# High-dimensional sparse classification using exponential weighting with empirical hinge loss

arXiv: https://arxiv.org/abs/2312.12952 · HTML: https://mlsanity.com/paper/2312.12952/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 10 of 10)

- [A sparse PAC-Bayesian approach for high-dimensional quantile prediction](https://mlsanity.com/paper/2409.01687/) (2024)
- [High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior](https://mlsanity.com/paper/2505.08288/) (2025)
- [Optimal sparse phase retrieval via a quasi-Bayesian approach](https://mlsanity.com/paper/2504.09509/) (2025)
- [Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance](https://mlsanity.com/paper/2405.19016/) (2024)
- [Misclassification excess risk bounds for PAC-Bayesian classification via convexified loss](https://mlsanity.com/paper/2408.08675/) (2024)
- [High-dimensional prediction for count response via sparse exponential weights](https://mlsanity.com/paper/2410.15381/) (2024)
- [Kullback-Leibler excess risk bounds for exponential weighted aggregation in Generalized linear models](https://mlsanity.com/paper/2504.10171/) (2025)
- [Sparse classification with positive-confidence data in high dimensions](https://mlsanity.com/paper/2512.24443/) (2025)
- [Robust low-rank estimation with multiple binary responses using pairwise AUC loss](https://mlsanity.com/paper/2601.08618/) (2026)
- [Mixture-of-experts Wishart model for covariance matrices with an application to Cancer drug screening](https://mlsanity.com/paper/2602.13888/) (2026)

## Abstract (arXiv)

In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights and empirical hinge loss. Through the employment of a suitable sparsity-inducing prior distribution, we demonstrate that our method yields favorable theoretical results on prediction error. The efficiency of our procedure is achieved through the utilization of Langevin Monte Carlo, a gradient-based sampling approach. To illustrate the effectiveness of our approach, we conduct comparisons with the logistic Lasso on simulated data and a real dataset. Our method frequently demonstrates superior performance compared to the logistic Lasso.