Sepideh Mahabadi

Senior Researcher

Publications

Afficher par:

Efficiently Computing Similarities to Private Datasets

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

ICLR 2024 | May 2024

Publication Projet
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Siddharth Gollapudi, Sepideh Mahabadi, Varun Sivashankar

NeurIPS 2023 | December 2023

Publication

Core-sets for Fair and Diverse Data Summarization

Sepideh Mahabadi, Stojan Trajanovski

NeurIPS 2023 | December 2023

Publication PDF

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayana

NeurIPS 2023 | December 2023

Spotlight Presentation

Publication Projet

Tight Bounds for Volumetric Spanners and Applications

Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian

NeurIPS 2023 | December 2023

Publication

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Sepideh Mahabadi, Shyam Narayanan

APPROX | September 2023

Publication

Approximation Algorithms for Fair Range Clustering

Sèdjro S. Hotegni, Sepideh Mahabadi, Ali Vakilian

ICML | July 2023

Publication
Composable Coresets for Constrained Determinant Maximization and Beyond

Sepideh Mahabadi, Thuy-Duong Vuong

November 2022

Publication

Adaptive Sketches for Robust Regression with Importance Sampling

Sepideh Mahabadi, David P. Woodruff, Samson Zhou

RANDOM | September 2022

Publication

Sampling Near Neighbors in Search for Fairness

Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri

Communications of the ACM (CACM) | August 2022

Publication

Efficiently Computing Similarities to Private Datasets

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

ICLR 2024 | May 2024

Publication Projet

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayana

NeurIPS 2023 | December 2023

Spotlight Presentation

Publication Projet

Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Siddharth Gollapudi, Sepideh Mahabadi, Varun Sivashankar

NeurIPS 2023 | December 2023

Publication

Tight Bounds for Volumetric Spanners and Applications

Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian

NeurIPS 2023 | December 2023

Publication

Core-sets for Fair and Diverse Data Summarization

Sepideh Mahabadi, Stojan Trajanovski

NeurIPS 2023 | December 2023

Publication PDF

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Sepideh Mahabadi, Shyam Narayanan

APPROX | September 2023

Publication

Approximation Algorithms for Fair Range Clustering

Sèdjro S. Hotegni, Sepideh Mahabadi, Ali Vakilian

ICML | July 2023

Publication

Composable Coresets for Constrained Determinant Maximization and Beyond

Sepideh Mahabadi, Thuy-Duong Vuong

November 2022

Publication

Adaptive Sketches for Robust Regression with Importance Sampling

Sepideh Mahabadi, David P. Woodruff, Samson Zhou

RANDOM | September 2022

Publication

Sampling Near Neighbors in Search for Fairness

Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri

Communications of the ACM (CACM) | August 2022

Publication
Efficiently Computing Similarities to Private Datasets

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

ICLR 2024 | May 2024

Publication Projet

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayana

NeurIPS 2023 | December 2023

Spotlight Presentation

Publication Projet
Differentially Private Approximate Near Neighbor Counting in High Dimensions

Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayana

NeurIPS 2023 | December 2023

Spotlight Presentation

Publication Projet

Core-sets for Fair and Diverse Data Summarization

Sepideh Mahabadi, Stojan Trajanovski

NeurIPS 2023 | December 2023

Publication PDF

Efficiently Computing Similarities to Private Datasets

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

ICLR 2024 | May 2024

Publication Projet

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayana

NeurIPS 2023 | December 2023

Spotlight Presentation

Publication Projet

Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Siddharth Gollapudi, Sepideh Mahabadi, Varun Sivashankar

NeurIPS 2023 | December 2023

Publication

Tight Bounds for Volumetric Spanners and Applications

Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian

NeurIPS 2023 | December 2023

Publication

Core-sets for Fair and Diverse Data Summarization

Sepideh Mahabadi, Stojan Trajanovski

NeurIPS 2023 | December 2023

Publication PDF

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Sepideh Mahabadi, Shyam Narayanan

APPROX | September 2023

Publication

Approximation Algorithms for Fair Range Clustering

Sèdjro S. Hotegni, Sepideh Mahabadi, Ali Vakilian

ICML | July 2023

Publication

Adaptive Sketches for Robust Regression with Importance Sampling

Sepideh Mahabadi, David P. Woodruff, Samson Zhou

RANDOM | September 2022

Publication
Composable Coresets for Constrained Determinant Maximization and Beyond

Sepideh Mahabadi, Thuy-Duong Vuong

November 2022

Publication
Sampling Near Neighbors in Search for Fairness

Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri

Communications of the ACM (CACM) | August 2022

Publication

Sepideh Mahabadi

Publications

Efficiently Computing Similarities to Private Datasets

Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Core-sets for Fair and Diverse Data Summarization

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Tight Bounds for Volumetric Spanners and Applications

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Approximation Algorithms for Fair Range Clustering

Composable Coresets for Constrained Determinant Maximization and Beyond

Adaptive Sketches for Robust Regression with Importance Sampling

Sampling Near Neighbors in Search for Fairness

Efficiently Computing Similarities to Private Datasets

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Tight Bounds for Volumetric Spanners and Applications

Core-sets for Fair and Diverse Data Summarization

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Approximation Algorithms for Fair Range Clustering

Composable Coresets for Constrained Determinant Maximization and Beyond

Adaptive Sketches for Robust Regression with Importance Sampling

Sampling Near Neighbors in Search for Fairness

Efficiently Computing Similarities to Private Datasets

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Core-sets for Fair and Diverse Data Summarization

Efficiently Computing Similarities to Private Datasets

Differentially Private Approximate Near Neighbor Counting in High Dimensions

Composable Coresets for Determinant Maximization: Greedy is Almost Optimal

Tight Bounds for Volumetric Spanners and Applications

Core-sets for Fair and Diverse Data Summarization

Improved Diversity Maximization Algorithms for Matching and Pseudoforest

Approximation Algorithms for Fair Range Clustering

Adaptive Sketches for Robust Regression with Importance Sampling

Composable Coresets for Constrained Determinant Maximization and Beyond

Sampling Near Neighbors in Search for Fairness

Contact Sepideh Mahabadi

Microsoft Research Lab – Redmond

Sepideh Mahabadi

Publications

Publication Type

2024

2023

2022

Filtre par zone de recherche

Algorithmes

Security, privacy, and cryptography

Intelligence artificielle

Publication Type

Inproceedings (Conference)

Unpublished

Article (Journal)