AI RESEARCH
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients
arXiv CS.AI
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ArXi:2406.06751v3 Announce Type: replace-cross We propose a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Our work is aligned with the popular DSR framework which focuses on learning a data-specific expression generator, without relying on pretrained models or additional search or planning procedures.