9 citations · 19 across the 5 of their papers we have counts for
6 papers
Optimal Sketching Bounds for Sparse Linear Regression
Tung Mai, Alexander Munteanu, Cameron Musco +3
We study oblivious sketching for -sparse linear regression under various loss functions such as an norm, or from a broad class of hinge-like loss functions, which inclu…
Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai +2
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some tr…
Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries
Raghavendra Addanki, Andrew McGregor, Cameron Musco
We study the problem of estimating the number of edges in an -vertex graph, accessed via the Bipartite Independent Set query model introduced by Beame et al. (ITCS '18). In this…
Coresets for Classification -- Simplified and Strengthened
Tung Mai, Anup B. Rao, Cameron Musco
We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss. Our construction achieves $(1\pm ε…
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Raghavendra Addanki, Andrew McGregor, Cameron Musco
We study the problem of learning the causal relationships between a set of observed variables in the presence of latents, while minimizing the cost of interventions on the observed…
Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor +1
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two interventio…