In the causal inference literature, this generalization task has been formalized under the rubric oftransportability (Pearl and Bareinboim, 2011), where a number of criteria and algorithms have been developed forvarious settings.

Comparing images above and below the green line in every figure clearly shows the failure in generating coherent samples whose digit identities are supposed to match to conditioned images.

Multi-View Representation Learning (MVRL) aims to learn a shared representation of multiple observations from different types of views.

Amidst therecent strides inevaluating LargeLanguage Models forCode (Code LLMs), existing benchmarks havemainly focused onthefunctional correctness of generated code, neglecting the importance of their computational efficiency.

Ouranalysis culminates inshowing thatthere exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of eO(1/3 n) in L1 error.