Loading Now

Summary of Distributional Refinement Network: Distributional Forecasting Via Deep Learning, by Benjamin Avanzi et al.


Distributional Refinement Network: Distributional Forecasting via Deep Learning

by Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong

First submitted to arxiv on: 3 Jun 2024

Categories

  • Main: Machine Learning (stat.ML)
  • Secondary: Machine Learning (cs.LG); Risk Management (q-fin.RM); Methodology (stat.ME)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The proposed Distributional Refinement Network (DRN) addresses challenges in classic (distributional) regression approaches like Generalized Linear Models (GLMs) by combining an inherently interpretable baseline model with a flexible neural network. The DRN refines the entire baseline distribution, capturing varying effects of features across all quantiles and improving predictive performance while maintaining adequate interpretability. The approach is inspired by the Combined Actuarial Neural Network (CANN) and demonstrates superior distributional forecasting capacity using both synthetic and real-world data.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper proposes a new model that combines two different approaches to improve actuarial modelling. It uses a baseline model like GLMs, but also adds a neural network to make the predictions more accurate. This combined approach is called the Distributional Refinement Network (DRN). The DRN does a better job of predicting how often certain events will happen than other models do. This could be useful for making decisions about insurance and risk management.

Keywords

» Artificial intelligence  » Neural network  » Regression