default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDCameron Musco
Person information
- affiliation: University of Massachusetts Amherst, MA, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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 - [i79]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) - [i78]Cameron Musco, Kshiteej Sheth:
Sublinear Time Low-Rank Approximation of Toeplitz Matrices. CoRR abs/2404.13757 (2024) - [i77]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) - [i76]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) - [i75]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) - [i74]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) - [i73]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) - 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]Nancy A. Lynch, Cameron Musco, Merav Parter:
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. ITCS 2017: 15:1-15:44 - [c13]Cameron Musco, Christopher Musco:
Recursive Sampling for the Nystrom Method. NIPS 2017: 3833-3845 - [c12]Cameron Musco, David P. Woodruff:
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? NIPS 2017: 4435-4445 - [c11]Michael B. Cohen, Cameron Musco, Christopher Musco:
Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling. SODA 2017: 1758-1777 - [c10]Nancy A. Lynch, Cameron Musco, Merav Parter:
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks. DISC 2017: 33:1-33:16 - [i19]Cameron Musco, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. CoRR abs/1704.03371 (2017) - [i18]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR abs/1704.04163 (2017) - [i17]Nancy A. Lynch, Cameron Musco, Merav Parter:
Neuro-RAM Unit with Applications to Similarity Testing and Compression in Spiking Neural Networks. CoRR abs/1706.01382 (2017) - [i16]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. CoRR abs/1708.07788 (2017) - [i15]Cameron Musco, David P. Woodruff:
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? CoRR abs/1711.01596 (2017) - [i14]Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis:
Minimizing Polarization and Disagreement in Social Networks. CoRR abs/1712.09948 (2017) - 2016
- [c9]Michael B. Cohen, Cameron Musco, Jakub Pachocki:
Online Row Sampling. APPROX-RANDOM 2016: 7:1-7:18 - [c8]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. ICML 2016: 2349-2357 - [c7]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML 2016: 2626-2634 - [c6]Cameron Musco, Hsin-Hao Su, Nancy A. Lynch:
Ant-Inspired Density Estimation via Random Walks: Extended Abstract. PODC 2016: 469-478 - [i13]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. CoRR abs/1602.06872 (2016) - [i12]Cameron Musco, Hsin-Hao Su, Nancy A. Lynch:
Ant-Inspired Density Estimation via Random Walks. CoRR abs/1603.02981 (2016) - [i11]Michael B. Cohen, Cameron Musco, Jakub Pachocki:
Online Row Sampling. CoRR abs/1604.05448 (2016) - [i10]Cameron Musco, Christopher Musco:
Provably Useful Kernel Matrix Approximation in Linear Time. CoRR abs/1605.07583 (2016) - [i9]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR abs/1605.08754 (2016) - [i8]Nancy A. Lynch, Cameron Musco, Merav Parter:
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. CoRR abs/1610.02084 (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]Cameron Musco, Christopher Musco:
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition. NIPS 2015: 1396-1404 - [c3]Mohsen Ghaffari, Cameron Musco, Tsvetomira Radeva, Nancy A. Lynch:
Distributed House-Hunting in Ant Colonies. PODC 2015: 57-66 - [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 - [i7]Cameron Musco, Christopher Musco:
Stronger Approximate Singular Value Decomposition via the Block Lanczos and Power Methods. CoRR abs/1504.05477 (2015) - [i6]Mohsen Ghaffari, Cameron Musco, Tsvetomira Radeva, Nancy A. Lynch:
Distributed House-Hunting in Ant Colonies. CoRR abs/1505.03799 (2015) - [i5]Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. CoRR abs/1510.08896 (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
[c65] [i79] [i77] [i75] [i74] [j5] [c57] [c56] [i72] [i71] [i68] [j4] [c50] [c49] [i62] [i61] [i57] [c39] [c36] [i51] [i47] [c34] [c33] [c28] [c27] [c26] [i44] [i40] [i37] [c22] [i33] [i32] [i29] [i26] [c21] [c19] [c18] [c17] [i25] [i24] [i23] [i22] [i20] [j1] [c15] [c13] [c11] [i16] [i14] [c8] [i13] [i10] [c5] [c4] [c2] [i7] [i4] [c1] [i3] [i2] [i1]
Charalampos E. Tsourakakis
aka: Babis Tsourakakis
aka: Babis Tsourakakis
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-06 20:26 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
