Tree Ensembles

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Stable Policy Learning
Sep 16, 2026Harvey Barnhard, Giacomo Opocher, Rahul SinghWelfare Tree Ensembles
Harvard University · University of Mannheim
Bagging Robustly Learns VC Classes with Linear Sample Complexity
Aug 13, 2026Omar MontasserEmpirical Risk Minimization Tree Ensembles
Yale University
Feature Bagging Provides Stability
Jul 29, 2026Yuheng Ma, Qiang SunTree Ensembles Random Forest
School of Statistics, East China Normal University · University of Toronto · MBZUAI
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
Jun 30, 2026Zakk Heile, Hayden McTavish, Margo Seltzer +1Decision Trees Tree Ensembles
Department of Computer Science · Duke University · Durham, USA +2
The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles
Jun 29, 2026Zewen LiuOversampling Imbalanced Classification
Qilu Institute of Technology · Tai’an, Shandong, China
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
Jun 27, 2026Donghwan Kim, Seung Hwan Park, Jun-Geol BaekTree Ensembles Kernel Method
School of Industrial Management Engineering, Korea University Seoul, Korea · School of Mechanical Engineering, Chungnam National University Daejeon, Korea
Kernel of Partition Paths: A Unified Representation for Tree Ensembles
Jun 17, 2026Nicolas MahlerTree Ensembles Decision Trees
Datapred SAS 23 rue Mirabeau 94300 Vincennes, France · Datapred SA EPFL Innovation Park – Bâtiment A 1015 Lausanne, Switzerland
Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning
Jun 11, 2026Meher Sai Preetam Madiraju, Meher Bhaskar MadirajuTree Ensembles Ensemble
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
May 27, 2026Haruki Yajima, Yusuke MatsuiTree Ensembles Structured Pruning
The University of Tokyo.
Proxy-Based Approximation of Shapley and Banzhaf Interactions
May 21, 2026Santo M. A. R. Thies, Hubert Baniecki, R. Teal Witter +3Shapley Value Tree Ensembles
LMU Munich · MCML · DFKI +4
A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees
May 19, 2026Massimo Aria, Agostino Gnasso, Carmela IorioTree Ensembles Ensemble
Department of Economics and Statistics, University of Naples2026 Federico II, Naples, Italy
Woodelf++: A Fast and Unified Partial Dependence Plot Algorithm for Decision Tree Ensembles
May 14, 2026Ron Wettenstein, Alexander Nadel, Udi BokerTree Ensembles Explainability
Reichman University, Herzliya, Israel · Faculty of Data and Decision Sciences, Technion, Haifa, Israel
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
May 13, 2026Ajinkya Naik, Chaitanya Garg, S. Akshay +2Tree Ensembles Ensemble
Indian Institute of Technology Bombay, India · University of Toronto, Canada
Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
May 12, 2026Lara Sá Neves, Afonso Lourenço, Lizy K. John +1Tree Ensembles Unlabeled Data
GECAD, ISEP, Polytechnic of Porto, Portugal · The University of Texas at Austin, USA
Minimax Rates and Spectral Distillation for Tree Ensembles
May 12, 2026Binh Duc Vu, David S. WatsonTree Ensembles Random Forest
King’s College London
Optimal Counterfactual Search in Tree Ensembles: A Study Across Modeling and Solution Paradigms
May 7, 2026Awa Khouna, Youssouf Emine, Julien Ferry +1Tree Ensembles Counterfactual Explanation
Polytechnique Montréal · Ivey Business School
Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions
May 6, 2026Ron Wettenstein, Rory Mitchell, Peng YuShapley Additive Explanations Tree Ensembles
Reichman University, Herzliya, Israel · Nvidia Corporation · Shopify
RCProb: Probabilistic rule extraction from classification tree ensembles
Apr 28, 2026Josue ObregonTree Ensembles Probabilistic Model
Department of Industrial Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul, 01811, Republic of Korea
Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance
Mar 27, 2025Markéta Krúpová, Nabil Rachdi, Quentin GuibertInsurance Tree Ensembles
CEREMADE · 1ADDACTIS France, 46 bis Chemin du Vieux Moulin, 69160 Tassin-la-Demi-Lune, France · 2CEREMADE, Universit´e Paris-Dauphine, PSL University, CNRS, 75016 Paris, France

Sep 16, 2026, econ.EMJ/K move · Enter open · S save

Stable Policy Learning

Harvey Barnhard, Giacomo Opocher, Rahul Singh

Harvard University · University of Mannheim

Welfare Tree Ensembles

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In evidence-based policymaking, typically one experimental sample is observed, then a learned policy recommendation is implemented at scale. Policies learned from the experimental data can perform well in expected welfare, yet random sampling in the experiment can produce recommendations with poor welfare outcomes. In this paper, we ask: how should policy learning algorithms balance expected welfare against sampling risk? Our main contribution is to show that algorithmic stability plays a central role in characterizing and navigating the tradeoff. Intuitively, if a policy learning algorithm's recommendation remains stable when one experimental unit is replaced, then that algorithm has limited sampling risk. We propose a method for policy learning called policy-vote bagging, which learns treatment decisions on many subsamples then averages their votes into treatment probabilities. Relative to using one subsample, averaging across subsamples preserves expected welfare and improves expected utility for a risk-averse researcher. We derive sharp bounds linking estimation accuracy, subsample size, and welfare variation, including an exact guarantee under CARA utility.

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