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Found 8 papers, 0 papers with code
Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback
At round $i$, a user with unknown utility $h_q$ arrives; the optimizer selects a new item to add to $S_q$, and receives a noisy marginal gain.
Fast First-Order Methods for Monotone Strongly DR-Submodular Maximization
Then, we study $L$-smooth monotone strongly DR-submodular functions that have bounded curvature, and we show how to exploit such additional structure to obtain algorithms with improved approximation guarantees and faster convergence rates for the maximization problem.
Improved Regret Bounds for Online Submodular Maximization
For $(1)$, we obtain the first logarithmic regret bounds.
Function Design for Improved Competitive Ratio in Online Resource Allocation with Procurement Costs
We study the problem of online resource allocation, where multiple customers arrive sequentially and the seller must irrevocably allocate resources to each incoming customer while also facing a procurement cost for the total allocation.
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
In this paper, we consider an online optimization problem in which the reward functions are DR-submodular, and in addition to maximizing the total reward, the sequence of decisions must satisfy some convex constraints on average.
Online DR-Submodular Maximization with Stochastic Cumulative Constraints
Stochastic long-term constraints arise naturally in applications where there is a limited budget or resource available and resource consumption at each step is governed by stochastically time-varying environments.
Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints
In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property.
Competitive Algorithms for Online Budget-Constrained Continuous DR-Submodular Problems
In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints.
