This research tackles uncertainty in gradient boosting models using ensemble methods.
problem Quantifying uncertainty in gradient boosting models for high-risk applications.
method Probabilistic ensemble-based framework for gradient boosting classification and regression models.
result Ensembles of gradient boosting models detect anomalous inputs but have limited ability to improve total uncertainty.
BAE uses boosting to improve autoencoder ensembles for robust outlier detection.
problem Overfitting in autoencoders limits their effectiveness in unsupervised outlier detection.
method Boosting-based Autoencoder Ensemble (BAE) trains autoencoders sequentially with weighted sampling to reduce outliers and inject diversity.
result BAE outperforms state-of-the-art approaches in various outlier detection conditions.
This study extends verifiable learning to boosted tree ensembles, enabling efficient security verification.
problem Efficiently verifying the robustness of boosted tree ensembles against norm-based attackers.
method Formal verification of robustness for large-spread boosted tree ensembles, considering L∞-norm and pseudo-polynomial time for Lp-norm verification. result Polynomial time verification for L∞-norm attackers, NP-hard for other norms, and pseudo-polynomial time for Lp-norm verification. Boosting strategies for merging vs. ensembling studies analyzed.
problem Deciding between merging and ensembling studies for boosting.
method Analytical transition point and bias-variance decomposition for boosting with linear learners.
result Theoretical guidelines for merging vs. ensembling studies.
A method to improve gradient boosting models using stacking.
problem Improving the performance of gradient boosting models.
method Proposes a stacking algorithm to learn a meta-model for ensembles of gradient boosting models.
result The proposed approach can be extended to differentiable combination models like neural networks.
Deep Incremental Boosting speeds up Deep Learning training with improved generalization.
problem Reducing training time and improving generalization in Deep Learning.
method Inspired by AdaBoost and Transfer Learning, Deep Incremental Boosting adapts Ensemble methods to Deep Learning.
result Reduces training time and improves generalization on Deep Learning datasets.
Proposes GBBHE for efficient large-scale regression.
problem Large-scale regression problems.
method Gradient Boosting with binary histogram partition and ensemble learning.
result Improves computational efficiency and performance on large datasets.
Better boosting with bandits improves probability estimation in online learning.
problem Poor calibration of probability estimates from boosting ensembles in online learning.
method Use bandit optimization to decide between updating ensemble or calibrator parameters.
result Superior performance in probability estimation compared to uncalibrated and naively-calibrated online boosting.
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
Boosting for label ranking outperforms existing methods.
problem Improving label ranking predictions using boosting techniques.
method Proposed a boosting algorithm tailored for label ranking tasks.
result Significantly outperforms existing label ranking algorithms.
This work improves adversarial robustness by boosting model ensembles with margin maximization.
problem Single models are insufficient for defending against adversarial attacks.
method Margin-boosting approach to learn ensembles with maximum margin.
result Our algorithm outperforms existing ensembling techniques and large models trained end-to-end.
BoostForest combines multiple BoostTree models for improved accuracy.
problem Improving ensemble learning performance.
method BoostTree uses gradient boosting and random cut-points. BoostForest bootstraps training data and randomly samples parameters.
result BoostForest outperforms classical ensemble methods on 35 datasets.
Proposes a simple neural network model similar to gradient boosted decision trees.
problem Building a neural network equivalent to gradient boosted decision trees.
method Converts an ensemble of decision trees to a neural network, relaxes properties, and trains a simple neural network model.
result The proposed Hammock model achieves similar performance to gradient boosted decision trees.
ABHT boosts regression by filtering regions with different smoothness.
problem Improving regression performance through local adaptivity.
method Gradient boosting with adaptive histogram transform.
result ABHT converges faster than PEHT in Hölder continuous spaces.
New method grows many trees in parallel and selects a compact subset for optimization.
problem Creating interpretable and compact tree ensembles.
method Growing a large pool of trees in parallel with loss optimization on a subset.
result Compact tree ensemble achieves lower loss and misclassification error.
Classifier evasion consists in finding for a given instance x the nearest instance x′ such that the classifier predictions of x and x′ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixe…
Boost-R uses gradient boosted trees for analyzing recurrence data.
problem Analyzing recurrence data with static and dynamic features.
method Gradient boosted additive trees with time-dependent functions.
result Estimates the cumulative intensity function of recurrent event processes.
Boosted trees improve reinforcement learning solutions that are easy to understand.
problem Creating accurate reinforcement learning solutions that are also easy to understand.
method Using boosted regression trees to combine multiple regression trees.
result Boosted regression trees produce solutions that are as accurate as other methods but are also easy to understand.
Boosting models improve pediatric ICU transfer prediction.
problem Predicting transfer of pediatric patients to ICU.
method Adaptive and gradient boosting classifiers combined into an ensemble model.
result Improved accuracy, sensitivity, specificity, and AUROC over baseline.
Multi-headed ensembles boost model performance with faster training.
problem Limited computational resources hinder ensemble search performance.
method Extend NES to multi-headed ensembles, leveraging end-to-end training and one-shot NAS methods.
result Multi-headed ensemble search finds robust ensembles 3 times faster with comparable performance.
This paper improves prediction rule ensembles using model-based data generation.
problem Improving the sparsity and predictive accuracy of prediction rule ensembles.
method The authors use surrogate models to train Lasso regression with data generated by a boosted decision tree ensemble, improving PRE performance.
result The use of surrogacy models can substantially improve the sparsity of PRE while retaining predictive accuracy.
Paper studies ensemble probabilistic regression trees for smooth approximations.
problem Smooth approximations of regression functions.
method Ensemble versions of probabilistic regression trees.
result Ensemble probabilistic regression trees are consistent and perform well.
We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Fur…
Enhances apparel attribute recognition with a two-layer ensemble method.
problem Improving accuracy in apparel attributes classification using deep neural networks.
method Proposes a two-layer mixture framework combining bagging and boosting for ensemble learning.
result The proposed method outperforms individual models and ensemble methods.
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
Fair MP-Boost improves fairness and interpretability in boosting methods.
problem Improving fairness and interpretability in boosting methods.
method Fair MP-Boost uses adaptive sampling of minipatches to balance accuracy and fairness.
result Fair MP-Boost enhances fairness and accuracy while providing interpretable feature importance.
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
problem Lack of effective short-term load forecasting methods.
method Hybrid strategy integrating four different inference models: tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB).
result Demonstrates effectiveness of hybrid strategy on real datasets.
Boosting Nyström improves accuracy of matrix approximations.
problem Generating low-rank approximations of large matrices efficiently.
method Iteratively generate multiple weak Nyström approximations, combine them to form a strong approximation.
result Boosting Nyström yields more efficient and accurate low-rank approximations.
Paper uses ensemble learning for more accurate power flow modeling.
problem Improving accuracy and efficiency of power flow modeling.
method Applies polynomial regression and ensemble learning (GB, bagging) to create a more accurate linear power flow model.
result Data-driven model outperforms traditional methods in accuracy and speed.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
problem Understanding the mathematical principles behind ensemble learning and ant colony behavior.
method Developed a formal mapping between AdaBoost's adaptive reweighting and ant recruitment dynamics.
result Proved that the fundamental theorem of weak learnability has a direct analog in colony decision-making.
Paper proposes ensemble methods to prevent forgetting in neural networks.
problem Catastrophic forgetting in retraining neural networks.
method Gradient boosting and meta-learning approaches.
result Prevents forgetting in pre-trained neural network models.
We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a log(1/ε) convergence rate for SelfieBoost under some "SGD success" assumpti…
Enhances recommendation performance with an ensemble of collaborative filters.
problem Collaborative filtering's performance is unsatisfactory in diverse real-world applications.
method Formulated a probabilistic model integrating items, users, and associations. Derived a progressive algorithm to construct an ensemble of collaborative filters.
result Substantial improvement over state-of-the-art methods, including L2Boost.
A method interprets black-box models using an ensemble of gradient boosting machines.
problem Local and global interpretation of black-box models.
method An ensemble of gradient boosting machines (GBMs) to form a generalized additive model.
result Efficiency and properties demonstrated on synthetic and real datasets.
We generate counterfactual explanations for tree-based boosting ensembles.
problem Understanding how tree-based models make predictions.
method Extending a method for random forests to GBDTs, accounting for tree sequential dependency and negative gradients.
result A method to generate counterfactual explanations for GBDTs.
A scalable framework for gradient boosting using TensorFlow.
problem Training gradient boosted trees efficiently on large datasets.
method Distributed training architecture, automatic loss differentiation, layer-by-layer boosting, multi-class handling, regularization.
result Faster prediction and smaller ensembles compared to traditional methods.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
Boosted DT classifiers become DP with new calibrated loss.
problem Making boosted DT classifiers differentially private.
method Crafted Mα-loss and objective calibration. result Significantly outperforms random forests in DP settings.
Develops RKHS framework for analyzing tree ensembles.
problem Analyzing the theoretical properties of tree ensembles.
method Reproducing Kernel Hilbert Spaces (RKHS) for tree ensembles.
result Characterizes Random Forest predictor as minimizer of a penalized empirical risk functional in RKHS.
New findings show the large margins theory is insufficient for explaining ensemble methods.
problem Explaining the performance of ensemble methods, especially boosting.
method Illustrated by counterexamples that show how to improve margin distribution without improving test set performance.
result The large margins theory is not sufficient to explain the performance of ensemble methods.
BENN improves binary neural networks by ensemble methods, boosting accuracy without sacrificing efficiency.
problem Inefficiency and accuracy degradation in binary neural networks.
method Proposes Binary Ensemble Neural Network (BENN) using ensemble techniques.
result BENN outperforms state-of-the-art binary networks and full-precision networks.
MAC combines models without locking them, improving ensemble performance.
problem Improving ensemble learning performance with flexibility.
method Model agnostic combination technique that dynamically combines models.
result MAC outperforms classical methods and competitive to boosting.
MEBoost improves classification of imbalanced datasets by mixing two weak learners.
problem Class imbalance in real datasets.
method Mixing two weak learners with boosting.
result Significant improvement over existing methods on imbalanced datasets.
CatBoost boosts performance on datasets with categorical features.
problem Handling categorical features in gradient boosting.
method Gradient boosting library with GPU and CPU implementations.
result Outperforms existing implementations on popular datasets.
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative poin…
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
problem Concept drift in evolving data streams.
method Adapts XGB to update the ensemble with new data, maintaining consistency with current concept.
result Improves classification accuracy on evolving data streams compared to other methods.
agtboost speeds up gradient tree boosting with automatic complexity adjustment.
problem Speeding up and simplifying gradient tree boosting computations.
method Adaptive gradient tree boosting with automatic complexity adjustment and feature importance.
result Significant decrease in computation time and simplification of model complexity.