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Coresets for Classification -- Simplified and Strengthened


Jun 08, 2021
Tung Mai, Anup B. Rao, Cameron Musco


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DeepWalking Backwards: From Embeddings Back to Graphs


Feb 17, 2021
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis


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Faster Kernel Matrix Algebra via Density Estimation


Feb 16, 2021
Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner


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Faster Kernel Interpolation for Gaussian Processes


Jan 28, 2021
Mohit Yadav, Daniel Sheldon, Cameron Musco

* To appear, Artificial Intelligence and Statistics (AISTATS) 2021 

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Intervention Efficient Algorithms for Approximate Learning of Causal Graphs


Dec 27, 2020
Raghavendra Addanki, Andrew McGregor, Cameron Musco

* To appear, International Conference on Algorithmic Learning Theory(ALT) 2021 

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Estimation of Shortest Path Covariance Matrices


Nov 19, 2020
Raj Kumar Maity, Cameron Musco


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Hutch++: Optimal Stochastic Trace Estimation


Nov 12, 2020
Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff

* To appears in SIAM Symposium on Simplicity in Algorithms (SOSA21) 

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Model-specific Data Subsampling with Influence Functions


Oct 20, 2020
Anant Raj, Cameron Musco, Lester Mackey, Nicolo Fusi


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Subspace Embeddings Under Nonlinear Transformations


Oct 05, 2020
Aarshvi Gajjar, Cameron Musco


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Fourier Sparse Leverage Scores and Approximate Kernel Learning


Jun 12, 2020
Tamás Erdélyi, Cameron Musco, Christopher Musco


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Node Embeddings and Exact Low-Rank Representations of Complex Networks


Jun 10, 2020
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis


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InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity


May 29, 2020
Sudhanshu Chanpuriya, Cameron Musco

* Accepted to KDD 2020 Research Track 

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Efficient Intervention Design for Causal Discovery with Latents


May 24, 2020
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco


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Projection-Cost-Preserving Sketches: Proof Strategies and Constructions


Apr 17, 2020
Cameron Musco, Christopher Musco


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Importance Sampling via Local Sensitivity


Nov 04, 2019
Anant Raj, Cameron Musco, Lester Mackey


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Toward a Characterization of Loss Functions for Distribution Learning


Jun 06, 2019
Nika Haghtalab, Cameron Musco, Bo Waggoner


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Sample Efficient Toeplitz Covariance Estimation


Jun 06, 2019
Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco


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Learning to Prune: Speeding up Repeated Computations


Apr 26, 2019
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik


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Winner-Take-All Computation in Spiking Neural Networks


Apr 25, 2019
Nancy Lynch, Cameron Musco, Merav Parter


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Low-Rank Approximation from Communication Complexity


Apr 22, 2019
Cameron Musco, Christopher Musco, David P. Woodruff


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A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms


Dec 20, 2018
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh


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A Basic Compositional Model for Spiking Neural Networks


Aug 12, 2018
Nancy Lynch, Cameron Musco


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Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees


May 21, 2018
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

* An extended abstract of this work appears in the Proceedings of the 34th International Conference on Machine Learning (ICML 2017) 

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Learning Networks from Random Walk-Based Node Similarities


Jan 23, 2018
Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis


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Minimizing Polarization and Disagreement in Social Networks


Dec 28, 2017
Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis

* 19 pages (accepted, WWW 2018) 

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Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness


Nov 20, 2017
Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff

* To appear, ITCS 2018 

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