Summary of Conformalized High-density Quantile Regression Via Dynamic Prototypes-based Probability Density Estimation, by Batuhan Cengiz et al.
Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density Estimation
by Batuhan Cengiz, Halil Faruk Karagoz, Tufan Kumbasar
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 recent paper introduces a novel approach to quantile regression, addressing challenges posed by heteroscedastic, multimodal, or skewed data. The proposed method, conformalized high-density quantile regression, uses dynamically adaptive prototypes to optimize the set of quantization bins throughout training. This approach provides valid coverage guarantees and focuses on regions with the highest probability density. Experimental results across diverse datasets and dimensionalities demonstrate improved prediction regions, enhanced coverage, and robustness, while utilizing fewer prototypes and memory. The code is available at https://github.com/batuceng/max_quantile. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to do quantile regression, which helps with tricky data problems. It’s called conformalized high-density quantile regression, and it uses special prototypes that change during training. This method makes sure the predictions are accurate and provides guarantees about how good they will be. The results show that this approach works well on different kinds of datasets and is better than other methods at handling complex data. |
Keywords
* Artificial intelligence * Probability * Quantization * Regression
