Summary of Complexity-aware Deep Symbolic Regression with Robust Risk-seeking Policy Gradients, by Zachary Bastiani et al.
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients
by Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter, Shandian Zhe
First submitted to arxiv on: 10 Jun 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 A novel deep symbolic regression approach is proposed to enhance the robustness and interpretability of data-driven mathematical expression discovery. The state-of-the-art method, DSR, has limitations, including being built on recurrent neural networks and potentially meeting tail barriers that can zero out the policy gradient. To overcome these limitations, transformers are used in conjunction with breadth-first-search to improve learning performance. Bayesian information criterion (BIC) is used as a reward function to explicitly account for expression complexity and optimize the trade-off between interpretability and data fitness. A modified risk-seeking policy is proposed that ensures unbiasness of the gradient and removes tail barriers, ensuring effective updates from top performers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper suggests a new way to discover mathematical expressions using computers. The current best method has some problems, like not being very good at understanding what it’s doing or getting stuck. To fix these issues, a different type of neural network (transformers) is used with an old idea from computer science (breadth-first-search). A special formula (Bayesian information criterion) helps the computer understand how to balance making predictions and understanding what those predictions mean. |
Keywords
» Artificial intelligence » Neural network » Regression
