Zobrazeno 1 - 10
of 4 680
pro vyhledávání: '"Cheng, Guang"'
Autor:
Suh, Namjoon, Yang, Yuning, Hsieh, Din-Yin, Luan, Qitong, Xu, Shirong, Zhu, Shixiang, Cheng, Guang
In this paper, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations, the heterogeneous nature of the feature in the table has been one of the main obstacles
Externí odkaz:
http://arxiv.org/abs/2406.16028
The evaluation of synthetic data generation is crucial, especially in the retail sector where data accuracy is paramount. This paper introduces a comprehensive framework for assessing synthetic retail data, focusing on fidelity, utility, and privacy.
Externí odkaz:
http://arxiv.org/abs/2406.13130
The promise of tabular generative models is to produce realistic synthetic data that can be shared and safely used without dangerous leakage of information from the training set. In evaluating these models, a variety of methods have been proposed to
Externí odkaz:
http://arxiv.org/abs/2406.13012
Generative Foundation Models (GFMs) have produced synthetic data with remarkable quality in modalities such as images and text. However, applying GFMs to tabular data poses significant challenges due to the inherent heterogeneity of table features. E
Externí odkaz:
http://arxiv.org/abs/2406.04619
Recommender systems play a crucial role in internet economies by connecting users with relevant products or services. However, designing effective recommender systems faces two key challenges: (1) the exploration-exploitation tradeoff in balancing ne
Externí odkaz:
http://arxiv.org/abs/2406.04374
Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substa
Externí odkaz:
http://arxiv.org/abs/2405.16876
Autor:
Yu, Peiyu, Zhang, Dinghuai, He, Hengzhi, Ma, Xiaojian, Miao, Ruiyao, Lu, Yifan, Zhang, Yasi, Kong, Deqian, Gao, Ruiqi, Xie, Jianwen, Cheng, Guang, Wu, Ying Nian
Offline Black-Box Optimization (BBO) aims at optimizing a black-box function using the knowledge from a pre-collected offline dataset of function values and corresponding input designs. However, the high-dimensional and highly-multimodal input design
Externí odkaz:
http://arxiv.org/abs/2405.16730
Autor:
Wang, Chi-Hua, Cheng, Guang
We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks. Backdoor attacks involve embedding malicious triggers into a training dataset to disrupt the model's
Externí odkaz:
http://arxiv.org/abs/2405.15979
With the proliferation of generative AI and the increasing volume of generative data (also called as synthetic data), assessing the fidelity of generative data has become a critical concern. In this paper, we propose a discriminative approach to esti
Externí odkaz:
http://arxiv.org/abs/2405.15337
In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity, and also
Externí odkaz:
http://arxiv.org/abs/2405.14018