Zobrazeno 1 - 10
of 291
pro vyhledávání: '"Xiao, Xiaokui"'
Publikováno v:
ICML2021
This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of $n$ arms with reward distributions supported on $[0,1]$ with unknown means. The arms in the stream are arriving one by o
Externí odkaz:
http://arxiv.org/abs/2410.17835
Influence maximization (IM) is a classic problem that aims to identify a small group of critical individuals, known as seeds, who can influence the largest number of users in a social network through word-of-mouth. This problem finds important applic
Externí odkaz:
http://arxiv.org/abs/2410.16603
Given a group size m and a sensitive dataset D, group privacy (GP) releases information about D with the guarantee that the adversary cannot infer with high confidence whether the underlying data is D or a neighboring dataset D' that differs from D b
Externí odkaz:
http://arxiv.org/abs/2408.09943
As a solution concept in cooperative game theory, Shapley value is highly recognized in model interpretability studies and widely adopted by the leading Machine Learning as a Service (MLaaS) providers, such as Google, Microsoft, and IBM. However, as
Externí odkaz:
http://arxiv.org/abs/2407.11359
Autor:
Wei, Jianxin, Zhu, Yizheng, Xiao, Xiaokui, Bao, Ergute, Yang, Yin, Cai, Kuntai, Ooi, Beng Chin
Graph Convolutional Networks (GCNs) are a popular machine learning model with a wide range of applications in graph analytics, including healthcare, transportation, and finance. Similar to other neural networks, a GCN may memorize parts of the traini
Externí odkaz:
http://arxiv.org/abs/2407.05034
Recent years have witnessed a growing trend toward employing deep reinforcement learning (Deep-RL) to derive heuristics for combinatorial optimization (CO) problems on graphs. Maximum Coverage Problem (MCP) and its probabilistic variant on social net
Externí odkaz:
http://arxiv.org/abs/2406.14697
Graph representation learning (GRL) is to encode graph elements into informative vector representations, which can be used in downstream tasks for analyzing graph-structured data and has seen extensive applications in various domains. However, the ma
Externí odkaz:
http://arxiv.org/abs/2406.13369
In this paper, we study cascading failures in power grids through the lens of information diffusion models. Similar to the spread of rumors or influence in an online social network, it has been observed that failures (outages) in a power grid can spr
Externí odkaz:
http://arxiv.org/abs/2406.08522
Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains challenging. Unlike traditiona
Externí odkaz:
http://arxiv.org/abs/2405.17934
Autor:
Ooi, Beng Chin, Cai, Shaofeng, Chen, Gang, Shen, Yanyan, Tan, Kian-Lee, Wu, Yuncheng, Xiao, Xiaokui, Xing, Naili, Yue, Cong, Zeng, Lingze, Zhang, Meihui, Zhao, Zhanhao
Publikováno v:
SCIENCE CHINA Information Sciences 67, 10 (2024)
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on en
Externí odkaz:
http://arxiv.org/abs/2405.03924