Cameron Musco
35 papers · 2015–2026 · 6 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 10 separate collaboration groups - the largest holds only 34% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
Achievements are not shown for a combined profile - each one describes a single researcher's record, which this is not.
Which Cameron Musco?
4 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Sudhanshu Chanpuriya, Tung Mai, Konstantinos Sotiropoulos
12 papers
· 2017–2023
· NeurIPS, ICML, AISTATS
Hide ▲
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
NeurIPS 2023
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
NeurIPS 2023
Optimal Sketching Bounds for Sparse Linear Regression
AISTATS 2023
Sample Constrained Treatment Effect Estimation
NeurIPS 2022
Simplified Graph Convolution with Heterophily
NeurIPS 2022
Coresets for Classification – Simplified and Strengthened
NeurIPS 2021
DeepWalking Backwards: From Embeddings Back to Graphs
ICML 2021
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
ALT 2021
On the Power of Edge Independent Graph Models
NeurIPS 2021
Efficient Intervention Design for Causal Discovery with Latents
ICML 2020
Node Embeddings and Exact Low-Rank Representations of Complex Networks
NeurIPS 2020
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
NeurIPS 2017
6 papers whose co-authors appear nowhere else under this name
No-regret Algorithms for Fair Resource Allocation
NeurIPS 2023
Faster Kernel Matrix Algebra via Density Estimation
ICML 2021
Subspace Embeddings under Nonlinear Transformations
ALT 2021
Importance Sampling via Local Sensitivity
AISTATS 2020
Learning to Prune: Speeding up Repeated Computations
COLT 2019
Toward a Characterization of Loss Functions for Distribution Learning
NeurIPS 2019