22 citations · 76 across the 26 of their papers we have counts for
27 papers
When Weak Submodularity Meets Matroids: How Does Standard Greedy Behave?
Qixin Zhang, Shi Fu, Shengminjie Chen +2
Weakly submodular maximization finds numerous applications in machine learning, data mining and statistics. In particular, under a cardinality constraint, the standard greedy algor…
Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
Yu-Ran Gu, Chao Bian, Chao Qian
Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations resea…
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Chao Bian, Yawen Zhou, Miqing Li +1
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population u…
Fast Teammate Adaptation in the Presence of Sudden Policy Change
Ziqian Zhang, Lei Yuan, Lihe Li +5
In cooperative multi-agent reinforcement learning (MARL), where an agent coordinates with teammate(s) for a shared goal, it may sustain non-stationary caused by the policy change o…
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Lei Yuan, Zi-Qian Zhang, Ke Xue +6
Cooperative multi-agent reinforcement learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Can Evolutionary Clustering Have Theoretical Guarantees?
Chao Qian
Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group…
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…
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms
Zeqiong Lv, Chao Qian, Gary G. Yen +1
Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks. Despite its groundbreaking appli…
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Ke Xue, Yutong Wang, Cong Guan +5
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some stu…
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…
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…
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
Shengcai Liu, Ning Lu, Wenjing Hong +2
The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can suc…
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou
Given a ground set of items, the result diversification problem aims to select a subset with high "quality" and "diversity" while satisfying some constraints. It arises in various…
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Chao Qian, Dan-Xuan Liu, Chao Feng +1
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximat…
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions
Chao Qian
As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes…
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…
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Chao Bian, Chao Qian, Yang Yu +1
Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective ev…
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Chao Bian, Chao Qian, Ke Tang +1
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand…
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…
Analysis of Noisy Evolutionary Optimization When Sampling Fails
Chao Qian, Chao Bian, Yang Yu +2
In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{…
ZOOpt: Toolbox for Derivative-Free Optimization
Yu-Ren Liu, Yi-Qi Hu, Hong Qian +2
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima,…
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
Chao Qian, Yang Yu, Ke Tang +2
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimiz…
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
Chao Qian, Chao Bian, Wu Jiang +1
In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a ty…