Christopher Musco

Christopher Musco

New York University

H-index: 18

North America-United States

About Christopher Musco

Christopher Musco, With an exceptional h-index of 18 and a recent h-index of 17 (since 2020), a distinguished researcher at New York University, specializes in the field of Algorithms, Theory of Computation, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

A simple and practical method for reducing the disparate impact of differential privacy

Fixed-sparsity matrix approximation from matrix-vector products

Structured semidefinite programming for recovering structured preconditioners

Simple Analysis of Priority Sampling

On the unreasonable effectiveness of single vector krylov methods for low-rank approximation

Improved Bounds for Agnostic Active Learning of Single Index Models

Algorithm-agnostic low-rank approximation of operator monotone matrix functions

Improved Active Learning via Dependent Leverage Score Sampling

Christopher Musco Information

University

Position

Assistant Professor

Citations(all)

2461

Citations(since 2020)

1960

Cited By

1215

hIndex(all)

18

hIndex(since 2020)

17

i10Index(all)

28

i10Index(since 2020)

28

Email

University Profile Page

Google Scholar

Christopher Musco Skills & Research Interests

Algorithms

Theory of Computation

Machine Learning

Top articles of Christopher Musco

A simple and practical method for reducing the disparate impact of differential privacy

Proceedings of the AAAI Conference on Artificial Intelligence

2024/3/24

Fixed-sparsity matrix approximation from matrix-vector products

arXiv preprint arXiv:2402.09379

2024/2/14

Structured semidefinite programming for recovering structured preconditioners

Advances in Neural Information Processing Systems

2024/2/13

Jerry Li
Jerry Li

H-Index: 3

Christopher Musco
Christopher Musco

H-Index: 13

Aaron Sidford
Aaron Sidford

H-Index: 26

Simple Analysis of Priority Sampling

2024

On the unreasonable effectiveness of single vector krylov methods for low-rank approximation

2024

Cameron Musco
Cameron Musco

H-Index: 17

Christopher Musco
Christopher Musco

H-Index: 13

Improved Bounds for Agnostic Active Learning of Single Index Models

2023/12/22

Algorithm-agnostic low-rank approximation of operator monotone matrix functions

arXiv preprint arXiv:2311.14023

2023/11/23

David Persson
David Persson

H-Index: 11

Christopher Musco
Christopher Musco

H-Index: 13

Improved Active Learning via Dependent Leverage Score Sampling

arXiv preprint arXiv:2310.04966

2023/10/8

Christopher Musco
Christopher Musco

H-Index: 13

Sampling Methods for Inner Product Sketching

arXiv preprint arXiv:2309.16157

2023/9/28

Moments, Random Walks, and Limits for Spectrum Approximation

2023/7/12

Dimensionality reduction for general KDE mode finding

2023/7/3

Christopher Musco
Christopher Musco

H-Index: 13

Low-memory Krylov subspace methods for optimal rational matrix function approximation

SIAM Journal on Matrix Analysis and Applications

2023/6/30

Weighted minwise hashing beats linear sketching for inner product estimation

2023/6/18

Active learning for single neuron models with lipschitz non-linearities

2023/4/11

Near-Optimal Approximation of Matrix Functions by the Lanczos Method

arXiv preprint arXiv:2303.03358

2023/3/6

Efficient Block Approximate Matrix Multiplication

2023

Christopher Musco
Christopher Musco

H-Index: 13

Near-Linear Sample Complexity for Lp Polynomial Regression

2023

Cameron Musco
Cameron Musco

H-Index: 17

Christopher Musco
Christopher Musco

H-Index: 13

A tight analysis of hutchinson's diagonal estimator

2023

Christopher Musco
Christopher Musco

H-Index: 13

Active Linear Regression for ℓp Norms and Beyond

2022/10/31

Sublinear Time Spectral Density Estimation

2022/6/9

See List of Professors in Christopher Musco University(New York University)

Co-Authors

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