Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Hu, Haoyuan"'
Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potenti
Externí odkaz:
http://arxiv.org/abs/2406.12349
Real-world scenarios frequently involve multi-objective data-driven optimization problems, characterized by unknown problem coefficients and multiple conflicting objectives. Traditional two-stage methods independently apply a machine learning model t
Externí odkaz:
http://arxiv.org/abs/2406.00779
Autor:
Gao, Yuan, Zhu, Yiheng, Cao, Yuanbin, Zhou, Yinzhi, Wu, Zhen, Chen, Yujie, Wu, Shenglan, Hu, Haoyuan, Dai, Xinyu
Open Domain Multi-Hop Question Answering (ODMHQA) plays a crucial role in Natural Language Processing (NLP) by aiming to answer complex questions through multi-step reasoning over retrieved information from external knowledge sources. Recently, Large
Externí odkaz:
http://arxiv.org/abs/2403.12393
Autor:
Lu, Minfang, Jiang, Yuchen, Dong, Huihui, Li, Qi, Xu, Ziru, Liu, Yuanlin, Wu, Lixia, Hu, Haoyuan, Zhu, Han, Jiang, Yuning, Xu, Jian, Zheng, Bo
In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item a
Externí odkaz:
http://arxiv.org/abs/2310.15492
Autor:
Mao, Xiaowei, Wen, Haomin, Zhang, Hengrui, Wan, Huaiyu, Wu, Lixia, Zheng, Jianbin, Hu, Haoyuan, Lin, Youfang
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service route of a worker given his current task pool, has received rising attention in recent years. Deep neural networks based on supervised learning have emerged as th
Externí odkaz:
http://arxiv.org/abs/2307.16246
Autor:
Wu, Lixia, Wen, Haomin, Hu, Haoyuan, Mao, Xiaowei, Xia, Yutong, Shan, Ergang, Zhen, Jianbin, Lou, Junhong, Liang, Yuxuan, Yang, Liuqing, Zimmermann, Roger, Lin, Youfang, Wan, Huaiyu
Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery data
Externí odkaz:
http://arxiv.org/abs/2306.10675
Autor:
Wu, Lixia, Liu, Jianlin, Lou, Junhong, Hu, Haoyuan, Zheng, Jianbin, Wen, Haomin, Song, Chao, He, Shu
Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistic
Externí odkaz:
http://arxiv.org/abs/2304.01559
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints. The online RAP is a 0-1 integer linear programming problem where the resource consumption coefficients are revealed column by column along with the co
Externí odkaz:
http://arxiv.org/abs/2303.03254
Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising
Externí odkaz:
http://arxiv.org/abs/2211.12105
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized online algorithms for the monotone continuous DR-submodular maximizati
Externí odkaz:
http://arxiv.org/abs/2208.08681