Establishes component-wise identifiability guarantees for partially shared causal latents in multimodal nonlinear mixing and introduces a differentiable Wasserstein-based module for recovery.
Thirty-seventh Conference on Neural Information Processing Systems , year=
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Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
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Identifiable Multimodal Causal Representation Learning under Partial Latent Sharing
Establishes component-wise identifiability guarantees for partially shared causal latents in multimodal nonlinear mixing and introduces a differentiable Wasserstein-based module for recovery.
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Content-Style Identification via Differential Independence
Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.