Maximizing Submodular or Monotone Approximately Submodular Functions by
Multi-objective Evolutionary Algorithms

v1v2 (latest)

Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms

20 November 2017

ArXiv (abs)PDF HTML

Papers citing "Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms"

8 / 8 papers shown

Title
Pareto Set Learning for Multi-Objective Reinforcement Learning Erlong Liu Yu-Chang Wu Xiaobin Huang Chengrui Gao Ren-Jian Wang Ke Xue Chao Qian OffRL 233 2 0 12 Jan 2025
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms Chao Bian Yawen Zhou Mingxing Li Chao Qian 55 34 0 05 Jun 2023
On Approximation Guarantees for Greedy Low Rank Optimization Rajiv Khanna Ethan R. Elenberg A. Dimakis S. Negahban 61 24 0 08 Mar 2017
Guarantees for Greedy Maximization of Non-submodular Functions with Applications Yatao Bian J. M. Buhmann Andreas Krause Sebastian Tschiatschek 67 237 0 06 Mar 2017
Restricted Strong Convexity Implies Weak Submodularity Ethan R. Elenberg Rajiv Khanna A. Dimakis S. Negahban 68 159 0 02 Dec 2016
Determinantal point processes for machine learning Alex Kulesza B. Taskar 254 1,140 0 25 Jul 2012
Near-optimal Nonmyopic Value of Information in Graphical Models Andreas Krause Carlos Guestrin 85 467 0 04 Jul 2012
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection Abhimanyu Das David Kempe 202 484 0 19 Feb 2011