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Found 6 papers, 3 papers with code
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
We consider a cross-silo federated learning (FL) setting where a machine learning model with a fully connected first layer is trained between different clients and a central server using FedAvg, and where the aggregation step can be performed with secure aggregation (SA).
Sampling from Arbitrary Functions via PSD Models
In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i. i. d.)
Finding Global Minima via Kernel Approximations
We consider the global minimization of smooth functions based solely on function evaluations.
Non-parametric Models for Non-negative Functions
The paper is complemented by an experimental evaluation of the model showing its effectiveness in terms of formulation, algorithmic derivation and practical results on the problems of density estimation, regression with heteroscedastic errors, and multiple quantile regression.
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regression and softmax regression.
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictors through positive-definite kernels.
