Loading Now

Summary of Distpred: a Distribution-free Probabilistic Inference Method For Regression and Forecasting, by Daojun Liang et al.


DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting

by Daojun Liang, Haixia Zhang, Dongfeng Yuan

First submitted to arxiv on: 17 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)

     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
A novel approach called DistPred is proposed for regression and forecasting tasks, overcoming limitations of traditional methods. This method transforms proper scoring rules into a differentiable discrete form, serving as a loss function to train the model end-to-end. This allows the model to sample numerous samples in a single forward pass, estimating the potential distribution of the response variable. The approach outperforms existing methods on multiple datasets and significantly improves computational efficiency. For example, DistPred has a 180x faster inference speed compared to state-of-the-art models.
Low GrooveSquid.com (original content) Low Difficulty Summary
DistPred is a new way to do regression and forecasting tasks. It helps estimate how likely something is to happen. Traditional methods are limited because they only give point estimates and don’t show uncertainty. DistPred solves this problem by using scoring rules in a special way that allows it to generate many samples quickly. This makes it faster than other approaches. In tests, DistPred did better than existing methods on multiple datasets.

Keywords

» Artificial intelligence  » Inference  » Loss function  » Regression