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pro vyhledávání: '"Guo Zijian"'
Data from multiple environments offer valuable opportunities to uncover causal relationships among variables. Leveraging the assumption that the causal outcome model remains invariant across heterogeneous environments, state-of-the-art methods attemp
Externí odkaz:
http://arxiv.org/abs/2412.11850
Statistical Inference in High-dimensional Poisson Regression with Applications to Mediation Analysis
Autor:
Rakshit, Prabrisha, Guo, Zijian
Large-scale datasets with count outcome variables are widely present in various applications, and the Poisson regression model is among the most popular models for handling count outcomes. This paper considers the high-dimensional sparse Poisson regr
Externí odkaz:
http://arxiv.org/abs/2410.20671
The quantification and inference of predictive importance for exposure covariates have recently gained significant attention in the context of interpretable machine learning. Contemporary scientific investigations often involve data originating from
Externí odkaz:
http://arxiv.org/abs/2409.07380
Despite recent advances in transfer learning with multiple source data sets, there still lacks developments for mixture target populations that could be approximated through a composite of the sources due to certain key factors like ethnicity in prac
Externí odkaz:
http://arxiv.org/abs/2407.20073
Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that is (A1) associated with the exposure, (A2) has no direct effect
Externí odkaz:
http://arxiv.org/abs/2407.19558
Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estima
Externí odkaz:
http://arxiv.org/abs/2405.06763
Autor:
Jing, Longlong, Yu, Ruichi, Chen, Xu, Zhao, Zhengli, Sheng, Shiwei, Graber, Colin, Chen, Qi, Li, Qinru, Wu, Shangxuan, Deng, Han, Lee, Sangjin, Sweeney, Chris, He, Qiurui, Hung, Wei-Chih, He, Tong, Zhou, Xingyi, Moussavi, Farshid, Guo, Zijian, Zhou, Yin, Tan, Mingxing, Yang, Weilong, Li, Congcong
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in
Externí odkaz:
http://arxiv.org/abs/2405.00236
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real
Externí odkaz:
http://arxiv.org/abs/2402.17217
Many high-dimensional data sets suffer from hidden confounding which affects both the predictors and the response of interest. In such situations, standard regression methods or algorithms lead to biased estimates. This paper substantially extends pr
Externí odkaz:
http://arxiv.org/abs/2312.02860
Publikováno v:
E3S Web of Conferences, Vol 294, p 06001 (2021)
There will be total suspended solids in port dredging, which will affect aquatic organisms and water quality. Therefore, with the Green Port becoming the core concept of port development, it is necessary to consider the impact of dredging on marine e
Externí odkaz:
https://doaj.org/article/1ff9f3bb340147208618a0c0db72b13a
