Gradient Boosted Tree

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Period ending 2026-09-21

1 new paper

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Period ending 2026-09-07

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A weekly snapshot of new work published in Gradient Boosted Tree.

42 papers

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Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Sep 14, 2026Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri +1Urban Environments Gradient Boosted Tree
Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
Sep 3, 2026David Milec, Spyridon Samothrakis, Michael Fairbank +1Singular Learning Theory Imperfect-Information Games
Defensive Boosting for Online Probabilistic Forecasting
Aug 13, 2026Georgy Noarov, Aaron RothLearning-Augmented Algorithms Gradient Boosted Tree
Accounting Graph Transformer for Short-History Multi-KPI Forecasting in Small Businesses
Aug 7, 2026Shrutendra Harsola, Vignesh SubrahmaniamLlm-Based Forecasting Methods Load Forecasting
Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling
Aug 4, 2026Ryuichi KanohGradient Boosted Tree Decision Trees
Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression
Jul 30, 2026Silas KoemenGradient Boosted Tree Logistic Regression
TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees
Jul 29, 2026James ChapmanGradient Boosted Tree Spurious Correlations
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation
Jul 21, 2026Rahil SharmaFraud Detection Algorithm Auditing
Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches
Jul 18, 2026Al Zadid Sultan Bin Habib, Md Asif Bin Syed, Md. Ekramul Islam +1Machine Learning Gradient Boosted Tree
Parallel gradient boosting for flexible estimation of conditional distributions
Jul 15, 2026Rémy Chapelle, Nicolas Vayatis, Bruno Falissard +1Quantile Regression Gradient Boosted Tree
Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
Jul 14, 2026Jorge Alda, Jacobo Asorey, Alejandro Mir +1High Energy Physics Gradient Boosted Tree
Spectrally Deconfounded Gradient Boosting
Jul 10, 2026Andrea Nava, Peter Bühlmann, Fabio SigristGradient Boosted Tree Latent Confounders
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Jul 8, 2026Claudio Meggio, Johan Pensar, Riccardo De BinGraph Representation Learning Graph Neural Networks
Gradient boosting with vector-valued leafs
Jun 28, 2026David CortesGradient Boosted Tree Decision Trees
EMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening
Jun 24, 2026Yan SongGradient Boosted Tree Feature Selection
Gradient boosting for extremes: sampling theory and application to insurance
Jun 12, 2026Stéphane Lhaut, Olivier LopezGradient Boosted Tree Gaussian Process
High-Frequency Pricing at Scale for E-Commerce
Jun 11, 2026Stefan Birr, Tobias Huelden, Mones Raslan +7Demand Forecasting Prices
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
Jun 3, 2026Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Kumar Gyawali +2Tabular Foundation Models Tabpfn
ScoreStop: Gradient-based early stopping using functional score tests
Jun 1, 2026Oliver J. Hines, Christian L. HinesEarly Stopping Gradient Boosted Tree
Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles
May 26, 2026Joyjit Roy, Samaresh Kumar Singh, Laxmi ShawChurn Prediction Gradient Boosted Tree
Practical Anonymous Two-Party Gradient Boosting Decision Tree
May 26, 2026Chenyu Huang, Fan Zhang, Minxin Du +6Privacy-Preserving Framework Homomorphic Encryption
Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data
May 23, 2026Tomer Lavi, Bracha Shapira, Nadav RappoportGradient Boosted Tree Tabular Data
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
May 22, 2026Clément Bénard, Manon Arfib, Christophe Labreuche +1Class-Incremental Concept Bottleneck Model Concept Bottleneck Models
Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
May 18, 2026Aditya Tanna, Yash Desai, Pratinav Seth +3Tabular Foundation Models Ensemble
Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees
May 18, 2026Aditya Tanna, Nassim Bouarour, Mohamed Bouadi +2Tabular Foundation Models Gradient Boosted Tree
AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark
May 15, 2026Raj Kiran Gupta KatakamTraining Data Attribution Gradient Boosted Tree
TabPFN-3: Technical Report
May 13, 2026Léo Grinsztajn, Klemens Flöge, Oscar Key +38Tabpfn Benchmark Datasets
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
May 7, 2026Yi-Siang Wang, Kuan-Yu Chen, Yu-Chen Den +1Large Language Model Fine-Tuning Tabular Learning
ITBoost: Information-Theoretic Trust for Robust Boosting
May 6, 2026Ye Su, Longlong Zhao, Diego Garcia-Gil +4Noisy Labels Gradient Boosted Tree
Gradient Boosted Risk Scores
May 4, 2026Costa Georgantas, Jonas RichiardiGradient Boosted Tree Clinical Prediction
Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews
May 2, 2026Lidia Natasyah Marpaung, Vania Claresta, Iqfina Haula Halika +3Term Frequency-Inverse Document Frequency Sentiment Analysis
Gradient Regularized Newton Boosting Trees with Global Convergence
May 1, 2026Nikita Zozoulenko, Daniel Falkowski, Thomas Cass +1Gradient Boosted Tree Proximal Gradient Descent
Multiple Additive Neural Networks for Structured and Unstructured Data
Apr 29, 2026Janis Mohr, Jörg FrochteConvolutional Neural Networks Neural Network
Electricity price forecasting across Norway's five bidding zones in the post-crisis era
Apr 29, 2026My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha +1Electricity Price Forecasting Electricity Markets
Path-Based Gradient Boosting for Graph-Level Prediction
Apr 26, 2026Claudio Meggio, Johan Pensar, Riccardo De BinGraph Representation Learning Gradient Boosted Tree
An Integrated Framework for Explainable, Fair, and Observable Hospital Readmission Prediction: Development and Validation on MIMIC-IV
Apr 24, 2026Isaac Tosin AdisaAuc-Roc Gradient Boosted Tree
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Feb 13, 2026Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2Energy Forecasting Offshore Wind Turbine
TabICLv2: A better, faster, scalable, and open tabular foundation model
Feb 11, 2026Jingang Qu, David Holzmüller, Gaël Varoquaux +1Tabular Foundation Models Model Pretraining
Selecting Hyperparameters for Tree-Boosting
Feb 5, 2026Floris Jan Koster, Fabio SigristGradient Boosted Tree Hyperparameter Optimization
Multi-Objective Hyperparameter Search via Damped Gauss--Newton Optimization
Jan 7, 2024Qinwu Xu, Yifan JiangHyperparameter Optimization Gradient Boosted Tree
Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning
Sep 30, 2023Sujith K MandalaMachine Learning Gradient Boosted Tree
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
Aug 4, 2021Yang Bai, Kuntara PukthuanthongPortfolio Optimization Gradient Boosted Tree

Sep 14, 2026 · cs.LGJ/K move · Enter open · S save

Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework

Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri, Joseph Broach

Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive and cover only a small share of intersections. We present a machine learning pipeline that predicts 2-hour PM peak pedestrian volume at 101 urban intersections in Portland, Oregon, from built-environment, land-use, and street-network features drawn from open GIS data. Starting from the Negative Binomial GLM used in practice, we add feature selection, count-aware gradient boosting, and repeated cross-validation, selecting one configuration by a combined rank over RMSE, MAPE, and SMAPE across four cross-validation strategies. The winner, a histogram-based gradient boosting model with Poisson loss and L1 Lasso feature selection, reduces cross-validated RMSE by 12% over the GLM baseline (89.8 to 78.7) and holdout RMSE by 19% (108.0 to 87.9). Code is released on GitHub.

Urban Environments Gradient Boosted Tree Logistic Regression Cross Validation Machine Learning

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