Musco, Christopher

28 publications

ICLR 2026 PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression Fabian Fumagalli, R. Teal Witter, Christopher Musco

ICLR 2026 The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm Noah Amsel, David Persson, Christopher Musco, Robert M. Gower

NeurIPS 2025 Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search Yousef Al-Jazzazi, Haya Diwan, Jinrui Gou, Cameron N Musco, Christopher Musco, Torsten Suel

ICLR 2025 Matrix Product Sketching via Coordinated Sampling Majid Daliri, Juliana Freire, Danrong Li, Christopher Musco

ICLR 2025 Provably Accurate Shapley Value Estimation via Leverage Score Sampling Christopher Musco, R. Teal Witter

NeurIPS 2025 Regression-Adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values R. Teal Witter, Yurong Liu, Christopher Musco

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

AAAI 2024 A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco

COLT 2024 Agnostic Active Learning of Single Index Models with Linear Sample Complexity Aarshvi Gajjar, Wai Ming Tai, Xu Xingyu, Chinmay Hegde, Christopher Musco, Yi Li

NeurIPS 2024 Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm R. Teal Witter, Christopher Musco

COLT 2024 Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract) Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh

ICLR 2024 Improved Active Learning via Dependent Leverage Score Sampling Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare

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

AISTATS 2023 Active Learning for Single Neuron Models with Lipschitz Non-Linearities Aarshvi Gajjar, Christopher Musco, Chinmay Hegde

ICML 2023 Dimensionality Reduction for General KDE Mode Finding Xinyu Luo, Christopher Musco, Cas Widdershoven

NeurIPSW 2023 Improved Bounds for Agnostic Active Learning of Single Index Models Aarshvi Gajjar, Xingyu Xu, Christopher Musco, Chinmay Hegde

COLT 2023 Moments, Random Walks, and Limits for Spectrum Approximation Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh

NeurIPS 2023 Structured Semidefinite Programming for Recovering Structured Preconditioners Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian

NeurIPS 2021 Dynamic Trace Estimation Prathamesh Dharangutte, Christopher Musco

AAAI 2021 Graph Learning for Inverse Landscape Genetics Prathamesh Dharangutte, Christopher Musco

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

NeurIPS 2020 The Statistical Cost of Robust Kernel Hyperparameter Turning Raphael Meyer, Christopher Musco

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

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 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