22 citations · 22 across the 3 of their papers we have counts for
5 papers
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…
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…
Computing Tight Differential Privacy Guarantees Using FFT
Antti Koskela, Joonas Jälkö, Antti Honkela
Differentially private (DP) machine learning has recently become popular. The privacy loss of DP algorithms is commonly reported using -DP. In this paper, we propo…
Learning Rate Adaptation for Federated and Differentially Private Learning
Antti Koskela, Antti Honkela
We propose an algorithm for the adaptation of the learning rate for stochastic gradient descent (SGD) that avoids the need for validation set use. The idea for the adaptiveness com…