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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A discrete-time constant flux condition on the heat equation forces the domain to be a ball under suitable regularity.
A nonlinear observer on SL(3) achieves local exponential convergence for homography estimation by minimizing an image-intensity cost function with explicit non-degeneracy conditions.
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.
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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A discrete-time overdetermined problem for the heat equation
A discrete-time constant flux condition on the heat equation forces the domain to be a ball under suitable regularity.
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Equivariant Observer Design on SL(3) for Image Intensity-Based Homography Estimation
A nonlinear observer on SL(3) achieves local exponential convergence for homography estimation by minimizing an image-intensity cost function with explicit non-degeneracy conditions.
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
LeJEPA derives an optimal isotropic Gaussian target for embeddings and enforces it via sketched regularization to deliver scalable, heuristics-free self-supervised pretraining with 79% ImageNet linear accuracy on ViT-H/14.