Machine Learning
2026
8
- AutoGluon: Simplifying Machine Learning Baselines to a Few Lines of Code
- Why Better Simulators Often Combine Learning and Rules: From PDEs and Ray Tracing to DLSS
- Training with Imbalanced Samples: From Statistical Learning to Long-Tail Learning
- From Bagging to Stacking: Notes on Ensemble Learning
- Shapley and SHAP: State-of-the-Art Tools for Model Interpretability
- Fundamental Limits of Foundation Forecasting Models: Multimodality and Rigorous Evaluation
- Tree-Based Models Are Still SOTA for Tabular Data: XGBoost, LightGBM, and CatBoost
- From Euclidean Space to Manifold Topology: Dimensionality Reduction for High-Dimensional Data
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