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pro vyhledávání: '"Wang, Chendi"'
A recent study by De et al. (2022) has reported that large-scale representation learning through pre-training on a public dataset significantly enhances differentially private (DP) learning in downstream tasks, despite the high dimensionality of the
Externí odkaz:
http://arxiv.org/abs/2405.08920
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such randomness is difficult to take into account when proving differential pri
Externí odkaz:
http://arxiv.org/abs/2310.19973
Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a pro
Externí odkaz:
http://arxiv.org/abs/2301.08844
Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's dataset with respect to a given buyer's machine learning task, also known
Externí odkaz:
http://arxiv.org/abs/2212.11468
Publikováno v:
In Building and Environment 1 November 2024 265
Publikováno v:
In Atmospheric Research 15 October 2024 309
Publikováno v:
In Sustainable Cities and Society March 2024 102
Autor:
Qin, Xiang, Ji, Yunkang, Nong, Lijuan, Wang, Chendi, Li, Huiting, Xie, Chunyu, Ji, Lijun, Zhu, Aiping
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 5 June 2023 666
Autor:
Wang, Chendi, Leung, Mei-yung
Publikováno v:
In Building and Environment 1 June 2023 237
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