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Found 8 papers, 0 papers with code
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise.
Large deviations rates for stochastic gradient descent with strongly convex functions
In this work we provide a formal framework for the study of general high probability bounds with SGD, based on the theory of large deviations.
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown.
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails.
Gradient Based Clustering
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions.
Personalized Federated Learning via Convex Clustering
The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter $\lambda$.
Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics
The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate.
Anomaly Detection
Networking and Internet Architecture
Primal-dual methods for large-scale and distributed convex optimization and data analytics
The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier"(often unconstrained) sub-problems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables.
Optimization and Control Information Theory Information Theory
