{"total":85,"rows":[{"id":"2205.09435","title":"Adversarial random forests for density estimation and generative modeling","created":"2022-05-19","cited_by_count":44,"area":null},{"id":"2104.13881","title":"Large Scale Prediction with Decision Trees","created":"2021-04-28","cited_by_count":28,"area":null},{"id":"2105.11724","title":"SHAFF: Fast and consistent SHApley eFfect estimates via random Forests","created":"2021-05-25","cited_by_count":22,"area":null},{"id":"2303.12695","title":"Adaptive Conformal Prediction by Reweighting Nonconformity Score","created":"2023-03-22","cited_by_count":16,"area":null},{"id":"2103.16700","title":"Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest","created":"2021-03-30","cited_by_count":13,"area":null},{"id":"2202.04912","title":"Random Forest Weighted Local Fr\\'echet Regression with Random Objects","created":"2022-02-10","cited_by_count":12,"area":null},{"id":"2102.13347","title":"MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA","created":"2021-02-26","cited_by_count":10,"area":null},{"id":"2106.03820","title":"Accurate Shapley Values for explaining tree-based models","created":"2021-06-07","cited_by_count":9,"area":null},{"id":"2110.09626","title":"A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds","created":"2021-10-18","cited_by_count":9,"area":null},{"id":"2102.12328","title":"Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning","created":"2021-02-23","cited_by_count":7,"area":null},{"id":"2111.09790","title":"MCCE: Monte Carlo sampling of realistic counterfactual explanations","created":"2021-11-18","cited_by_count":7,"area":null},{"id":"2302.03931","title":"Fast Linear Model Trees by PILOT","created":"2023-02-08","cited_by_count":7,"area":null},{"id":"2511.18630","title":"Majority of the Bests: Improving Best-of-N via Bootstrapping","created":"2025-11-23","cited_by_count":7,"area":"lr"},{"id":"2308.03369","title":"Variable importance for causal forests: breaking down the heterogeneity of treatment effects","created":"2023-08-07","cited_by_count":6,"area":null},{"id":"2310.09702","title":"Inference with Mondrian Random Forests","created":"2023-10-15","cited_by_count":6,"area":null},{"id":"2311.17885","title":"Are Ensembles Getting Better all the Time?","created":"2023-11-29","cited_by_count":6,"area":null},{"id":"2507.08150","title":"CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk","created":"2025-07-10","cited_by_count":6,"area":null},{"id":"2510.24815","title":"Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2310.17114","title":"On the Convergence of CART under Sufficient Impurity Decrease Condition","created":"2023-10-26","cited_by_count":5,"area":null},{"id":"2108.09733","title":"A universally consistent learning rule with a universally monotone error","created":"2021-08-22","cited_by_count":4,"area":null},{"id":"2109.10541","title":"Minimax Rates for High-Dimensional Random Tessellation Forests","created":"2021-09-22","cited_by_count":4,"area":null},{"id":"2111.04658","title":"Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models","created":"2021-11-08","cited_by_count":4,"area":null},{"id":"2201.12692","title":"Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance","created":"2022-01-30","cited_by_count":4,"area":null},{"id":"2302.13511","title":"Extrapolated cross-validation for randomized ensembles","created":"2023-02-27","cited_by_count":4,"area":null},{"id":"2309.02211","title":"Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation","created":"2023-09-05","cited_by_count":4,"area":null},{"id":"2310.18814","title":"Stability of Random Forests and Coverage of Random-Forest Prediction Intervals","created":"2023-10-28","cited_by_count":4,"area":null},{"id":"2111.02749","title":"Label Ranking through Nonparametric Regression","created":"2021-11-04","cited_by_count":3,"area":null},{"id":"2302.07449","title":"A model-free feature selection technique of feature screening and random forest based recursive feature elimination","created":"2023-02-15","cited_by_count":3,"area":null},{"id":"2605.07002","title":"Adaptive auditing of AI systems with anytime-valid guarantees","created":"2026-05-07","cited_by_count":3,"area":null},{"id":"2201.06821","title":"Nonparametric Feature Selection by Random Forests and Deep Neural Networks","created":"2022-01-18","cited_by_count":2,"area":null},{"id":"2211.13289","title":"Shapley Curves: A Smoothing Perspective","created":"2022-11-23","cited_by_count":2,"area":null},{"id":"2310.12115","title":"MMD-based Variable Importance for Distributional Random Forest","created":"2023-10-18","cited_by_count":2,"area":null},{"id":"2503.10278","title":"Climate land use and other drivers impacts on island ecosystem services: a global review","created":"2025-03-13","cited_by_count":2,"area":null},{"id":"2504.12860","title":"When do Random Forests work?","created":"2025-04-17","cited_by_count":2,"area":null},{"id":"2505.17468","title":"Efficient Adaptive Experimentation with Noncompliance","created":"2025-05-23","cited_by_count":2,"area":null},{"id":"2111.03662","title":"Predicting Mortality from Credit Reports","created":"2021-11-05","cited_by_count":1,"area":null},{"id":"2205.09342","title":"Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2207.01678","title":"FACT: High-Dimensional Random Forests Inference","created":"2022-07-04","cited_by_count":1,"area":null},{"id":"2302.13828","title":"Random forests for binary geospatial data","created":"2023-02-27","cited_by_count":1,"area":null},{"id":"2305.00805","title":"Interpreting Deep Forest through Feature Contribution and MDI Feature Importance","created":"2023-05-01","cited_by_count":1,"area":null},{"id":"2404.01832","title":"When does Subagging Work?","created":"2024-04-02","cited_by_count":1,"area":null},{"id":"2406.14033","title":"Ensembles of Probabilistic Regression Trees","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2407.02458","title":"Statistical Advantages of Oblique Randomized Decision Trees and Forests","created":"2024-07-02","cited_by_count":1,"area":null},{"id":"2410.02623","title":"Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection","created":"2024-10-03","cited_by_count":1,"area":null},{"id":"2503.16753","title":"EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python","created":"2025-03-20","cited_by_count":1,"area":null},{"id":"2506.15643","title":"Revisiting Randomization in Greedy Model Search","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2507.07965","title":"Prospective Learning in Retrospect","created":"2025-07-10","cited_by_count":1,"area":null},{"id":"2602.13104","title":"Random Forests as Statistical Procedures: Design, Variance, and Dependence","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2603.27320","title":"Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach","created":"2026-03-28","cited_by_count":1,"area":null},{"id":"2103.11327","title":"Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)","created":"2021-03-21","cited_by_count":0,"area":null},{"id":"2105.07610","title":"Cross-Cluster Weighted Forests","created":"2021-05-17","cited_by_count":0,"area":null},{"id":"2109.08010","title":"WildWood: a new Random Forest algorithm","created":"2021-09-16","cited_by_count":0,"area":null},{"id":"2110.02715","title":"Variance function estimation in regression model via aggregation procedures","created":"2021-10-06","cited_by_count":0,"area":null},{"id":"2111.15320","title":"Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest","created":"2021-11-30","cited_by_count":0,"area":null},{"id":"2112.02589","title":"Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning","created":"2021-12-05","cited_by_count":0,"area":null},{"id":"2211.02273","title":"Time series quantile regression using random forests","created":"2022-11-04","cited_by_count":0,"area":null},{"id":"2305.16907","title":"CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2306.11908","title":"Generalized Random Forests using Fixed-Point Trees","created":"2023-06-20","cited_by_count":0,"area":null},{"id":"2307.12022","title":"Latent Utility Q-Learning for Preference-Adaptive Dynamic Treatment Regimes","created":"2023-07-22","cited_by_count":0,"area":null},{"id":"2311.14139","title":"Machine Learning For An Explainable Cost Prediction of Medical Insurance","created":"2023-11-23","cited_by_count":0,"area":null},{"id":"2402.04550","title":"Riemann-Lebesgue Forest for Regression","created":"2024-02-07","cited_by_count":0,"area":null},{"id":"2402.11052","title":"Building Trees for Probabilistic Prediction via Scoring Rules","created":"2024-02-16","cited_by_count":0,"area":null},{"id":"2403.06015","title":"Grafting: Making Random Forests Consistent","created":"2024-03-09","cited_by_count":0,"area":null},{"id":"2404.19127","title":"A model-free subdata selection method for classification","created":"2024-04-29","cited_by_count":0,"area":null},{"id":"2407.05108","title":"The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest","created":"2024-07-06","cited_by_count":0,"area":null},{"id":"2409.14123","title":"Consistency for Large Neural Networks: Regression and Classification","created":"2024-09-21","cited_by_count":0,"area":null},{"id":"2410.04996","title":"Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2410.12209","title":"Global Censored Quantile Random Forest","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2412.13020","title":"A Central Limit Theorem for the permutation importance measure","created":"2024-12-17","cited_by_count":0,"area":null},{"id":"2503.19306","title":"Centroid Decision Forest","created":"2025-03-25","cited_by_count":0,"area":null},{"id":"2505.12444","title":"High-Dimensional Dynamic Covariance Models with Random Forests","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2506.17696","title":"Regular Tree Search for Simulation Optimization","created":"2025-06-21","cited_by_count":0,"area":null},{"id":"2507.01430","title":"Targeted tuning of random forests for quantile estimation and prediction intervals","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2510.10870","title":"Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2511.13934","title":"Empirical Likelihood for Random Forests and Ensembles","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2601.07281","title":"Covariance-Driven Regression Trees: Reducing Overfitting in CART","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2601.21410","title":"Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.03258","title":"Principled Federated Random Forests for Heterogeneous Data","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.07020","title":"Financial Bond Similarity Search Using Representation Learning","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2604.10469","title":"Exact Finite-Sample Variance Decomposition of Subagging: A Spectral Filtering Perspective","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2605.04838","title":"PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.11841","title":"Minimax Rates and Spectral Distillation for Tree Ensembles","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2609.21454","title":"Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.40342","title":"Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.16447","title":"Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}