Zobrazeno 1 - 10
of 2 233
pro vyhledávání: '"Wang,Yining"'
Autor:
Meng, Rui, Zhao, Hangyu, Xu, Bingxuan, Wang, Yining, Xu, Xiaodong, Lv, Suyu, Tao, Xiaofeng, Zhang, Ping
Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT
Externí odkaz:
http://arxiv.org/abs/2411.08628
In the trading process, financial signals often imply the time to buy and sell assets to generate excess returns compared to a benchmark (e.g., an index). Alpha is the portion of an asset's return that is not explained by exposure to this benchmark,
Externí odkaz:
http://arxiv.org/abs/2410.18448
Role-play in the Large Language Model (LLM) is a crucial technique that enables models to adopt specific perspectives, enhancing their ability to generate contextually relevant and accurate responses. By simulating different roles, theis approach imp
Externí odkaz:
http://arxiv.org/abs/2409.13979
In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints. Given the current resource inventory levels, the retailer makes an assortment decision at ea
Externí odkaz:
http://arxiv.org/abs/2407.05564
Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become
Externí odkaz:
http://arxiv.org/abs/2407.02539
Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning. In this work, we consider the problem of optimizing second-order smooth and strongly convex functions where the algo
Externí odkaz:
http://arxiv.org/abs/2406.19617
In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic de
Externí odkaz:
http://arxiv.org/abs/2406.09182
Recent studies have noted an intriguing phenomenon termed Neural Collapse, that is, when the neural networks establish the right correlation between feature spaces and the training targets, their last-layer features, together with the classifier weig
Externí odkaz:
http://arxiv.org/abs/2405.05587
Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing interest in designing \emph{locally private} linear contextual bandit algor
Externí odkaz:
http://arxiv.org/abs/2404.09413
Autor:
Miao, Sentao, Wang, Yining
This paper proposes a practically efficient algorithm with optimal theoretical regret which solves the classical network revenue management (NRM) problem with unknown, nonparametric demand. Over a time horizon of length $T$, in each time period the r
Externí odkaz:
http://arxiv.org/abs/2404.04467