Summary of On the Correctness Of the Generalized Isotonic Recursive Partitioning Algorithm, by Joong-ho Won and Jihan Jung
On the Correctness of the Generalized Isotonic Recursive Partitioning Algorithm
by Joong-Ho Won, Jihan Jung
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 an improved Generalized Isotonic Recursive Partitioning (GIRP) algorithm for fitting isotonic models under separable convex losses, building upon previous work by Luss and Rosset (2014) and Painsky and Rosset (2016). The GIRP algorithm has the attractive property of maintaining isotonicity at each step. However, the authors show that the original algorithm may not always produce an isotonic model, highlighting the need for careful consideration of solution existence and uniqueness. They then present a modified GIRP algorithm that ensures correctness and preserves isotonicity throughout the recursive binary partitioning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper fixes a problem with the Generalized Isotonic Recursive Partitioning (GIRP) algorithm. The original algorithm is used to fit models, but it doesn’t always work correctly. The authors show an example where the algorithm fails and then explain how they fixed the problem by making some small changes. Now the algorithm produces correct results and keeps working as expected. |
