# Penalty Learning for Optimal Partitioning using Multilayer Perceptron

arXiv: https://arxiv.org/abs/2408.00856 · HTML: https://mlsanity.com/paper/2408.00856/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [On Optimal Multiple Changepoint Algorithms for Large Data](https://mlsanity.com/paper/1409.1842/) (2014)
- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [Maximum Margin Interval Trees](https://mlsanity.com/paper/1710.04234/) (2017)
- [An Efficient Training Algorithm for Kernel Survival Support Vector Machines](https://mlsanity.com/paper/1611.07054/) (2016)
- [Labeled Optimal Partitioning](https://mlsanity.com/paper/2006.13967/) (2020)
- [A changepoint approach for the identification of financial extreme regimes](https://mlsanity.com/paper/1902.09205/) (2019)

## Built on by (most cited first, 2 of 2)

- [Interval Regression: A Comparative Study with Proposed Models](https://mlsanity.com/paper/2503.02011/) (2025)
- [Learning Penalty for Optimal Partitioning via Automatic Feature Extraction](https://mlsanity.com/paper/2505.07413/) (2025)

## Abstract (arXiv)

Changepoint detection is a technique used to identify significant shifts in sequences and is widely used in fields such as finance, genomics, and medicine. To identify the changepoints, dynamic programming (DP) algorithms, particularly Optimal Partitioning (OP) family, are widely used. To control the changepoints count, these algorithms use a fixed penalty to penalize the changepoints presence. To predict the optimal value of that penalty, existing methods used simple models such as linear or tree-based, which may limit predictive performance. To address this issue, this study proposes using a multilayer perceptron (MLP) with a ReLU activation function to predict the penalty. The proposed model generates continuous predictions -- as opposed to the stepwise ones in tree-based models -- and handles non-linearity better than linear models. Experiments on large benchmark genomic datasets demonstrate that the proposed model improves accuracy and F1 score compared to existing models.