Summary of Vanilla Gradient Descent For Oblique Decision Trees, by Subrat Prasad Panda et al.
Vanilla Gradient Descent for Oblique Decision Trees
by Subrat Prasad Panda, Blaise Genest, Arvind Easwaran, Ponnuthurai Nagaratnam Suganthan
First submitted to arxiv on: 17 Aug 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 The proposed DTSemNet model enables efficient learning of accurate oblique Decision Trees (DTs) by mapping them to Neural Networks (NNs). This approach uses vanilla gradient descent, unlike previous methods that relied on probabilistic approximations or quantized gradient descent. Experimental results show that DTSemNet-oblique DTs outperform state-of-the-art techniques in classification and regression tasks while reducing training time. Additionally, the model can efficiently learn DT policies in Reinforcement Learning (RL) setups with physical inputs. This work demonstrates a novel semantically equivalent and invertible encoding for oblique DTs as NNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DTSemNet is a new way to make decision trees more powerful and efficient. It takes a decision tree and turns it into a special kind of neural network, called DTSemNet. This allows us to use more powerful algorithms to learn from the decision tree. The result is that we can get better predictions from our decision trees, faster and with less training data. This is useful for lots of applications, like making good decisions in tricky situations. |
Keywords
» Artificial intelligence » Classification » Decision tree » Gradient descent » Neural network » Regression » Reinforcement learning
