathamesh Dharangutte, Christopher Musco:
Dynamic Trace Estimation. NeurIPS 2021: 30088-30099 - [c25]Aécio S. R. Santos, Aline Bessa, Fernando Chirigati, Christopher Musco, Juliana Freire:
Correlation Sketches for Approximate Join-Correlation Queries. SIGMOD Conference 2021: 1531-1544 - [c24]Sheng Wang, Yuan Sun, Christopher Musco, Zhifeng Bao:
Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand. SIGMOD Conference 2021: 1906-1919 - [c23]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. SOSA 2021: 142-155 - [i33]Sheng Wang, Yuan Sun, Christopher Musco, Zhifeng Bao:
Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand. CoRR abs/2103.16084 (2021) - [i32]Aécio S. R. Santos, Aline Bessa, Fernando Chirigati, Christopher Musco, Juliana Freire:
Correlation Sketches for Approximate Join-Correlation Queries. CoRR abs/2104.03353 (2021) - [i31]Vladimir Braverman, Aditya Krishnan, Christopher Musco:
Linear and Sublinear Time Spectral Density Estimation. CoRR abs/2104.03461 (2021) - [i30]Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco:
Error bounds for Lanczos-based matrix function approximation. CoRR abs/2106.09806 (2021) - [i29]Christopher Musco, Indu Ramesh, Johan Ugander, R. Teal Witter:
How to Quantify Polarization in Models of Opinion Dynamics. CoRR abs/2110.11981 (2021) - [i28]Prathamesh Dharangutte, Christopher Musco:
Dynamic Trace Estimation. CoRR abs/2110.13752 (2021) - [i27]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Sampling for Linear Regression Beyond the $\ell_2$ Norm. CoRR abs/2111.04888 (2021) - 2020
- [c22]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 - [c21]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. ICASSP 2020: 4796-4800 - [c20]Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. NeurIPS 2020 - [c19]Raphael A. Meyer, Christopher Musco:
The Statistical Cost of Robust Kernel Hyperparameter Turning. NeurIPS 2020 - [c18]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. SODA 2020: 378-397 - [c17]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 - [c16]Uthsav Chitra, Christopher Musco:
Analyzing the Impact of Filter Bubbles on Social Network Polarization. WSDM 2020: 115-123 - [i26]Cameron Musco, Christopher Musco:
Projection-Cost-Preserving Sketches: Proof Strategies and Constructions. CoRR abs/2004.08434 (2020) - [i25]Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. CoRR abs/2006.07340 (2020) - [i24]Raphael A. Meyer, Christopher Musco:
The Statistical Cost of Robust Kernel Hyperparameter Tuning. CoRR abs/2006.08035 (2020) - [i23]Prathamesh Dharangutte, Christopher Musco:
Graph Learning for Inverse Landscape Genetics. CoRR abs/2006.12334 (2020) - [i22]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. CoRR abs/2010.09649 (2020)
2010 – 2019
- 2019
- [c15]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 - [i21]Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri:
Faster Spectral Sparsification in Dynamic Streams. CoRR abs/1903.12165 (2019) - [i20]Cameron Musco, Christopher Musco, David P. Woodruff:
Low-Rank Approximation from Communication Complexity. CoRR abs/1904.09841 (2019) - [i19]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. CoRR abs/1905.05643 (2019) - [i18]Uthsav Chitra, Christopher Musco:
Understanding Filter Bubbles and Polarization in Social Networks. CoRR abs/1906.08772 (2019) - [i17]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. CoRR abs/1911.08015 (2019) - [i16]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding the Mode of a Kernel Density Estimate. CoRR abs/1912.07673 (2019) - 2018
- [b1]Christopher Musco:
Faster linear algebra for data analysis and machine learning. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [c14]Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco:
Eigenvector Computation and Community Detection in Asynchronous Gossip Models. ICALP 2018: 159:1-159:14 - [c13]Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis:
Inferring Networks From Random Walk-Based Node Similarities. NeurIPS 2018: 3708-3719 - [c12]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. SODA 2018: 1605-1624 - [c11]Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Minimizing Polarization and Disagreement in Social Networks. WWW 2018: 369-378 - [i15]Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Learning Networks from Random Walk-Based Node Similarities. CoRR abs/1801.07386 (2018) - [i14]Frederik Mallmann-Trenn, Cameron Musco, Christopher Musco:
Eigenvector Computation and Community Detection in Asynchronous Gossip Models. CoRR abs/1804.08548 (2018) - [i13]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) - [i12]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) - [i11]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
- [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) - [c10]Christopher Musco, Maxim Sviridenko, Justin Thaler:
Determining Tournament Payout Structures for Daily Fantasy Sports. ALENEX 2017: 172-184 - [c9]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 - [c8]Cameron Musco, Christopher Musco:
Recursive Sampling for the Nystrom Method. NIPS 2017: 3833-3845 - [c7]Michael B. Cohen, Cameron Musco, Christopher Musco:
Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling. SODA 2017: 1758-1777 - [i10]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. CoRR abs/1708.07788 (2017) - [i9]Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Minimizing Polarization and Disagreement in Social Networks. CoRR abs/1712.09948 (2017) - 2016
- [c6]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. ICML 2016: 2349-2357 - [i8]Christopher Musco, Maxim Sviridenko, Justin Thaler:
Determining Tournament Payout Structures for Daily Fantasy Sports. CoRR abs/1601.04203 (2016) - [i7]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. CoRR abs/1602.06872 (2016) - [i6]Cameron Musco, Christopher Musco:
Provably Useful Kernel Matrix Approximation in Linear Time. CoRR abs/1605.07583 (2016) - 2015
- [c5]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. ITCS 2015: 181-190 - [c4]Brendan Juba, Christopher Musco, Fan Long, Stelios Sidiroglou-Douskos, Martin C. Rinard:
Principled Sampling for Anomaly Detection. NDSS 2015 - [c3]Cameron Musco, Christopher Musco:
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition. NIPS 2015: 1396-1404 - [c2]Michael B. Cohen, Sam Elder, Cameron Musco, Christopher Musco, Madalina Persu:
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation. STOC 2015: 163-172 - [i5]Cameron Musco, Christopher Musco:
Stronger Approximate Singular Value Decomposition via the Block Lanczos and Power Methods. CoRR abs/1504.05477 (2015) - [i4]Michael B. Cohen, Cameron Musco, Christopher Musco:
Ridge Leverage Scores for Low-Rank Approximation. CoRR abs/1511.07263 (2015) - 2014
- [c1]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. FOCS 2014: 561-570 - [i3]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. CoRR abs/1407.1289 (2014) - [i2]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. CoRR abs/1408.5099 (2014) - [i1]Michael B. Cohen, Sam Elder, Cameron Musco, Christopher Musco, Madalina Persu:
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation. CoRR abs/1410.6801 (2014)
Coauthor Index
[j4] [c36] [c35] [i48] [i47] [i46] [j2] [c33] [c31] [i38] [i37] [i34] [c27] [c23] [i30] [i27] [c22] [c21] [c20] [c18] [c17] [i26] [i25] [i22] [c15] [i21] [i20] [i19] [i17] [c14] [c13] [c12] [c11] [i15] [i14] [i13] [i12] [i11] [j1] [c9] [c8] [c7] [i10] [i9] [c6] [i7] [i6] [c5] [c3] [c2] [i5] [i4] [c1] [i3] [i2] [i1]
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last updated on 2023-12-01 18:38 CET by the dblp team
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