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.
MP-Boost boosts accuracy faster and more interpretable than AdaBoost.
problem Developing a faster, more interpretable boosting method.
method Adaptive selection of minipatches (small subsets of instances and features) at each iteration.
result Achieves comparable accuracy to AdaBoost and gradient boosting but faster and more interpretable.
Boosting connects to kernel-based methods, improving learning algorithms.
problem Improving learning algorithms for classification and prediction.
method Connecting boosting to kernel-based methods and showing equivalence with a boosting kernel.
result Boosting with a weak linear learner defined by a kernel is equivalent to estimation with a boosting kernel.
Wavelet boosts gradient boosting, improving accuracy, especially in imbalanced data.
problem Improving gradient boosting performance, especially in imbalanced data.
method Wavelet decomposition of trees in gradient boosting.
result Wavelet-based gradient boosting outperforms existing methods, especially in imbalanced data.
A novel multi-clustering method based on boosting improves hierarchical clustering quality.
problem Improving hierarchical clustering quality in flat clustering problems.
method A boosting iteration with weighted random sampling of elements from the original dataset, followed by hierarchical clustering on each subsample and consensus combination.
result The proposed method provides superior quality solutions compared to standard hierarchical clustering methods.
Proximal boosting improves gradient boosting for non-differentiable losses.
problem Minimizing non-differentiable losses in prediction models.
method Proximal point algorithm applied to gradient boosting.
result Proximal boosting outperforms gradient boosting in convergence rate and accuracy.
L2-Boosting fails to recover sparse parameters in high-dimensional models.
problem Theoretical differences between L2-Boosting and L1-penalized methods like Lasso.
method Proof of theoretical property differences between L2-Boosting and L1-penalized methods.
result L2-Boosting does not guarantee parameter recovery in high-dimensional models.
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.
Paper proposes a boosting method with fast learning rates and early stopping.
problem Missing theoretical guarantees for boosting methods in binary classification.
method Fully-corrective gradient boosting with squared hinge loss and ADMM algorithm.
result Derives fast learning rates of O((m/logm)−1/4) and O((m/logm)−1/2). 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 l1…
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.
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.
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.
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.
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.
A boosting method improves nonparametric density estimation without smoothing assumptions.
problem Overfitting in nonparametric data fitting.
method Introduces a boosting algorithm for univariate nonparametric maximum likelihood estimation.
result Demonstrates the effectiveness of the boosting approach through simulations and real data experiments.
Paper presents robust boosting methods for label noise.
problem Boosting methods degrade in noisy environments.
method Robust Minimax Boosting (RMBoost) with theoretical guarantees.
result RMBoost provides strong classification accuracy and robustness.
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.
SMAC method optimizes tree-boosting hyperparameters best.
problem Optimizing hyperparameters for tree-boosting to improve model accuracy.
method Compared and evaluated various hyperparameter optimization methods.
result SMAC method outperforms other methods for hyperparameter tuning.
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.
SGLB boosts machine learning with Langevin diffusion for multimodal loss functions.
problem Dealing with multimodal loss functions in machine learning.
method Stochastic Gradient Langevin Boosting (SGLB) based on Langevin diffusion equation.
result SGLB guarantees global convergence for multimodal loss functions.
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…
Boosting improves ICA for better component recovery.
problem Improving ICA's reliance on prior knowledge of sources.
method Maximizing likelihood via boosting and fixed-point unmixing.
result Boosting-based ICA outperforms existing methods.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
Boosting for off-policy learning reduces empirical risk.
problem Learning from logged bandit feedback without labeled data.
method A boosting algorithm optimizing policy's expected reward.
result Excess empirical risk decreases with each round of boosting.
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.
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.
SnapBoost uses random base hypothesis classes to improve gradient boosting performance.
problem Improving gradient boosting performance.
method Heterogeneous Newton Boosting Machine (HNBM) with variable base hypothesis classes.
result SnapBoost achieves better generalization loss than competing frameworks.
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.
New outlier detection method using graph Laplacian spectrum boosts performance.
problem Detecting outliers in large datasets efficiently.
method Boosted outlier detection based on graph Laplacian spectrum.
result Outperforms existing methods on synthetic datasets.
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.
Accelerated Gradient Boosting improves performance and sparsity of predictions.
problem Improving prediction accuracy and sparsity in machine learning models.
method Combining gradient boosting with Nesterov's accelerated descent.
result Accelerated Gradient Boosting (AGB) outperforms traditional gradient boosting in terms of sparsity and sensitivity to shrinkage parameters.
A new boosting model handles dependent censoring in time-to-event data.
problem Independent censoring assumption leads to biased predictions in time-to-event analysis.
method Clayton-boost, a boosting approach using Clayton copula.
result Clayton-boost outperforms other methods in handling dependent censoring.
New methods improve prediction performance and reduce computation time in boosting and random forest models.
problem Improving prediction performance and reducing computation time in boosting and random forest models.
method Random tree depth injection approach for Boosting and Random Forests.
result The new methods can improve prediction performance and reduce computation time by up to 40%.
FGBoost boosts gradient boosting for complex data.
problem Gradient boosting struggles with non-Euclidean data.
method Introduces FGBoost for geodesic metric spaces.
result FGBoost performs well on complex data.
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.
Boosts weak online learners to strong ones with sublinear regret.
problem Online learning agnostic setting without strong guarantees.
method Reduction to online convex optimization, boosting via marginally-better-than-trivial regret guarantees.
result First agnostic online boosting algorithm with sublinear regret.
Boosted tree method improves MTL in heterogeneous domains.
problem Improving MTL in diverse, domain-specific tasks.
method Two-stage approach: common model for shared features, specific models for task-specific instances.
result Enhanced multi-task learning performance with interpretability.
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.
Boosting algorithm improved by DRO framework for financial prediction.
problem Improving boosting algorithms for robust financial prediction.
method Proposes DRO-Boosting algorithm to solve DRO formulation.
result DRO-Boosting algorithm recovers AdaBoost and performs well on financial data.
RFpredInterval package builds prediction intervals for random forests and boosted forests.
problem Quantifying uncertainty in random forest and boosted forest point predictions.
method 16 methods to build prediction intervals with random forests and boosted forests.
result The proposed method outperforms existing methods in building prediction intervals.
Contrast trees assess machine learning accuracy, boosting improves performance.
problem Lack of accuracy assessment for machine learning results.
method Contrast trees and distribution boosting methods.
result Distribution boosting provides an assumption-free method for estimating outcome distributions.
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 (FSε) and least squares boosting (LS-Boost($…
Gradient boosting method enforced with individual fairness.
problem Enforcing fairness in machine learning models.
method Functional gradient descent on robust loss function.
result Algorithm converges globally and generalizes.
SEBOOST boosts stochastic learning by optimizing recent steps and directions.
problem Improving the performance and robustness of stochastic optimization methods.
method SEBOOST applies a secondary optimization process in the subspace of recent steps and descent directions.
result SEBOOST significantly boosts the performance of various stochastic optimization methods.
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.
VeriGB verifies robustness of gradient boosted models.
problem Proving robustness of gradient boosted models.
method VeriGB encodes model and robustness as SMT formula for verification.
result VeriGB proves robustness of large gradient boosted models.
BoostTransformer uses boosting to improve transformer efficiency and accuracy.
problem Heavy computational resources and hyperparameter tuning in transformer architectures.
method Augments transformers with boosting principles through subgrid token selection and importance-weighted sampling, incorporating a least square boosting objective directly into the pipeline.
result BoostTransformer demonstrates faster convergence and higher accuracy compared to standard transformers.