Summary of Predictive Inference with Fast Feature Conformal Prediction, by Zihao Tang et al.
Predictive Inference With Fast Feature Conformal Prediction
by Zihao Tang, Boyuan Wang, Chuan Wen, Jiaye Teng
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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 This paper introduces Fast Feature Conformal Prediction (FFCP), a novel approach that reduces the computational time required for conformal prediction in deep learning models. FFCP employs a non-conformity score and Taylor expansion to approximate nonlinear operations, achieving a 50x speedup compared to traditional methods while maintaining comparable performance. The proposed method is model-agnostic and distribution-free, making it suitable for uncertainty quantification. The authors demonstrate the effectiveness of FFCP through empirical validations, outperforming vanilla conformal prediction and existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is a widely used technique in deep learning to quantify uncertainty. Researchers have developed Feature Conformal Prediction (FCP) which reduces band lengths. However, FCP has limitations due to the time-consuming non-linear operations required. The new method, Fast Feature Conformal Prediction (FFCP), uses a novel score and Taylor expansion to speed up computations by 50x while maintaining performance. This makes it suitable for practical applications. |
Keywords
» Artificial intelligence » Deep learning
