# A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming

arXiv: https://arxiv.org/abs/2401.08488 · HTML: https://mlsanity.com/paper/2401.08488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications](https://mlsanity.com/paper/2302.12487/) (2023)
- [A Survey of Approximate Quantile Computation on Large-scale Data (Technical Report)](https://mlsanity.com/paper/2004.08255/) (2020)

## Abstract (arXiv)

Generalized linear regressions, such as logistic regressions or Poisson regressions, are long-studied regression analysis approaches, and their applications are widely employed in various classification problems. Our study considers a stochastic generalized linear regression model as a stochastic problem with chance constraints and tackles it using nonconvex programming techniques. Clustering techniques and quantile estimation are also used to estimate random data's mean and variance-covariance matrix. Metrics for measuring the performance of logistic regression are used to assess the model's efficacy, including the F1 score, precision score, and recall score. The results of the proposed algorithm were over 1 to 2 percent better than the ordinary logistic regression model on the same dataset with the above assessment criteria.