no code implementations • 4 Mar 2024 • Tianheng Ling, Julian Hoever, Chao Qian, Gregor Schiele
In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge.
no code implementations • 28 Feb 2024 • Ke Xue, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, Chao Qian
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.
1 code implementation • 27 Feb 2024 • Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, Chao Qian
In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
no code implementations • 22 Jan 2024 • Zeqiong Lv, Chao Qian, Yanan sun
Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success.
no code implementations • 19 Jan 2024 • Chao Qian, Ke Xue, Ren-Jian Wang
In this paper, we try to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
1 code implementation • 16 Dec 2023 • Xiaobin Huang, Lei Song, Ke Xue, Chao Qian
Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.
no code implementations • 25 Nov 2023 • Tianheng Ling, Chao Qian, Gregor Schiele
Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception.
no code implementations • 13 Oct 2023 • Dan-Xuan Liu, Yu-Ran Gu, Chao Qian, Xin Mu, Ke Tang
In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem.
no code implementations • 12 Oct 2023 • Tianhao Lu, Chao Bian, Chao Qian
Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II.
no code implementations • 10 Oct 2023 • Ren-Jian Wang, Ke Xue, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, Chao Qian
DivHF learns a behavior descriptor consistent with human preference by querying human feedback.
no code implementations • 4 Oct 2023 • Chao Qian, Tianheng Ling, Gregor Schiele
To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique.
no code implementations • 4 Oct 2023 • Tianheng Ling, Chao Qian, Lukas Einhaus, Gregor Schiele
This study explores the quantisation-aware training (QAT) on time series Transformer models.
no code implementations • 15 Sep 2023 • Jinming Fan, Chao Qian, Shaodong Zhou
A Lewis-mode group contribution method (LGC) -- multi-stage Bayesian neural network (msBNN) -- evolutionary algorithm (EA) framework is reported for rational design of D-Pi-A type organic small-molecule nonlinear optical materials is presented.
1 code implementation • 27 Aug 2023 • Chengrui Gao, Haopu Shang, Ke Xue, Dong Li, Chao Qian
Machine learning has been adapted to help solve NP-hard combinatorial optimization problems.
no code implementations • 18 Jul 2023 • 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 research.
no code implementations • 5 Jun 2023 • Chao Bian, Yawen Zhou, Miqing Li, Chao Qian
This work is an attempt to challenge a common practice in the design of existing MOEAs.
1 code implementation • 10 May 2023 • Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Li-He Li, Chao Qian, Yang Yu
Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.
1 code implementation • 2 Feb 2023 • Frank Neumann, Aneta Neumann, Chao Qian, Viet Anh Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.
no code implementations • 4 Dec 2022 • Chao Qian
We prove that for discrete $k$-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.
1 code implementation • 13 Oct 2022 • Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu
MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.
no code implementations • 11 Oct 2022 • Zeqiong Lv, Chao Qian, Gary G. Yen, Yanan sun
Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks.
1 code implementation • 4 Oct 2022 • Lei Song, Ke Xue, Xiaobin Huang, Chao Qian
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios.
no code implementations • 9 Aug 2022 • Ke Xue, Yutong Wang, Cong Guan, Lei Yuan, Haobo Fu, Qiang Fu, Chao Qian, Yang Yu
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL).
no code implementations • 3 May 2022 • Chao Bian, Yawen Zhou, Chao Qian
We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets.
no code implementations • 12 Apr 2022 • Haopu Shang, Jia-Liang Wu, Wenjing Hong, Chao Qian
Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy.
no code implementations • 22 Mar 2022 • Chao Bian, Chao Qian
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool.
no code implementations • 20 Mar 2022 • Ren Kai Tan, Chao Qian, Dan Xu, Wenjing Ye
Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes.
1 code implementation • 2 Nov 2021 • Shengcai Liu, Ning Lu, Wenjing Hong, Chao Qian, Ke Tang
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 successfully fool the target model.
1 code implementation • 18 Oct 2021 • Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou
Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i. e., the greedy algorithm and local search) under both static and dynamic environments.
no code implementations • ICLR 2022 • Yutong Wang, Ke Xue, Chao Qian
However, due to the inefficient selection mechanisms, these methods cannot fully guarantee both high quality and diversity.
no code implementations • 20 Apr 2021 • Chao Qian, Dan-Xuan Liu, Chao Feng, Ke Tang
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution.
no code implementations • 29 Jan 2021 • Chao Qian, Renkai Tan, Wenjing Ye
In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process.
BIG-bench Machine Learning Generative Adversarial Network +1
no code implementations • 14 Sep 2020 • Chao Qian, Wenjing Ye
In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design process of topology optimization.
1 code implementation • IJCAI 2020 • Fei-Yu Liu, Zi-Niu Li, Chao Qian
Evolution Strategies (ES) are a class of black-box optimization algorithms and have been widely applied to solve problems, e. g., in reinforcement learning (RL), where the true gradient is unavailable.
no code implementations • 12 Oct 2019 • Chao Qian
To complement this line of research, this paper studies the problem class of maximizing monotone approximately submodular minus modular functions (i. e., $f=g-c$) with a size constraint, where $g$ is a non-negative monotone approximately submodular function and $c$ is a non-negative modular function, resulting in the objective function $f$ being non-monotone non-submodular.
no code implementations • 12 Oct 2019 • 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.
no code implementations • 28 Jul 2019 • Chao Bian, Chao Qian, Yang Yu, Ke Tang
Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value.
no code implementations • 17 Jun 2019 • Chao Bian, Chao Qian, Ke Tang, Yang Yu
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties.
no code implementations • 16 Oct 2018 • Yibo Zhang, Chao Qian, Ke Tang
Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm.
no code implementations • 11 Oct 2018 • Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao
In noisy evolutionary optimization, sampling is a common strategy to deal with noise.
3 code implementations • 31 Dec 2017 • Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Chao Qian, Yang Yu
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions.
no code implementations • NeurIPS 2017 • Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou
The problem of selecting the best $k$-element subset from a universe is involved in many applications.
no code implementations • 20 Nov 2017 • Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou
To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems.
no code implementations • 2 Nov 2017 • Chao Qian, Chao Bian, Wu Jiang, Ke Tang
We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds.
no code implementations • 10 Jun 2016 • Chao Qian, Yang Yu, Zhi-Hua Zhou
Our results imply that the increase of population size, while usually desired in practice, bears the risk of increasing the lower bound of the running time and thus should be carefully considered.
no code implementations • NeurIPS 2015 • Chao Qian, Yang Yu, Zhi-Hua Zhou
Selecting the optimal subset from a large set of variables is a fundamental problem in various learning tasks such as feature selection, sparse regression, dictionary learning, etc.
no code implementations • 20 Nov 2013 • Chao Qian, Yang Yu, Zhi-Hua Zhou
On a representative problem where the noise has a strong negative effect, we examine two commonly employed mechanisms in EAs dealing with noise, the re-evaluation and the threshold selection strategies.