# Post-selection inference with HSIC-Lasso

arXiv: https://arxiv.org/abs/2010.15659 · HTML: https://mlsanity.com/paper/2010.15659/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Efficient Aggregated Kernel Tests using Incomplete U-statistics](https://mlsanity.com/paper/2206.09194/) (2022)
- [KSD Aggregated Goodness-of-fit Test](https://mlsanity.com/paper/2202.00824/) (2022)
- [MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence](https://mlsanity.com/paper/2405.18786/) (2024)
- [Enabling tabular deep learning when d gg n with an auxiliary knowledge graph](https://mlsanity.com/paper/2306.04766/) (2023)

## Abstract (arXiv)

Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning. Variable selection methods have thus been gaining much attention as well as post-selection inference. Indeed, the selected features can be significantly flawed when the selection procedure is not accounted for. We propose a selective inference procedure using the so-called model-free "HSIC-Lasso" based on the framework of truncated Gaussians combined with the polyhedral lemma. We then develop an algorithm, which allows for low computational costs and provides a selection of the regularisation parameter. The performance of our method is illustrated by both artificial and real-world data based experiments, which emphasise a tight control of the type-I error, even for small sample sizes.