Results 51 to 60 of about 13,203 (237)
Continuous submodular function maximization
CoRR, 2020 Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time.
Bian, Yatao; id_orcid0000-0002-2368-4084 +2 more
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A Min-Max . . . Functions and Its Implications [PDF]
, 2014 A. Huber and V. Kolmogorov (ISCO 2012) introduced a concept of k-submodular function as a generalization of ordinary submodular (set) functions and bisubmodular functions and obtained a min-max theorem for minimization of k-submodular functions.
Satoru Fujishige, Shin-ichi Tanigawa
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Near Optimal Dynamic Mobile Advertisement Offloading With Time Constraints
IEEE Access, 2019 Owing to the accuracy and flexibility, mobile advertising has become a very attractive marketing method based on smart mobile terminals. The more common mobile advertisement distribution methods are based on location and content.
Wanru Xu, Chaocan Xiang, Chang Tian
doaj +1 more source
A Submodular Optimization Framework for Imbalanced Text Classification With Data Augmentation
IEEE Access, 2023 In the domain of text classification, imbalanced datasets are a common occurrence. The skewed distribution of the labels of these datasets poses a great challenge to the performance of text classifiers.
Eyor Alemayehu, Yi Fang
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Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions [PDF]
, 2015 The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become ...
Badanidiyuru, Ashwinkumar +4 more
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Parallel Submodular Function Minimization
Advances in Neural Information Processing Systems 36, 2023 We consider the parallel complexity of submodular function minimization (SFM). We provide a pair of methods which obtain two new query versus depth trade-offs a submodular function defined on subsets of $n$ elements that has integer values between $-M$ and $M$.
Deeparnab Chakrabarty +3 more
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Deterministic Algorithms for Submodular Maximization Problems
, 2015 Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization.
Buchbinder, Niv, Feldman, Moran
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Hardness of submodular cost allocation : lattice matching and a simplex coloring conjecture [PDF]
, 2014 We consider the Minimum Submodular Cost Allocation (MSCA) problem. In this problem, we are given k submodular cost functions f1, ... , fk: 2V -> R+ and the goal is to partition V into k sets A1, ..., Ak so as to minimize the total cost sumi = 1,k fi(Ai).
Ene, Alina, Vondrák, Jan
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NeuSub: A Neural Submodular Approach for Citation Recommendation
IEEE Access, 2021 Citation recommendation is a task that aims to automatically select suitable references for a working manuscript. This task has become increasingly urgent as the typical pools of candidates continue to grow, in the order of tens or hundreds of thousands ...
Binh Thanh Kieu +4 more
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, 2014 Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice?
Badanidiyuru, Ashwinkumar +4 more
core +1 more source