Categories: FAANG

Instance-Optimal Private Density Estimation in the Wasserstein Distance

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances.
For distributions…

GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics

Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing technologies are inherently destructive and can only measure a limited array of data modalities simultaneously. This limitation underscores the need for new methods capable of realigning cells. Optimal transport (OT)…

December 4, 2024

In "FAANG"

Universally Instance-Optimal Mechanisms for Private Statistical Estimation

We consider the problem of instance-optimal statistical estimation under the constraint of differential privacy where mechanisms must adapt to the difficulty of the input dataset. We prove a new instance specific lower bound using a new divergence and show it characterizes the local minimax optimal rates for private statistical estimation.…

April 2, 2025

In "FAANG"

AI enables more effective humanitarian action by estimating population density

Researchers from EPFL and ETH Zurich, working together with the International Committee of the Red Cross (ICRC) and Bin Khalifa Unversity (Qatar), have developed a program that can generate population density estimates with unparalleled precision, and only needs a rough estimate at the regional level to learn.

December 13, 2022

In "AI/ML News"

AI Generated Robotic Content