Summary of Efficient Decision Trees For Tensor Regressions, by Hengrui Luo et al.
Efficient Decision Trees for Tensor Regressions
by Hengrui Luo, Akira Horiguchi, Li Ma
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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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 tensor-input tree (TT) method tackles scalar-on-tensor and tensor-on-tensor regression issues by developing novel models and algorithms. For scalar-on-tensor problems, a regression tree model is designed with tensor inputs, and efficient fitting procedures are implemented to make TT competitive with tensor-input Gaussian process (GP) models. Building upon these scalar-on-tensor trees, the method extends to tensor-on-tensor problems using additive ensemble approaches. Theoretical justifications and experiments on real and synthetic datasets demonstrate the performance of TT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve certain kinds of math problems by creating special models that work with complex data structures called tensors. This helps machines learn from data in a more efficient way, which is useful for many applications like predicting outcomes or making decisions. The method is tested on different types of datasets and shows promising results. |
Keywords
» Artificial intelligence » Regression
