Stagewise boosting improves gradient boosting for distributional regression.
problem Vanishing gradient in gradient boosting for distributional regression leads to suboptimal models.
method Proposes a stagewise boosting-type algorithm for distributional regression, combining stagewise regression ideas with gradient boosting and incorporating a novel regularization method, correlation filtering.
result The proposed algorithm provides better results, especially for complex distributions, by reducing the risk of being trapped in a local optimum.
This research compares gradient and Newton boosting methods in classification and regression.
problem The distinction between gradient descent and Newton updates in boosting algorithms is not well understood.
method Presented a unified framework for gradient and Newton boosting, and compared them with tree base learners.
result Newton boosting outperforms gradient and hybrid boosting in predictive accuracy on most datasets.
NGBoost boosts probabilistic predictions using natural gradients.
problem Uncertainty estimation in probabilistic predictions.
method Gradient boosting for probabilistic regression with natural gradient correction.
result NGBoost outperforms existing methods for probabilistic prediction.
Robust boosting improves regression accuracy in noisy data.
problem Handling outliers in non-parametric regression.
method Two-stage approach: robust residual scale minimization followed by bounded loss optimization.
result Robust boosting outperforms standard methods in outlier-prone data.
Unified framework for statistical inference in gradient boosting regression.
problem Challenges in statistical inference and uncertainty quantification for gradient boosting.
method Integrates dropout or parallel training with regularization for CLT in boosting.
result Increasing dropout rate and parallel trees enhances signal recovery and performance.
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.
Gradient boosting with randomized trees reduces discontinuities and complexity.
problem Discontinuities in regression functions due to sparse training data.
method Gradient boosting machine with partially randomized decision trees.
result Improves robustness and computational efficiency of gradient boosting.
GrowNet uses shallow neural networks for gradient boosting, outperforming existing methods.
problem Improving gradient boosting performance through shallow neural networks.
method Unified gradient boosting framework with shallow neural networks as weak learners, incorporating corrective steps.
result GrowNet outperformed state-of-the-art boosting methods in classification, regression, and learning to rank tasks.
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.
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.
Boulevard boosts trees with regularization, converging to a normal distribution.
problem Improving regression model accuracy and uncertainty quantification.
method Regularized stochastic gradient boosting trees with subsampling and shrinkage.
result The algorithm converges to a normal distribution, enabling uncertainty characterization.
Parallel gradient boosting speeds up multi-output regression.
problem Efficiently predicting entire conditional distributions.
method Modification of gradient boosting to use a common descent direction.
result Significantly faster and comparable performance to state-of-the-art boosting libraries.
Unified probabilistic gradient boosting for entire conditional distribution modeling.
problem Creating accurate probabilistic forecasts from regression tasks.
method Unified probabilistic gradient boosting framework using XGBoost and LightGBM, modeling conditional moments or CDF via Normalizing Flows.
result Achieves state-of-the-art forecast accuracy.
Paper compares ARMA with exogenous variables to Gradient Boosting Regression for electricity price forecasting.
problem Energy price forecasting is hard and multi-step time series forecasting.
method ARMA with exogenous variables vs. Gradient Boosting Regression
result Gradient Boosting Regression outperforms ARMA with exogenous variables in terms of error metrics.
Efficient boosting method for regression with limited feedback.
problem Online boosting for regression tasks with noisy multi-point bandit feedback.
method Efficient regret minimization method with online boosting algorithm and projection-free online convex optimization.
result Improved state-of-the-art guarantees in efficiency.
Gradient boosting adapted for multi-label and multi-output tasks.
problem Joint prediction of multiple classification or regression outputs.
method Gradient tree boosting with random output projections.
result Random projection improves adaptation to different output correlation patterns.
Gradient boosting for spatial regression models improves prediction accuracy.
problem Spatial data with autoregressive disturbances.
method Model-based gradient boosting algorithm for spatial regression models.
result Improves prediction accuracy on out-of-sample spatial data.
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.
A new boosting method corrects endogeneity bias in instrumental variable regression.
problem Endogeneity bias in instrumental variable regression.
method Causal Gradient Boosting (boostIV) that builds on gradient boosting algorithm.
result boostIV is consistent and performs well in finite samples compared to other methods.
Approach scales regression for large datasets using CART segmentation.
problem Scaling regression to large datasets in various applications.
method Two-step approach: CART segmentation followed by model development for each segment.
result Predictive performance matches Gradient Boosted Trees.
Python package for ordinal regression using gradient boosting.
problem Handling ordinal variables in machine learning.
method Gradient boosting with latent variable framework.
result Performs joint optimization of latent function and threshold vector.
AGBoost uses attention weights to improve GBM for regression problems.
problem Improving gradient boosting machine for regression tasks.
method Attention-based modification of GBM with trainable attention weights.
result AGBoost achieves better performance on regression datasets.
Tree-structured boosting connects gradient boosted stumps and full decision trees.
problem Connecting gradient boosted stumps and full decision trees.
method Introducing tree-structured boosting to create a single decision tree.
result Tree-structured boosting produces models equivalent to CART or gradient boosted stumps at the extremes.
Gradient boosting can be seen as Gaussian process inference.
problem Improving uncertainty estimates in out-of-domain detection.
method Gradient boosting reformulated as a kernel method converging to Gaussian process inference.
result Gradient boosting can provide better uncertainty estimates through Monte-Carlo estimation of posterior variance.
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.
Hyperboost uses gradient boosting for hyperparameter optimization, outperforming state-of-the-art methods.
problem Hyperparameter tuning for machine learning algorithms
method Gradient boosting surrogate model with quantile regression and distance metric
result Hyperboost outperforms state-of-the-art techniques in empirical tests
Infinitesimal gradient boosting is a new algorithm derived from gradient boosting.
problem Improving the efficiency and smoothness of gradient boosting.
method Introduced a new class of randomized regression trees and used a limit process in vanishing-learning-rate asymptotic.
result Convergence of the stochastic algorithm and characterization of the limiting procedure as a unique solution of a nonlinear ODE.
Calibrated Boosting-Forest improves ranking and probability calibration in classification tasks.
problem Need for superior ranking power and well-calibrated probability estimates in classification tasks.
method Ensemble of gradient boosting machines supporting both continuous and binary labels.
result Calibrated Boosting-Forest achieves significant improvements in ranking and probability calibration compared to state-of-the-art models.
Wasserstein gradient boosting predicts probability distributions for supervised learning.
problem Distribution-valued supervised learning where outputs are probability distributions.
method Fits a new weak learner to Wasserstein gradients of loss functionals of probability distributions.
result Superior performance in probabilistic prediction compared to existing methods.
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.
An ensemble approach learns vector-weighted formulae for RLR.
problem Learning Relational Logistic Regression (RLR) with vector-weighted features.
method Functional-gradient boosting methods for probabilistic logic models.
result Our approach outperforms other methods for learning RLR.
NGBoost boosts multivariate probabilistic regression.
problem Joint probabilistic regression for multivariate targets.
method Natural Gradient Boosting for nonparametric modeling.
result Competitive performance in oceanographic velocity prediction.
Combines gradient boosting with collective inference for better continuous value prediction.
problem Improving predictive performance on relational data with interdependent instances.
method Proposes a boosting algorithm that learns a collective inference model to predict continuous target variables.
result The proposed algorithm outperforms alternative boosting methods on a real network dataset.
Study on L2-boosting behavior as learning rate approaches zero.
problem Understanding the asymptotic behavior of L2-boosting algorithms with vanishing learning rates.
method Analyzes L2-boosting for regression with linear base learners, proving a deterministic limit and characterizing it as a solution to a linear differential equation.
result Proves the existence of a unique solution to the limit problem and analyzes the training and test error.
A new method for automatic gradient tree boosting using information theory.
problem Automatic selection of tree complexity and number in gradient boosting.
method Optimism of greedy leaf splitting procedure modeled as a Cox-Ingersoll-Ross process, leading to an information criterion for model selection.
result The method achieves significant speedups (10-1400) compared to xgboost without sacrificing predictive power.
Boosted Frank-Wolfe accelerates optimization for nonconvex problems.
problem Optimizing nonconvex and quasar-convex objectives efficiently.
method Developed a novel step size strategy for stochastic Frank-Wolfe, extending it to various gradient estimators.
result Boosted Frank-Wolfe achieves faster convergence rates than non-boosted Frank-Wolfe.
GWRBoost improves GWR for better spatial relationship quantification.
problem Underfitting in GWR for complex data and lack of explainable quantification.
method Geographically weighted gradient boosting model using localized additive model and gradient boosting optimization.
result Significant improvement in RMSE and AICc compared to classic GWR.
New statistical methods improve explainability of boosting models.
problem Uncertainty quantification for boosting models is computationally intensive and hard to interpret.
method Derive methods for statistical inference using gradient boosting and Boulevard regularization.
result Achieve asymptotically normal predictions with theoretical guarantees and runtime independent of data size.
ScoreStop uses gradient tests to stop gradient boosting early.
problem Overfitting in gradient boosted decision trees.
method ScoreStop uses a functional score test based on gradients to stop boosting.
result ScoreStop is competitive with loss-based early stopping methods.
Boosts hazard estimation with time-varying covariates using gradient boosting.
problem Estimating nonparametric hazard functions with time-dependent covariates.
method Gradient boosting procedure for nonparametric hazard estimation.
result Step-size restriction prevents overfitting and ensures convergence.
Enhances GBDT robustness with one-hot encoding and regularization.
problem Low robustness of GBDT models against covariate perturbation.
method One-hot encoding to linear framework, risk decomposition, L1 or L2 regularization. result Regularization enhances GBDT robustness.
Proposes an algorithm for semi-supervised learning with budget constraints.
problem Learning classifiers within test-time budget constraints with limited labeled data.
method Leverages unlabeled data through Laplace smoothing and gradient boosted regression trees.
result First algorithm for semi-supervised budgeted learning.
Predicts arterial road incident duration with extreme gradient boosting.
problem Predicting incident duration on arterial roads, especially with limited data.
method Bi-level framework combining classification and regression models.
result Extreme gradient boosting outperformed other models by 53%.
Gradient Boosted Mixed Models estimate mean and variance components for clustered data.
problem Limited flexibility in linear mixed models for complex settings.
method Gradient Boosting extended to mixed models with likelihood-based gradients and flexible base learners.
result Accurate recovery of variance components and improved predictive accuracy.
Proposes mGBDTs for learning hierarchical representations in gradient boosting decision trees.
problem Inability of gradient boosting decision trees to learn hierarchical representations.
method Introduces multi-layered GBDT forest (mGBDTs) with explicit emphasis on hierarchical learning.
result Jointly trained mGBDTs can learn hierarchical representations effectively without backpropagation.
PathBoost boosts graph-level predictions using path-based features.
problem Graph-level classification and regression challenges.
method Gradient tree boosting method for graph-level prediction.
result PathBoost outperforms graph neural networks and graph kernel approaches in many cases.
Boosting methods for interval-censored data improve predictive accuracy in survival analysis.
problem Handling interval-censored data in survival analysis and time-to-event studies.
method Nonparametric boosting methods using censoring unbiased transformations and functional gradient descent.
result Effective boosting methods for regression and classification with interval-censored data, offering robust performance.
Boosting method adapted for non-square loss functions.
problem Applying gradient boosting to non-square loss functions.
method Introducing column measure ν^(L_2) to adapt L2-Boosting for different loss functions.
result L2-Boosting can be modified to select variables according to different loss functions.