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Found 9 papers, 0 papers with code
PersA-FL: Personalized Asynchronous Federated Learning
Our main technical contribution is a unified proof for asynchronous federated learning with bounded staleness that we apply to MAML and ME personalization frameworks.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems.
Distributed Optimization
Optimization and Control
Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention
Natural language processing techniques have demonstrated promising results in keyphrase generation.
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
Then, we study distributed optimization algorithms for non-dual friendly functions, as well as a method to improve the dependency on the parameters of the functions involved.
Optimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks.
Communication-Efficient Algorithms for Decentralized and Stochastic Optimization
Our major contribution is to present a new class of decentralized primal-dual type algorithms, namely the decentralized communication sliding (DCS) methods, which can skip the inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions.
Coordinate Dual Averaging for Decentralized Online Optimization with Nonseparable Global Objectives
In ODA-C, to mitigate the disagreements on the primal-vector updates, the agents implement a generalization of the local information-exchange dynamics recently proposed by Li and Marden over a static undirected graph.
On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging
This paper considers stochastic subgradient mirror-descent method for solving constrained convex minimization problems.
Optimization and Control Systems and Control
