Summary of Learning Under Singularity: An Information Criterion Improving Wbic and Sbic, by Lirui Liu and Joe Suzuki
Learning under Singularity: An Information Criterion improving WBIC and sBIC
by Lirui Liu, Joe Suzuki
First submitted to arxiv on: 20 Feb 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 The proposed Learning under Singularity (LS) Information Criterion enhances the functionality of Widely Applicable Bayes Information Criterion (WBIC) and Singular Bayesian Information Criterion (sBIC). LS demonstrates stability without regularity constraints and is effective in singular scenarios. The new criterion addresses limitations in WBIC’s application to large sample sizes and redundant estimation, while also improving sBIC’s broader applicability by incorporating the empirical loss from Widely Applicable Information Criterion (WAIC) and a penalty term similar to that of sBIC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to choose the best model is introduced. It helps fix problems with previous methods when dealing with big data sets or complex models. The new approach, called Learning under Singularity, makes it possible to select the best model without having to make assumptions about how well the model fits the data. This makes it a more flexible and reliable tool for researchers. |
