1 citations · 1 across the 2 of their papers we have counts for
7 papers · 1 filter
Scalable Variational Causal Discovery Unconstrained by Acyclicity
Nu Hoang, Bao Duong, Thin Nguyen
Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represen…
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Nu Hoang, Bao Duong, Thin Nguyen
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility con…
Domain Generalisation via Risk Distribution Matching
Toan Nguyen, Kien Do, Bao Duong +1
We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distrib…
Differentiable Bayesian Structure Learning with Acyclicity Assurance
Quang-Duy Tran, Phuoc Nguyen, Bao Duong +1
Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achievi…
Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent…
Conditional Independence Testing via Latent Representation Learning
Bao Duong, Thin Nguyen
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms. In this study, we introduce LCIT…