# Linear Regression with Unknown Truncation Beyond Gaussian Features

arXiv: https://arxiv.org/abs/2602.12534 · HTML: https://mlsanity.com/paper/2602.12534/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2026.

## Built on by (most cited first, 3 of 3)

- [Surprises in Proper Positive-Only Learning](https://mlsanity.com/paper/2606.28309/) (2026)
- [Efficient Learning of Truncated Boolean Product Distributions: Influence to the Rescue](https://mlsanity.com/paper/2607.22889/) (2026)
- [Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring](https://mlsanity.com/paper/2610.12538/) (2026)

## Abstract (arXiv)

In truncated linear regression, samples (x,y) are shown only when the outcome y falls inside a certain survival set S^star and the goal is to estimate the unknown d-dimensional regressor w^star. This problem has a long history of study in Statistics and Machine Learning going back to the works of (Galton, 1897; Tobin, 1958) and more recently in, e.g., (Daskalakis et al., 2019; 2021; Lee et al., 2023; 2024). Despite this long history, however, most prior works are limited to the special case where S^star is precisely known. The more practically relevant case, where S^star is unknown and must be learned from data, remains open: indeed, here the only available algorithms require strong assumptions on the distribution of the feature vectors (e.g., Gaussianity) and, even then, have a dpoly (1/varepsilon) run time for achieving varepsilon accuracy. In this work, we give the first algorithm for truncated linear regression with unknown survival set that runs in poly (d/varepsilon) time, by only requiring that the feature vectors are sub-Gaussian. Our algorithm relies on a novel subroutine for efficiently learning unions of a bounded number of intervals using access to positive examples (without any negative examples) under a certain smoothness condition. This learning guarantee adds to the line of works on positive-only PAC learning and may be of independent interest.