An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
org/abs/1706.09516
7 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.
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MuViS is a new unified benchmark showing that neither gradient-boosted trees nor deep neural networks hold a universal advantage in multimodal virtual sensing.
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.
CatBoost and other ensemble ML models achieve R² scores of 0.95, 0.916, and 0.903 on yield strength, ultimate tensile strength, and elongation for resorbable Mg alloys, with SHAP analysis highlighting processing conditions and Zn/Mn/Gd content as key drivers.
citing papers explorer
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Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI
An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
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TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
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WOODELF-HD: Efficient Background SHAP for High-Depth Decision Trees
WoodelfHD reduces Background SHAP preprocessing for decision trees from 3^D to 2^D complexity, enabling exact computation on depths up to 21 with reported speedups of 33x to 162x.
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MuViS: Multimodal Virtual Sensing Benchmark
MuViS is a new unified benchmark showing that neither gradient-boosted trees nor deep neural networks hold a universal advantage in multimodal virtual sensing.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
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RelAgent: LLM Agents as Data Scientists for Relational Learning
RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.
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Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction
CatBoost and other ensemble ML models achieve R² scores of 0.95, 0.916, and 0.903 on yield strength, ultimate tensile strength, and elongation for resorbable Mg alloys, with SHAP analysis highlighting processing conditions and Zn/Mn/Gd content as key drivers.