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Intervening to Learn and Compose Causally Disentangled Representations
Alex Markham, Isaac Hirsch, Jeri A. Chang +2
In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this pa…
stat.ML2025
Towards Interpretable Deep Generative Models via Causal Representation Learning
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art perform…