Summary of Bayesian Joint Additive Factor Models For Multiview Learning, by Niccolo Anceschi and Federico Ferrari and David B. Dunson and Himel Mallick
Bayesian Joint Additive Factor Models for Multiview Learning
by Niccolo Anceschi, Federico Ferrari, David B. Dunson, Himel Mallick
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
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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 joint additive factor regression model (JAFAR) with a structured additive design, accounting for shared and view-specific components, tackles the challenge of combining multimodal information to improve the prediction of outcomes in precision medicine. The paper’s focus is on studying relationships between multiview features and responses, motivated by applications like multi-omics data correlation with clinical outcomes. To address varying signal-to-noise ratios across views, the authors introduce a novel dependent cumulative shrinkage process (D-CUSP) prior to ensure identifiability. They provide an efficient implementation via a partially collapsed Gibbs sampler and extend their approach to allow flexible feature and outcome distributions. The model’s performance is demonstrated on a real-world task, predicting time-to-labor onset from immunome, metabolome, and proteome data, showing gains against state-of-the-art competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to combine different types of data together to make better predictions in fields like medicine. They’re working with big datasets that have lots of different information about the same samples, like how genes are expressed and what proteins are present. The goal is to use this information to predict things like when someone will get sick or what treatment will work best. The problem is that each type of data has its own strengths and weaknesses, so they need a special way to combine them that takes these differences into account. The new method is called JAFAR, and it uses a combination of old and new ideas to make sure the results are accurate and easy to understand. |
Keywords
» Artificial intelligence » Precision » Regression
