Order-based rehearsal learning learns sufficient order structures from observational data to make decisions avoiding undesired events, outperforming graph-based methods and matching oracle graph baselines in experiments.
arXiv preprint arXiv:2402.05052 , year=
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
A two-stage framework enables multimodal LLMs to learn shared latent representations from pairwise modality data and achieve cross-modal generation when incorporating new modalities.
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PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
MoRe identifies modular structure in representations themselves to enable principled reuse, alignment, and expansion of modules during continual adaptation on sequential data.
DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.
MOSAIC recovers identifiable latent variables and their sparse associated observations in scientific time series by combining temporal causal representation learning with support recovery through a sparse additive decoder.
The paper introduces a unified formulation for representation learning with task and constraint components, arguing for mutual benefits between causal and traditional approaches and showing via experiments that causal constraint effectiveness depends on paired tasks.
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Order-based Rehearsal Learning
Order-based rehearsal learning learns sufficient order structures from observational data to make decisions avoiding undesired events, outperforming graph-based methods and matching oracle graph baselines in experiments.
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MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
MOSAIC recovers identifiable latent variables and their sparse associated observations in scientific time series by combining temporal causal representation learning with support recovery through a sparse additive decoder.
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A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
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