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Disentangled Graph Autoencoder for Treatment Effect Estimation
Di Fan, Renlei Jiang, Yunhao Wen +1
Treatment effect estimation from observational data has attracted significant attention across various research fields. However, many widely used methods rely on the unconfoundedne…
Learning Network Representations with Disentangled Graph Auto-Encoder
Di Fan, Chuanhou Gao
The (variational) graph auto-encoder is widely used to learn representations for graph-structured data. However, the formation of real-world graphs is a complicated and heterogeneo…
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
Di Fan, Yannian Kou, Chuanhou Gao
Disentangled representation learning aims to learn low-dimensional representations where each dimension corresponds to an underlying generative factor. While the Variational Auto-E…
Domain Knowledge integrated for Blast Furnace Classifier Design
Shaohan Chen, Di Fan, Chuanhou Gao
Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace syst…