Cameron Musco
34 papers · 2015–2025 · 6 conferences · across top CS/AI conferences
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(34)
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Conferences
NIPS (20)
ICML (6)
AISTATS (3)
ALT (2)
COLT (2)
AAAI (1)
Top co-authors
Research topics
Keywords
matrix approximation
(5)
gaussian process
(3)
kernel matrix
(3)
leverage score
(3)
causal inference
(2)
graph algorithm
(2)
random fourier feature
(2)
nystrom method
(2)
node embedding
(2)
dimensionality reduction
(2)
compressed sensing
(2)
density estimation
(2)
link prediction
(2)
gaussian process regression
(2)
kernel approximation
(2)
importance sampling
(2)
gaussian kernel
(2)
loss function
(2)
principal component analysis
(2)
inducing point
(2)
Papers
Sharper Bounds for Chebyshev Moment Matching, with Applications
COLT 2025
Nearly Optimal Approximation of Matrix Functions by the Lanczos Method
NIPS 2024
Gaussian Process Bandits for Top-k Recommendations
NIPS 2024
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits
NIPS 2024
Efficient and Private Marginal Reconstruction with Local Non-Negativity
NIPS 2024
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
NIPS 2023
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
NIPS 2023
Optimal Sketching Bounds for Sparse Linear Regression
AISTATS 2023
No-regret Algorithms for Fair Resource Allocation
NIPS 2023
Sample Constrained Treatment Effect Estimation
NIPS 2022
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings
NIPS 2022
Kernel Interpolation with Sparse Grids
NIPS 2022
Sublinear Time Approximation of Text Similarity Matrices
AAAI 2022
Simplified Graph Convolution with Heterophily
NIPS 2022
DeepWalking Backwards: From Embeddings Back to Graphs
ICML 2021
Coresets for Classification β Simplified and Strengthened
NIPS 2021
On the Power of Edge Independent Graph Models
NIPS 2021
Faster Kernel Interpolation for Gaussian Processes
AISTATS 2021
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
ALT 2021
Subspace Embeddings under Nonlinear Transformations
ALT 2021
Faster Kernel Matrix Algebra via Density Estimation
ICML 2021
Node Embeddings and Exact Low-Rank Representations of Complex Networks
NIPS 2020
Importance Sampling via Local Sensitivity
AISTATS 2020
Efficient Intervention Design for Causal Discovery with Latents
ICML 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning
NIPS 2020
Toward a Characterization of Loss Functions for Distribution Learning
NIPS 2019
Learning to Prune: Speeding up Repeated Computations
COLT 2019
Inferring Networks From Random Walk-Based Node Similarities
NIPS 2018
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
NIPS 2017
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
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
ICML 2016
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
NIPS 2015