1 citations · 1 across the 5 of their papers we have counts for
5 papers
Efficiently Computing Similarities to Private Datasets
Arturs Backurs, Zinan Lin, Sepideh Mahabadi +2
Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out th…
Core-sets for Fair and Diverse Data Summarization
Sepideh Mahabadi, Stojan Trajanovski
We study core-set construction algorithms for the task of Diversity Maximization under fairness/partition constraint. Given a set of points in a metric space partitioned into $…
Tight Bounds for Volumetric Spanners and Applications
Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian
Given a set of points of interest, a volumetric spanner is a subset of the points using which all the points can be expressed using "small" coefficients (measured in an appropriate…
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
Siddharth Gollapudi, Sepideh Mahabadi, Varun Sivashankar
Given a set of vectors in , the goal of the \emph{determinant maximization} problem is to pick vectors with the maximum volume. Determinant maximization is th…
Adaptive Sketches for Robust Regression with Importance Sampling
Sepideh Mahabadi, David P. Woodruff, Samson Zhou
We introduce data structures for solving robust regression through stochastic gradient descent (SGD) by sampling gradients with probability proportional to their norm, i.e., import…