Musco, Cameron

34 publications

COLT 2025 Sharper Bounds for Chebyshev Moment Matching, with Applications Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh

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

NeurIPS 2024 Gaussian Process Bandits for Top-K Recommendations Mohit Yadav, Daniel Sheldon, Cameron 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 Nearly Optimal Approximation of Matrix Functions by the Lanczos Method Noah Amsel, Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco

NeurIPS 2023 Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings Sudhanshu Chanpuriya, Ryan Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, 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

AISTATS 2023 Optimal Sketching Bounds for Sparse Linear Regression Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David Woodruff

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 Sample Constrained Treatment Effect Estimation Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao

NeurIPS 2022 Simplified Graph Convolution with Heterophily Sudhanshu Chanpuriya, Cameron Musco

AAAI 2022 Sublinear Time Approximation of Text Similarity Matrices Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco

AISTATS 2021 Faster Kernel Interpolation for Gaussian Processes Mohit Yadav, Daniel Sheldon, Cameron Musco

NeurIPS 2021 Coresets for Classification – Simplified and Strengthened Tung Mai, Cameron Musco, Anup Rao

ICML 2021 DeepWalking Backwards: From Embeddings Back to Graphs Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

ICML 2021 Faster Kernel Matrix Algebra via Density Estimation Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner

ALT 2021 Intervention Efficient Algorithms for Approximate Learning of Causal Graphs Raghavendra Addanki, Andrew McGregor, Cameron Musco

NeurIPS 2021 On the Power of Edge Independent Graph Models Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

ALT 2021 Subspace Embeddings Under Nonlinear Transformations Aarshvi Gajjar, Cameron Musco

ICML 2020 Efficient Intervention Design for Causal Discovery with Latents Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco

NeurIPS 2020 Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamas Erdelyi, Cameron Musco, Christopher Musco

AISTATS 2020 Importance Sampling via Local Sensitivity Anant Raj, Cameron Musco, Lester Mackey

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

COLT 2019 Learning to Prune: Speeding up Repeated Computations Daniel Alabi, Adam Tauman Kalai, Katrina Liggett, Cameron Musco, Christos Tzamos, Ellen Vitercik

NeurIPS 2019 Toward a Characterization of Loss Functions for Distribution Learning Nika Haghtalab, Cameron Musco, Bo Waggoner

NeurIPS 2018 Inferring Networks from Random Walk-Based Node Similarities Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis

NeurIPS 2017 Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? Cameron Musco, David Woodruff

ICML 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

NeurIPS 2017 Recursive Sampling for the Nystrom Method Cameron Musco, Christopher Musco

ICML 2016 Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

ICML 2016 Principal Component Projection Without Principal Component Analysis Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford

NeurIPS 2015 Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco