1 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
Deconfounded Time Series Forecasting: A Causal Inference Approach
Wentao Gao, Xiaojing Du, Wenjun Yu +3
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current…
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
Wentao Gao, Ziqi Xu, Jiuyong Li +6
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting be…
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
Xiaojing Du, Feiyu Yang, Wentao Gao +1
As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal…
Estimating Peer Direct and Indirect Effects in Observational Network Data
Xiaojing Du, Jiuyong Li, Debo Cheng +3
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Many alg…
Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
Wentao Gao, Jiuyong Li, Debo Cheng +7
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, the GCM Outputs exhibit systematic biases due to model unce…
Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
Wentao Gao, Omid Tavallaie, Shuaijun Chen +1
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without shari…