activity
20162022
most citedMulti-agent Dynamic Algorithm Configuration

7 citations · 12 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20227 cited

Multi-agent Dynamic Algorithm Configuration

Ke Xue, Jiacheng Xu, Lei Yuan +4

Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (D…

cs.NE20221 cited

Robust Subset Selection by Greedy and Evolutionary Pareto Optimization

Chao Bian, Yawen Zhou, Chao Qian

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor plac…

cs.NE20223 cited

Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection

Chao Bian, Chao Qian

Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool. However, the theoretical foundation of mu…

stat.ML20191 cited

Bayesian Optimization using Pseudo-Points

Chao Qian, Hang Xiong, Ke Xue

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, robotics. BO usually model…

cs.LG2018

Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization

Yibo Zhang, Chao Qian, Ke Tang

In this paper, we study the problem of monotone (weakly) DR-submodular continuous maximization. While previous methods require the gradient information of the objective function, w…

cs.NE2016

A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions

Chao Qian, Yang Yu, Zhi-Hua Zhou

Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in various real-world optimization tasks. However, pr…