Estimating the optimal private density of instances in the Wasserstein distance
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical contexts, the Wasserstein ...
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical contexts, the Wasserstein ...
We study the problem of estimating the mean of private vectors in the random privacy model where north<annotation encoding="application/x-tex">northnorth Users ...
As bitcoin becomes more integrated into the economy, investors recognize the importance of scalability. bitcoin investors are particularly interested in ...
With the rise of language models, enormous attention has been paid to improving the learning of LMs to accelerate the ...
We study differentially private convex stochastic optimization (DP-SCO) under user-level privacy, where each user can have multiple data items. Existing ...
Two prominent limitations have long hampered the relevance of optimal transport methods for machine learning. First the computational cost of ...
Neural graph primitives (NGPs) show promise in enabling the seamless integration of new and old assets in various applications. They ...
We study the problem of locally private mean estimation of high-dimensional vectors on the Euclidean ball. Existing algorithms for this ...
As the digital landscape continues to evolve, electronic wallets, or eWallets, have become indispensable tools for managing our financial lives. ...