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cs.LG2025
Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
Hans Jarett J. Ong, Brian Godwin S. Lim, Dominic Dayta +2
Unsupervised representation learning seeks to recover latent generative factors, yet standard methods relying on statistical independence often fail to capture causal dependencies.…
cs.LG2024
Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
Hans Jarett J. Ong, Brian Godwin S. Lim, Renzo Roel P. Tan +1
Effective causal discovery is essential for learning the causal graph from observational data. The linear non-Gaussian acyclic model (LiNGAM) operates under the assumption of a lin…
cs.LG2024
Contextualized Messages Boost Graph Representations
Brian Godwin Lim, Galvin Brice Sy Lim, Renzo Roel Tan +1
Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studie…