Summary of Calibrating Bayesian Learning Via Regularization, Confidence Minimization, and Selective Inference, by Jiayi Huang et al.
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
by Jiayi Huang, Sangwoo Park, Osvaldo Simeone
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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 A novel approach to improve the reliability of artificial intelligence (AI) models in engineering applications involves integrating calibration regularization with variational inference-based Bayesian learning. This method, called selective CBNN-OCM, combines calibration-regularized Bayesian learning (CBNN), out-of-distribution confidence minimization (OCM), and selective calibration to enhance both in-distribution (ID) performance and out-of-distribution (OOD) detection. The proposed scheme is constructed by introducing CBNN, incorporating OCM, and then integrating selective calibration to produce SCBNN-OCM. This approach rejects inputs with insufficient calibration performance, achieving the best ID and OOD performance compared to existing state-of-the-art methods at the cost of rejecting a large number of inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models in engineering applications struggle with quantifying their reliability due to difficulty in detecting out-of-distribution (OOD) inputs. To improve calibration, researchers proposed Bayesian ensembling, but this approach is limited by computational constraints and model misspecification. A new method combines variational inference-based Bayesian learning with calibration regularization, confidence minimization, and selective calibration to enhance both ID performance and OOD detection. This approach rejects inputs with insufficient calibration performance, achieving the best results compared to existing methods. |
Keywords
» Artificial intelligence » Inference » Regularization
