Summary of Improving Decision Sparsity, by Yiyang Sun et al.
Improving Decision Sparsity
by Yiyang Sun, Tong Wang, Cynthia Rudin
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 Machine learning models often rely on sparse representations to improve interpretability, but traditional measures of sparsity focus on the model’s global usage of variables. This limitation is addressed by expanding the concept of decision sparsity, specifically the Sparse Explanation Value (SEV), which considers movement along a hypercube towards a reference point. By introducing flexibility in this reference and mapping distances in feature space, SEV can provide more meaningful explanations for various function classes. The paper presents two variants of SEV, cluster-based and tree-based, as well as methods to improve explanation credibility and optimize decision sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting better at making decisions, but we need to understand how they make those decisions. One way is by looking at what features or variables contribute most to the outcome. This paper takes that idea a step further by creating a new way to measure how sparse these explanations are. Sparsity matters because it helps us trust the models more and see why they’re making certain decisions. The authors introduce two new methods, cluster-based and tree-based, to make these explanations even more meaningful and accurate. |
Keywords
* Artificial intelligence * Machine learning
