# Distributionally Robust Safe Screening

arXiv: https://arxiv.org/abs/2404.16328 · HTML: https://mlsanity.com/paper/2404.16328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Memory Replay with Data Compression for Continual Learning](https://mlsanity.com/paper/2202.06592/) (2022)
- [Mind the duality gap: safer rules for the Lasso](https://mlsanity.com/paper/1505.03410/) (2015)
- [Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling](https://mlsanity.com/paper/1602.02485/) (2016)
- [GAP Safe screening rules for sparse multi-task and multi-class models](https://mlsanity.com/paper/1506.03736/) (2015)
- [Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining](https://mlsanity.com/paper/1602.04548/) (2016)
- [Safe Sample Screening for Robust Support Vector Machine](https://mlsanity.com/paper/1912.11217/) (2019)
- [Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems](https://mlsanity.com/paper/1504.02870/) (2015)
- [Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning](https://mlsanity.com/paper/2306.13561/) (2023)

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

- [Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation](https://mlsanity.com/paper/2502.12607/) (2025)

## Abstract (arXiv)

In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are subject to uncertainty within a predetermined range. By extending the SS technique to accommodate this weight uncertainty, the DRSS method is capable of reliably identifying unnecessary samples and features under any future distribution within a specified range. We provide a theoretical guarantee of the DRSS method and validate its performance through numerical experiments on both synthetic and real-world datasets.