22 citations · 27 across the 9 of their papers we have counts for
5 papers · 1 filter
Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela +2
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of…
Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods
Peter Kandolf, Antti Koskela, Samuel D. Relton +1
The Fréchet derivative of the matrix function plays an important role in many different applications, including condition number estimation and network analysis.…
Differentially private cross-silo federated learning
Mikko A. Heikkilä, Antti Koskela, Kana Shimizu +2
Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recent…
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Antti Koskela, Joonas Jälkö, Lukas Prediger +1
We propose a numerical accountant for evaluating the tight -privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy lo…
Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions
Antti Koskela, Samuel D. Relton
We consider linearizations of stochastic differential equations with additive noise using the Karhunen-Loève expansion. We obtain our linearizations by truncating the expansion and…