In this paper, we consider optimizing submodular functions that are drawn from some unknown distribution. This setting arises, e.g., in recommender systems, ...
Abstract. In this paper, we consider optimizing submodu- lar functions that are drawn from some unknown distribution. This setting arises, e.g., in recom-.
In this paper, we consider optimizing submodular functions that are drawn from some unknown distribution. This setting arises, e.g., in recommender systems, ...
In this work, we propose the first deterministic linear time algorithm for maximizing a monotone submodular function subject to a knapsack constraints (SMK), ...
Missing: Probabilistic | Show results with:Probabilistic
Oct 31, 2022 · Abstract: In this paper, we provide the first deterministic algorithm that achieves 1 / 2 -approximation for monotone submodular maximization ...
Sep 5, 2024 · In this paper, we in- troduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, ...
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the ...
Apr 3, 2024 · In this paper, we provide the first deterministic algorithm that achieves 1/2- approximation for monotone submodular maximization subject to ...
Missing: Probabilistic | Show results with:Probabilistic
Sep 5, 2024 · In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions.
In this paper, we study the problem of maximizing continuous submodular func- tions that naturally arise in many learning applications such as those ...