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
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
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.
Defines Hausdorff-style and Wasserstein-style metrics on C-sets, proving the latter are convex relaxations of the former and computable as linear programs.
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 performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.
PCA scatterplots misleadingly indicate clusters in Kuehneotherium teeth data, whereas t-SNE and persistent homology detect a ring-like one-dimensional manifold, backed by a generative model of uniform sampling from a unit circle whose cosine distances follow an arcsine distribution.
Documents a practical PyTorch implementation of batched Sinkhorn iterations for the entropy-regularized Wasserstein loss introduced by Cuturi.
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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Hausdorff and Wasserstein metrics on graphs and other structured data
Defines Hausdorff-style and Wasserstein-style metrics on C-sets, proving the latter are convex relaxations of the former and computable as linear programs.
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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 performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
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Weighted quantization using MMD: From mean field to mean shift via gradient flows
Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.
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Beyond Explained Variance: A Cautionary Tale of PCA
PCA scatterplots misleadingly indicate clusters in Kuehneotherium teeth data, whereas t-SNE and persistent homology detect a ring-like one-dimensional manifold, backed by a generative model of uniform sampling from a unit circle whose cosine distances follow an arcsine distribution.
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Implementation of batched Sinkhorn iterations for entropy-regularized Wasserstein loss
Documents a practical PyTorch implementation of batched Sinkhorn iterations for the entropy-regularized Wasserstein loss introduced by Cuturi.