Summary of M-dew: Extending Dynamic Ensemble Weighting to Handle Missing Values, by Adam Catto et al.
M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values
by Adam Catto, Nan Jia, Ansaf Salleb-Aouissi, Anita Raja
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach to missing value imputation for machine learning problems. The authors argue that treating the imputation model and downstream task model together and optimizing over full pipelines will lead to better results than treating them separately. They introduce M-DEW, a dynamic ensemble weighting approach that constructs two-stage imputation-prediction pipelines, trains each component separately, and dynamically calculates pipeline weights during inference time. The authors demonstrate that M-DEW outperforms state-of-the-art techniques in 17 out of 18 experiments, with statistically significant reductions in model perplexity and average precision improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to deal with missing data in machine learning. Right now, people usually do imputation (filling in the blanks) separately from their main task, like classifying or regressing. But what if we did both together? The authors think this will be better and came up with a new way called M-DEW. It’s like building a bridge between imputation and prediction. They tested it on lots of data and it worked really well, making predictions more accurate. |
Keywords
» Artificial intelligence » Inference » Machine learning » Perplexity » Precision
