← Authors

Cameron Musco

34 papers · 2015–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (7) 🌈 Renaissance Researcher (8) πŸ—ΊοΈ Taxonomy Completionist (61) 🏠 Conference Loyalist (20) 🧬 Topic Evolution πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter ❓ The Questioner πŸ’Ž Century Club (34) πŸ—ƒοΈ Keyword Collector (174) ⚑ Prolific Year (5)

Conferences

NIPS (20) ICML (6) AISTATS (3) ALT (2) COLT (2) AAAI (1)

Top co-authors

Christopher Musco (9) Sudhanshu Chanpuriya (5) Tung Mai (5) Mohit Yadav (3) Anup Rao (3) Charalampos Tsourakakis (3) Daniel Sheldon (3) Konstantinos Sotiropoulos (3) Raghavendra Addanki (3) Aaron Sidford (2)

Research topics

Privacy (1)

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 – 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