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Gaussian Partitioning for Reliable Learning of Multi-Index Models

Paper recorded by Signals 4 on 2026-11-03 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.LG · 机器学习 · first seen 2026-11-04

Abstract

We introduce Gaussian partitioning, an algorithmic primitive for distributions smooth with respect to a Gaussian. It partitions space into polyhedral regions so that almost all mass lies in thin strips restricting a few orthogonal directions. Within each strip, the conditional distribution approximately matches low-degree Gaussian moments in the unrestricted directions. Our construction builds on

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#14 most recent of 703 cs.LG papers we have recorded · ↑ newer: Stabilizing On-Policy Targeted Guidance in Agentic Reinforcement Learn · ↓ older: NovoCRF: Globally Normalized De Novo Peptide Sequencing on a Prefix-Ma
Cite this page: Gaussian Partitioning for Reliable Learning of Multi-Index Models: the #14 most recent of 703 cs.LG papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gaussian-partitioning-for-reliable-learning-of-multi-index-models.html
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