46 citations · 62 across the 6 of their papers we have counts for
7 papers
Federated Differentially Private Optimization via Low-Rank Preconditioning
Antti Koskela, Tejas Kulkarni, Elias Raninen
We consider differentially private (DP) optimization in the federated setting, where a DP estimate of the feature covariance matrix serves as a Hessian approximation for generalize…
EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver
Mikko Honkala, Dani Korpi, Elias Raninen +1
While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial…
Beamforming design for minimizing the signal power estimation error
Esa Ollila, Xavier Mestre, Elias Raninen
We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon…
On the variability of the sample covariance matrix under complex elliptical distributions
Elias Raninen, Esa Ollila, David E. Tyler
We derive the form of the variance-covariance matrix for any affine equivariant matrix-valued statistics when sampling from complex elliptical distributions. We then use this resul…
Coupled regularized sample covariance matrix estimator for multiple classes
Elias Raninen, Esa Ollila
The estimation of covariance matrices of multiple classes with limited training data is a difficult problem. The sample covariance matrix (SCM) is known to perform poorly when the…
Linear pooling of sample covariance matrices
Elias Raninen, David E. Tyler, Esa Ollila
We consider the problem of estimating high-dimensional covariance matrices of -populations or classes in the setting where the sample sizes are comparable to the data dimension.…
Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions
Esa Ollila, Elias Raninen
This paper considers the problem of estimating a high-dimensional (HD) covariance matrix when the sample size is smaller, or not much larger, than the dimensionality of the data, w…