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Found 8 papers, 1 papers with code
Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems
We constructed primal-dual algorithms that achieve competitive performance depending on the quality of the predictions.
Handling Delayed Feedback in Distributed Online Optimization : A Projection-Free Approach
Learning at the edges has become increasingly important as large quantities of data are continually generated locally.
One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization
Decentralized learning has been studied intensively in recent years motivated by its wide applications in the context of federated learning.
Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-building
The design of decentralized learning algorithms is important in the fast-growing world in which data are distributed over participants with limited local computation resources and communication.
Online Primal-Dual Algorithms with Predictions for Packing Problems
The domain of online algorithms with predictions has been extensively studied for different applications such as scheduling, caching (paging), clustering, ski rental, etc.
Online Non-Monotone DR-submodular Maximization
First, we present an online algorithm that achieves a $1/e$-approximation ratio with the regret of $O(T^{2/3})$ for maximizing DR-submodular functions over any down-closed convex set.
Online learning for min-max discrete problems
We show that for different nonlinear discrete optimization problems, it is strongly $NP$-hard to solve the offline optimization oracle, even for problems that can be solved in polynomial time in the static case (e. g. min-max vertex cover, min-max perfect matching, etc.).
Non-monotone DR-submodular Maximization: Approximation and Regret Guarantees
In this paper, we study the fundamental problem of maximizing non-monotone DR-submodular functions over down-closed and general convex sets in both offline and online settings.
