# UltraGBM: Unified GBDT Library Research

Comprehensive deep research covering ALL algorithmic advances in Gradient Boosted Decision Tree families.

**Date:** 2026-04-29

## Covered Frameworks
1. **XGBoost** (Chen & Guestrin, KDD 2016) — arxiv:1603.02754
2. **LightGBM** (Ke et al., NeurIPS 2017)
3. **CatBoost** (Prokhorenkova et al., 2017/2018) — arxiv:1706.09516
4. **Yggdrasil/TF-DF** (Guillame-Bert et al., KDD 2023) — arxiv:2212.02934
5. **Recent Advances** (2022-2026): GRANDE, NRGBoost, GBRL, NODE, C-GB, DP-EBM, HybridTree

## Key Innovations Catalogued
- 22 algorithmic innovations ranked by priority
- Exact equations, pseudocode, and hyperparameters for each
- Master recipe table with implementation complexity vs. impact
- Phased architecture roadmap for UltraGBM implementation

See `UltraGBM_Research_Report.md` for the full document.