research
∙
05/26/2023
Differentially private low-dimensional representation of high-dimensional data
Differentially private synthetic data provide a powerful mechanism to en...
share
research
∙
01/06/2023
Covariance loss, Szemeredi regularity, and differential privacy
We show how randomized rounding based on Grothendieck's identity can be ...
research
∙
05/24/2022
Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold
The stochastic block model is a canonical random graph model for cluster...
research
∙
04/20/2022
Private measures, random walks, and synthetic data
Differential privacy is a mathematical concept that provides an informat...
research
∙
01/02/2022
Fair Data Representation for Machine Learning at the Pareto Frontier
As machine learning powered decision making is playing an increasingly i...
research
∙
09/30/2021
Private sampling: a noiseless approach for generating differentially private synthetic data
In a world where artificial intelligence and data science become omnipre...
research
∙
09/03/2021
Privacy of synthetic data: a statistical framework
Privacy-preserving data analysis is emerging as a challenging problem wi...
research
∙
07/13/2021
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data
The protection of private information is of vital importance in data-dri...
research
∙
07/10/2020
A Performance Guarantee for Spectral Clustering
The two-step spectral clustering method, which consists of the Laplacian...
research
∙
04/21/2020
Strong Consistency, Graph Laplacians, and the Stochastic Block Model
Spectral clustering has become one of the most popular algorithms in dat...
research
∙
04/13/2020
The Numerics of Phase Retrieval
Phase retrieval, i.e., the problem of recovering a function from the squ...
research
∙
09/04/2019
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning
Machine learning at the edge offers great benefits such as increased pri...
research
∙
06/29/2018
Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering
Spectral clustering has become one of the most widely used clustering te...
research
∙
10/16/2017
When Do Birds of a Feather Flock Together? K-Means, Proximity, and Conic Programming
Given a set of data, one central goal is to group them into clusters bas...
research
∙
11/03/2010