The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie
5 Pith papers cite this work. Polarity classification is still indexing.
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Modern text encoders resist second-order collapse under mean pooling because token embeddings concentrate tightly within texts, and this resistance correlates with stronger downstream performance.
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
SPIN improves posterior inference under model misspecification in SBI by learning parameter-relevant information-preserving domain transfers from unpaired unlabeled real-world data.
PCA suggested clustering in fossil teeth data on a nonlinear manifold, but t-SNE and persistent homology show a ring structure with no clustering, supported by a unit-circle generative model whose arcsine distance distribution matches observations qualitatively.
citing papers explorer
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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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Why Mean Pooling Works: Quantifying Second-Order Collapse in Text Embeddings
Modern text encoders resist second-order collapse under mean pooling because token embeddings concentrate tightly within texts, and this resistance correlates with stronger downstream performance.
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Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
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Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference
SPIN improves posterior inference under model misspecification in SBI by learning parameter-relevant information-preserving domain transfers from unpaired unlabeled real-world data.
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Beyond Explained Variance: A Cautionary Tale of PCA
PCA suggested clustering in fossil teeth data on a nonlinear manifold, but t-SNE and persistent homology show a ring structure with no clustering, supported by a unit-circle generative model whose arcsine distance distribution matches observations qualitatively.