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Riemannian Simultaneous Inference for Tangent Vector Field Regression

Paper recorded by Signals 4 on 2026-09-18 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.LG · 机器学习 · first seen 2026-09-21

Abstract

We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary. Because responses at different points lie in different tangent spaces, the proposed kernel estimator first parallel transports nearby responses to the target tangent space and then forms a volume-corrected local average. We first derive its uniform second-order bias, finite-bandwidth covariance, an

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#19 most recent of 235 cs.LG papers we have recorded · ↑ newer: Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplac · ↓ older: Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven
Cite this page: Riemannian Simultaneous Inference for Tangent Vector Field Regression: the #19 most recent of 235 cs.LG papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/riemannian-simultaneous-inference-for-tangent-vector-field-regression.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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