Summary of On Uncertainty Quantification For Near-bayes Optimal Algorithms, by Ziyu Wang et al.
On Uncertainty Quantification for Near-Bayes Optimal Algorithms
by Ziyu Wang, Chris Holmes
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Machine learning algorithms can be crucial in safety-critical applications, but constructing their Bayesian counterparts is challenging. Our research proposes an approach to address this issue by leveraging the efficiency of commonly used ML algorithms across various tasks. We prove that it’s possible to recover the Bayesian posterior defined by an unknown task distribution using a martingale posterior built from the algorithm. Additionally, we provide a practical uncertainty quantification method applicable to general ML algorithms. Our experiments demonstrate the efficacy of our approach with non-neural network and neural network algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is important in many areas, but making sure it’s safe is crucial. One way to do this is by using Bayesian models, which help us understand how certain our predictions are. However, most machine learning algorithms aren’t easy to turn into Bayesian models. Our new approach makes it possible to build a Bayesian model from any machine learning algorithm, as long as it works well across many different tasks. We tested our method with various types of algorithms and showed that it’s effective. |
Keywords
* Artificial intelligence * Machine learning * Neural network
