Christopher Musco
23 papers · 2015–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge π£ Hot Topic Early Bird πΊοΈ Taxonomy Completionist (11) π§ Keyword Pioneer π Conference Polyglot (6)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π
Keyword Champion
(2)
π
Grand Slam
ποΈ
Keyword Collector
(110)
π
Trend Setter
π
Century Club
(23)
β‘
Prolific Year
(7)
Conferences
NIPS (10)
COLT (4)
ICLR (3)
ICML (3)
AAAI (2)
AISTATS (1)
Top co-authors
Research topics
Keywords
matrix approximation
(4)
active learning
(3)
leverage score sampling
(3)
kernel approximation
(2)
kernel methods
(2)
agnostic learning
(2)
random fourier feature
(2)
principal component analysis
(2)
leverage score
(2)
gaussian process regression
(2)
dimensionality reduction
(2)
graph learning
(2)
randomized algorithm
(2)
krylov subspace
(2)
random walk
(2)
sample complexity
(1)
ridge regression
(1)
algorithmic fairness
(1)
hyperparameter optimization
(1)
differential privacy
(1)
Papers
Sharper Bounds for Chebyshev Moment Matching, with Applications
COLT 2025
Matrix Product Sketching via Coordinated Sampling
ICLR 2025
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
ICLR 2025
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract)
COLT 2024
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits
NIPS 2024
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
NIPS 2024
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
COLT 2024
Nearly Optimal Approximation of Matrix Functions by the Lanczos Method
NIPS 2024
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
AAAI 2024
Improved Active Learning via Dependent Leverage Score Sampling
ICLR 2024
Dimensionality Reduction for General KDE Mode Finding
ICML 2023
Structured Semidefinite Programming for Recovering Structured Preconditioners
NIPS 2023
Active Learning for Single Neuron Models with Lipschitz Non-Linearities
AISTATS 2023
Moments, Random Walks, and Limits for Spectrum Approximation
COLT 2023
Graph Learning for Inverse Landscape Genetics
AAAI 2021
Dynamic Trace Estimation
NIPS 2021
The Statistical Cost of Robust Kernel Hyperparameter Turning
NIPS 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning
NIPS 2020
Inferring Networks From Random Walk-Based Node Similarities
NIPS 2018
Recursive Sampling for the Nystrom Method
NIPS 2017
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
ICML 2017
Principal Component Projection Without Principal Component Analysis
ICML 2016
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
NIPS 2015