DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
This addresses the need for efficient and accurate probabilistic inference in regression and forecasting tasks, offering a novel method that improves computational efficiency.
The paper tackles the problem of estimating uncertainty in regression and forecasting without relying on Gaussian assumptions or extensive sampling, proposing DistPred, which uses a differentiable scoring rule loss to enable single-pass sampling and achieves state-of-the-art performance with a 180x faster inference speed.
Traditional regression and prediction tasks often only provide deterministic point estimates. To estimate the distribution or uncertainty of the response variable, traditional methods either assume that the posterior distribution of samples follows a Gaussian process or require thousands of forward passes for sample generation. We propose a novel approach called DistPred for regression and forecasting tasks, which overcomes the limitations of existing methods while remaining simple and powerful. Specifically, we transform proper scoring rules that measure the discrepancy between the predicted distribution and the target distribution into a differentiable discrete form and use it as a loss function to train the model end-to-end. This allows the model to sample numerous samples in a single forward pass to estimate the potential distribution of the response variable. We have compared our method with several existing approaches on multiple datasets and achieved state-of-the-art performance. Additionally, our method significantly improves computational efficiency. For example, compared to state-of-the-art models, DistPred has a 180x faster inference speed Experimental results can be reproduced through https://github.com/Anoise/DistPred.