arXiv research

Fair MP-Boost improves fairness and interpretability in boosting methods.

MP-Boost boosts accuracy faster and more interpretable than AdaBoost.

Paper proposes a boosting method with fast learning rates and early stopping.

Efficient boosting method for regression with limited feedback.

Boosting is a generic learning method for classification and regression. Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance. Overfitting is an important and ubiquitous phenomenon, especially in regression settings. To avoid overfitting, we consider using $l_1$…

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm, wh…

Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the context of $\ell_2$ boosting, we start with a weak linear le…

Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…

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…

GrowNet uses shallow neural networks for gradient boosting, outperforming existing methods.

A boosting method improves nonparametric density estimation without smoothing assumptions.

Paper presents robust boosting methods for label noise.

This research tackles uncertainty in gradient boosting models using ensemble methods.

We prove that L2-Boosting lacks a theoretical property which is central to the behaviour of l1-penalized methods such as basis pursuit and the Lasso: Whereas l1-penalized methods are guaranteed to recover the sparse parameter vector in a high-dimensional linear model under an appropriate restricted nullspace property, …

SMAC method optimizes tree-boosting hyperparameters best.

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…

SGLB boosts machine learning with Langevin diffusion for multimodal loss functions.

Boosting improves ICA for better component recovery.

Boosting for off-policy learning reduces empirical risk.

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

Wasserstein gradient boosting predicts probability distributions for supervised learning.

SnapBoost uses random base hypothesis classes to improve gradient boosting performance.

New outlier detection method using graph Laplacian spectrum boosts performance.

Gradient boosting can be seen as Gaussian process inference.

Boosting methods for interval-censored data improve predictive accuracy in survival analysis.

A new boosting model handles dependent censoring in time-to-event data.

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

Boosted tree method improves MTL in heterogeneous domains.

Gradient boosting with randomized trees reduces discontinuities and complexity.

FGBoost boosts gradient boosting for complex data.

Boosts weak online learners to strong ones with sublinear regret.

New statistical methods improve explainability of boosting models.

Deep generative models are effective methods of modeling data. However, it is not easy for a single generative model to faithfully capture the distributions of complex data such as images. In this paper, we propose an approach for boosting generative models, which cascades meta-models together to produce a stronger mod…

RFpredInterval package builds prediction intervals for random forests and boosted forests.

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…

In this paper we analyze boosting algorithms in linear regression from a new perspective: that of modern first-order methods in convex optimization. We show that classic boosting algorithms in linear regression, namely the incremental forward stagewise algorithm (FS$_\varepsilon$) and least squares boosting (LS-Boost($…

Gradient boosting method enforced with individual fairness.

BoostTransformer uses boosting to improve transformer efficiency and accuracy.

Gradient Boosted Mixed Models estimate mean and variance components for clustered data.

Boosted conformal procedure improves prediction intervals.

BayesBoost combines boosting and Bayesian methods for linear mixed models, improving uncertainty estimation and variable selection.

A method to improve gradient boosting models using stacking.

New algorithm improves convergence of gradient boosting trees.

Boosting for label ranking outperforms existing methods.

The paper provides convergence guarantees for multicalibration gradient boosting.

Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be interpreted as the solutions of suitable formulated data-driven DRO problems.…

StructureBoost improves gradient boosting for complex categorical variables efficiently.

Boosts Q-learning by using value function bounds.