# Learning bounded subsets of Lp

arXiv: https://arxiv.org/abs/2002.01182 · HTML: https://mlsanity.com/paper/2002.01182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Distribution-Free Robust Linear Regression](https://mlsanity.com/paper/2102.12919/) (2021)

## Abstract (arXiv)

We study learning problems in which the underlying class is a bounded subset of Lp and the target Y belongs to Lp. Previously, minimax sample complexity estimates were known under such boundedness assumptions only when p=∞. We present a sharp sample complexity estimate that holds for any p > 4. It is based on a learning procedure that is suited for heavy-tailed problems.