Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Fan, Zhenan"'
We present $\textit{Learn2Aggregate}$, a machine learning (ML) framework for optimizing the generation of Chv\'atal-Gomory (CG) cuts in mixed integer linear programming (MILP). The framework trains a graph neural network to classify useful constraint
Externí odkaz:
http://arxiv.org/abs/2409.06559
Autor:
Feng, Yuxi, Li, Raymond, Fan, Zhenan, Carenini, Giuseppe, Pourreza, Mohammadreza, Zhang, Weiwei, Zhang, Yong
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL
Externí odkaz:
http://arxiv.org/abs/2406.07913
Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natural language expressions of queries, without considering SQL queries,
Externí odkaz:
http://arxiv.org/abs/2403.16204
Autor:
Li, Xijun, Zhu, Fangzhou, Zhen, Hui-Ling, Luo, Weilin, Lu, Meng, Huang, Yimin, Fan, Zhenan, Zhou, Zirui, Kuang, Yufei, Wang, Zhihai, Geng, Zijie, Li, Yang, Liu, Haoyang, An, Zhiwu, Yang, Muming, Li, Jianshu, Wang, Jie, Yan, Junchi, Sun, Defeng, Zhong, Tao, Zhang, Yong, Zeng, Jia, Yuan, Mingxuan, Hao, Jianye, Yao, Jun, Mao, Kun
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVer
Externí odkaz:
http://arxiv.org/abs/2401.05960
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to en
Externí odkaz:
http://arxiv.org/abs/2401.03244
Autor:
Fan, Zhenan, Zhou, Zirui, Pei, Jian, Friedlander, Michael P., Hu, Jiajie, Li, Chengliang, Zhang, Yong
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge. Such knowledge i
Externí odkaz:
http://arxiv.org/abs/2208.07530
We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimizatio
Externí odkaz:
http://arxiv.org/abs/2201.11183
Federated learning is a popular technology for training machine learning models on distributed data sources without sharing data. Vertical federated learning or feature-based federated learning applies to the cases that different data sources share t
Externí odkaz:
http://arxiv.org/abs/2201.02658
Autor:
Fan, Zhenan, Fang, Huang, Zhou, Zirui, Pei, Jian, Friedlander, Michael P., Liu, Changxin, Zhang, Yong
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE)
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To su
Externí odkaz:
http://arxiv.org/abs/2109.09046
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learning models without disclosing their private data and model parameters.
Externí odkaz:
http://arxiv.org/abs/2109.08344