Summary of Linear Mode Connectivity in Differentiable Tree Ensembles, by Ryuichi Kanoh et al.
Linear Mode Connectivity in Differentiable Tree Ensembles
by Ryuichi Kanoh, Mahito Sugiyama
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper explores the phenomenon of Linear Mode Connectivity (LMC) in machine learning models, which refers to the consistent performance of linearly interpolated models in the parameter space. The authors achieve LMC for soft tree ensembles, a type of differentiable model used extensively in practice. To do so, they incorporate two invariances inherent to tree architectures: subtree flip invariance and splitting order invariance, in addition to permutation invariance of trees. Additionally, the authors demonstrate that it is possible to exclude these additional invariances while keeping LMC by designing decision list-based tree architectures. The findings highlight the importance of accounting for architecture-specific invariances in achieving LMC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models work together. It’s like building with blocks – if you can make a new block that is just an average of two old blocks, it should still work well. This is called Linear Mode Connectivity (LMC). The researchers looked at special kinds of blocks called soft tree ensembles and found a way to make them work well when combined in different ways. They did this by making sure the blocks had certain properties that don’t change even if you mix them up. This helps us understand how machine learning models can be used together, which is important for things like combining information from different sources. |
Keywords
» Artificial intelligence » Machine learning
