Boosting Methods for Interval-Censored Data with Regression and Classification

February 14, 2026 · View on GitHub

Yuan Bian, Grace Y. Yi, and Wenqing He (2025).
Boosting methods for interval-censored data with regression and classification.
In The 13th International Conference on Learning Representations (ICLR 2025).
https://openreview.net/pdf?id=DzbUL4AJPP

Overview

This repository provides the implementation of the proposed L2L_2 boosting methods for interval-censored data, supporting both regression and classification settings. It also includes scripts for data simulation and example usage.

Repository Structure

Setup

  • install_packages.R
    Installs all required R packages for running the code.

Core Algorithms

  • L2BOOST-CUT.R
    Implements the core function for the L2L_2Boost-CUT algorithm.

  • L2BOOST-IMP.R
    Implements the core function for the L2L_2Boost-IMP algorithm.

Utilities

  • help_funcs.R
    Contains helper functions for data simulation and supporting computations.

Example

  • example.R
    Demonstrates how to simulate interval-censored data and apply the proposed algorithms.

Reproducing Results

To replicate the numerical results reported in the paper:

  1. Install required packages by running install_packages.R.
  2. Set the simulation parameters in example.R according to the configurations described in the paper.
  3. Run example.R repeatedly (e.g., across multiple seeds).
  4. For large-scale simulations, parallel execution on a computing cluster is recommended.

Citation

If you use this code in your research, please cite:

Bian, Y., Yi, G. Y., & He, W. (2025). Boosting methods for interval-censored data with regression and classification. In the 13th International Conference on Learning Representations.