1 code implementation • 29 May 2023 • Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi
How to train a generalizable meta-policy by continually learning a sequence of tasks?
1 code implementation • 11 Apr 2023 • Qiqi Duan, Chang Shao, Guochen Zhou, Haobin Yang, Qi Zhao, Yuhui Shi
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer optimization framework, on non-separable functions.
no code implementations • 12 Mar 2023 • Qi Zhao, Qiqi Duan, Bai Yan, Shi Cheng, Yuhui Shi
In the survey, we first present the concept of automated design of metaheuristic algorithms and provide a taxonomy by abstracting the automated design process into four parts, i. e., design space, design strategies, performance evaluation strategies, and targeted problems.
1 code implementation • 12 Mar 2023 • Qi Zhao, Bai Yan, Taiwei Hu, Xianglong Chen, Yuhui Shi
Metaheuristic algorithms are widely-recognized solvers for challenging optimization problems with multi-modality, discretization, large-scale, multi-objectivity, etc.
no code implementations • 10 Feb 2023 • Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin
In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.
1 code implementation • 12 Dec 2022 • Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yijun Yang, Qi Zhao, Yuhui Shi
In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO).
no code implementations • COLING 2022 • Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li
To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains.
1 code implementation • 27 Jul 2022 • Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives.
1 code implementation • 24 Jun 2022 • Lijun Sun, Yu-Cheng Chang, Chao Lyu, Ye Shi, Yuhui Shi, Chin-Teng Lin
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
no code implementations • 7 Apr 2022 • Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, Hongzhi Yin
In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.
1 code implementation • 3 Apr 2022 • Qi Zhao, Bai Yan, Xianglong Chen, Taiwei Hu, Shi Cheng, Yuhui Shi
However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family.
no code implementations • 25 Mar 2022 • Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, Hongzhi Yin
To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.
no code implementations • 3 Jan 2022 • Wenjian Luo, Xin Lin, Changhe Li, Shengxiang Yang, Yuhui Shi
This is very helpful for the decision makers, especially when facing changing environments.
no code implementations • ICLR 2022 • Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi
Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model.
no code implementations • 14 Jun 2021 • Qi Zhao, Bai Yan, Yuhui Shi
In many clustering scenes, data samples' attribute values change over time.
1 code implementation • 5 Jun 2021 • Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.
no code implementations • 27 May 2021 • Jian Yang, Yuhui Shi
Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values.
1 code implementation • 27 May 2021 • Jian Yang, Yuhui Shi
Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions.
no code implementations • 8 Jan 2020 • Yurui Ming, Dongrui Wu, Yu-Kai Wang, Yuhui Shi, Chin-Teng Lin
To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can be potentially generalized to other BCI cases.
1 code implementation • 23 Jan 2019 • Lijun Sun, Chao Lyu, Yuhui Shi
This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA).
Robotics Multiagent Systems
no code implementations • 12 Aug 2016 • Bin Liu, Shi Cheng, Yuhui Shi
Observing that the Student's t distribution has heavier and longer tails than the Gaussian, which may be beneficial for exploring the solution space, we propose a novel EDA algorithm termed ESTDA, in which the Student's t distribution, rather than Gaussian, is employed.
no code implementations • 12 Jun 2013 • Jun Sun, Xiao-Jun Wu, Vasile Palade, Wei Fang, Yuhui Shi
The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field.