Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Zhu, Shixiang"'
The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods. However, existing approaches often produce overly con
Externí odkaz:
http://arxiv.org/abs/2411.12193
Time series data are crucial across diverse domains such as finance and healthcare, where accurate forecasting and decision-making rely on advanced modeling techniques. While generative models have shown great promise in capturing the intricate dynam
Externí odkaz:
http://arxiv.org/abs/2410.13986
Autor:
Zheng, Minxing, Zhu, Shixiang
Generative models have shown significant promise in critical domains such as medical diagnosis, autonomous driving, and climate science, where reliable decision-making hinges on accurate uncertainty quantification. While probabilistic conformal predi
Externí odkaz:
http://arxiv.org/abs/2410.13735
Autor:
Li, Michael Lingzhi, Zhu, Shixiang
The surge in data availability has inundated decision-makers with an overwhelming array of choices. While existing approaches focus on optimizing decisions based on quantifiable metrics, practical decision-making often requires balancing measurable q
Externí odkaz:
http://arxiv.org/abs/2409.11535
Modeling and analysis for event series generated by heterogeneous users of various behavioral patterns are closely involved in our daily lives, including credit card fraud detection, online platform user recommendation, and social network analysis. T
Externí odkaz:
http://arxiv.org/abs/2407.05625
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
Autor:
Chen, Shuyi, Zhu, Shixiang
Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite substantial resea
Externí odkaz:
http://arxiv.org/abs/2403.17852
Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public policymaking is hindered by the
Externí odkaz:
http://arxiv.org/abs/2310.18449
Energy justice is a growing area of interest in interdisciplinary energy research. However, identifying systematic biases in the energy sector remains challenging due to confounding variables, intricate heterogeneity in counterfactual effects, and li
Externí odkaz:
http://arxiv.org/abs/2310.03258
Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification. However, their effectiveness can be hindered by the challenges arising from the widespread existence of n
Externí odkaz:
http://arxiv.org/abs/2306.08210