# Robust Calibration For Improved Weather Prediction Under Distributional Shift

arXiv: https://arxiv.org/abs/2401.04144 · HTML: https://mlsanity.com/paper/2401.04144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 21 of 21)

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- [The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review](https://mlsanity.com/paper/1511.05263/) (2015)
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- [Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions](https://mlsanity.com/paper/2312.14212/) (2023)

## Abstract (arXiv)

In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the textttShifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift challenge. We find that by leveraging a mixture of experts in conjunction with an advanced data augmentation technique borrowed from the computer vision domain, in conjunction with robust textitpost-hoc calibration of predictive uncertainties, we can potentially achieve more accurate and better-calibrated results with deep neural networks than with boosted tree models for tabular data. We quantify our predictions using several metrics and propose several future lines of inquiry and experimentation to boost performance.