Submodular maximization
Distributed Submodular Maximization
... a submodular set function subject to various ...to submodular optimization require centralized access to the full dataset, which is impractical for truly large-scale ...of submodular function ...
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Stochastic Submodular Maximization with Performance-Dependent Item Costs
... Our algorithms are based on the contention resolution scheme, which is a general framework to design approxi- mation algorithms for the submodular maximization. The contention resolution scheme is so useful ...
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Subtree Extractive Summarization via Submodular Maximization
... of submodular maximization prob- lem: a budgeted monotone nondecreasing sub- modular function maximization with a cost func- tion, where the cost of an extraction unit varies depending on what other ...
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Unsupervised Abstractive Meeting Summarization with Multi Sentence Compression and Budgeted Submodular Maximization
... Budgeted submodular maximization We apply the previous steps separately for all ut- terance communities, which results in a set S of abstractive sentences (one for each ...
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Word Alignment via Submodular Maximization over Matroids
... as submodular maximization subject to matroid ...respects: submodular objective functions generalize modular (linear) objective functions, and matroid constraints generalize matching ...since ...
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Subspace Selection via DR-Submodular Maximization on Lattices
... In the set lattice and distributive lattices, the double greedy algorithm (Buchbinder et al. 2015; Gottschalk and Peis 2015) achieves the deterministic 1/3 and randomized 1/2 approxi- mation ratio for the unconstrained ...
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New algorithms for distributed submodular maximization
... a submodular function f , and (possibly) a set of constraints, we aim to find a feasible set S maximizing f ...on submodular maximization, the community has a good understanding of the problem under ...
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Monotone submodular maximization over a matroid via non oblivious local search
... monotone submodular maximization over a ma- troid, the locality ratio for an algorithm changing a single element at each step is 1/2 ...the submodular ob- jective function f ...
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Resilient Submodular Maximization For Control And Sensing
... constrained maximization have met great success in machine learning and facility location, they are unable to meet the aforementioned problem of actuator ...matroid-constrained maximization; adaptiveness: ...
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Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
... Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive ...on submodular- ...tone submodular objective function, includ- ing many functions ...
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The Submodular Welfare Problem with Demand Queries
... particular submodular utility ...the Submodular Welfare Problem, using a simple greedy algorithm using only value ...on submodular maximization subject to a matroid constraint ...approximate ...
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A new wireless base pit inclinometer based on NB-IoT
... sum-rate maximization algorithm and min-rate maximization algorithm, which solve the corresponding problem by sequentially solves convex optimization ...
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Convex Analysis for Minimizing and Learning Submodular Set Functions
... of submodular functions: on one hand there are very basic or specialized functions that admit simple and practical minimization algorithms, but are fairly limited in what they can describe, and, on the other, ...
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Indivisible labor supply and involuntary unemployment: Increasing returns to scale case
... utility maximization of consumers and profit maximization of firms in an overlapping generations model under monopolistic competition according to Otaki (2007), Otaki (2009), Otaki (2011) and Otaki (2015) ...
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Comparative Document Summarisation via Classification
... This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative ...
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Discrete Methods in Statistics: Feature Selection and Fairness-Aware Data Mining
... The RH procedure has the highest power of all of the methods by a significant margin, even in cases in which its corrections are not exact. In low signal cases, this comes at a cost of having the highest FDR of the ...
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Choosing panels of genomics assays using submodular optimization
... functions. Submodular functions have a long history in economics [9, 10], game theory [11, 12], combinatorial optimization [13–15], elec- trical networks [16], operations research [17], and more recently, machine ...
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Active-set Methods for Submodular Minimization Problems
... other submodular functions on which our algorithm works, ...among submodular functions. We now review the relevant concepts from submodular analysis (for more details, see Bach, 2013; Fujishige, ...
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Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection
... In this section, we present our core theoretical result, which shows that, under certain conditions, the most important of which is submodularity, the error in the value function com- puted by backups based on greedy ...
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Submodular Optimization over Streams with Inhomogeneous Decays
... streaming submodular optimization (SSO) techniques have been developed for insertion-only streams where a subset is selected from all historical data (Badanidiyuru et ...
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