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Found 7 papers, 1 papers with code
Boosting Gradient Ascent for Continuous DR-submodular Maximization
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research.
MNL-Bandits under Inventory and Limited Switches Constraints
Optimizing the assortment of products to display to customers is a key to increasing revenue for both offline and online retailers.
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
Online Allocation Problem with Two-sided Resource Constraints
As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
It enables learning high-quality Laplacian representations that faithfully approximate the ground truth.
