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)
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| 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
