arXiv research

Boost-R uses gradient boosted trees for analyzing recurrence data.

Gradient boosted decision trees are a popular machine learning technique, in part because of their ability to give good accuracy with small models. We describe two extensions to the standard tree boosting algorithm designed to increase this advantage. The first improvement extends the boosting formalism from scalar-val…

Additive models, such as produced by gradient boosting, and full interaction models, such as classification and regression trees (CART), are widely used algorithms that have been investigated largely in isolation. We show that these models exist along a spectrum, revealing never-before-known connections between these t…

Gradient boosting with randomized trees reduces discontinuities and complexity.

Boosting meta-trees improve decision tree performance.

We introduce a novel boosting algorithm called `KTBoost' which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous …

In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial…

Boosting trees predict Twitch subscriptions from user activity.

TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, princ…

Infinitesimal gradient boosting is a new algorithm derived from gradient boosting.

DBT combines diffusion models and boosting for supervised learning.

SMAC method optimizes tree-boosting hyperparameters best.

As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning techniques, though the computational expensiveness severely limits its usage. Stochastic gradient boosting could be adopted to accelerates gr…

Multivariate boosted trees improve forecasting and control by capturing correlated predictions.

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…

Gradient boosting improved with lassoed trees achieves faster convergence.

agtboost speeds up gradient tree boosting with automatic complexity adjustment.

SHAP explains boosted trees with additively modeled features.

This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge …

We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based on Online Gradient Boosting theory is proved and suggests that ResNet could achieve Online Gradient Boosting regret bounds through neural n…

A new method for automatic gradient tree boosting using information theory.

New methods improve prediction performance and reduce computation time in boosting and random forest models.

New algorithm improves convergence of gradient boosting trees.

Two algorithms for interpreting and boosting tree-based models using rule covering.

Boosted tree method improves MTL in heterogeneous domains.

Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classificatio…

Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated…

DBDT uses deep boosting decision trees for fraud detection.

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic b…

We propose to use boosted regression trees as a way to compute human-interpretable solutions to reinforcement learning problems. Boosting combines several regression trees to improve their accuracy without significantly reducing their inherent interpretability. Prior work has focused independently on reinforcement lear…

Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…

Proposes new attribution methods for trees with regularization.

This study extends verifiable learning to boosted tree ensembles, enabling efficient security verification.

Gradient boosting adapted for vector inputs.

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…

Paper studies ensemble probabilistic regression trees for smooth approximations.

SBPMT combines bagging and boosting for improved classification.

Boosted decision trees enjoy popularity in a variety of applications; however, for large-scale datasets, the cost of training a decision tree in each round can be prohibitively expensive. Inspired by ideas from the multi-arm bandit literature, we develop a highly efficient algorithm for computing exact greedy-optimal d…

AGBoost uses attention weights to improve GBM for regression problems.

SCORE improves tree-based predictions with boosted residual extraTrees.

A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.

UnmaskingTrees improves tabular data imputation and generation using gradient-boosted decision trees.

Treeffuser predicts tabular data distributions using gradient-boosted trees.

pGMM kernel outperforms ordinary ridge regression and RBF kernel ridge regression without tuning.

We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss. In contrast to these traditional boosting algorithms that treat a tree learner…

SketchBoost accelerates GBDT for multioutput problems up to 40x.

Develops a new method for decision trees using categorical variable structure.

The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, interpretability, and efficiency. We show in this paper that for…