A new gradient tree boosting framework reduces variance and accelerates performance.
problem High variance in stochastic gradient boosting.
method Combining gradient tree boosting with importance sampling and a regularizer.
result Achieves a linear convergence rate on logistic loss and 2.5x--18x acceleration on LogitBoost and LambdaMART.
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.
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.
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.
New sampling technique improves boosting model accuracy.
problem Improving generalization performance and learning time in stochastic gradient boosting.
method Formulated optimization problem to maximize estimation accuracy, leading to Minimal Variance Sampling (MVS).
result MVS significantly increases model quality and reduces the number of examples needed.
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.
Functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting
problem Fluctuations of boosting processes around their deterministic limit
method Stochastic perturbation analysis of ODEs in Banach spaces
result Rescaled deviations converge to a Gaussian process
StochasticRank optimizes ranking metrics efficiently and guarantees global convergence.
problem Optimizing discrete ranking metrics due to their ill-posed nature.
method Stochastic smoothing, gradient estimate, debiasing, and Stochastic Gradient Langevin Boosting.
result Global convergence and superior performance on ranking datasets.
We analyze a new Markov chain model for better sampling and optimization.
problem Developing a new Markov chain model for improved sampling and optimization.
method We introduce a new class of Ito chains with arbitrary noise and inexact drift/diffusion coefficients, proving a bound in W2-distance. result Our analysis provides improved or first results for various applications like SGLD, sampling, and boosting.
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 …
In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is based on harmonic analysis and approximation theoretical elements, and as we show through extensive experimentation, our wavelet based method…
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.
In AI research and industry, machine learning is the most widely used tool. One of the most important machine learning algorithms is Gradient Boosting Decision Tree, i.e. GBDT whose training process needs considerable computational resources and time. To shorten GBDT training time, many works tried to apply GBDT on Par…
Boosting framework improves adversarial robustness in deep learning.
problem Adversarial robustness of deep neural networks.
method Multiclass boosting framework with theoretical guarantees.
result Multiclass boosting achieves adversarial robustness faster than state-of-the-art methods.
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.
SecureGBM securely trains GBM models across two parties without revealing data.
problem Securely training GBM models across parties with encrypted data.
method Extending LightGBM with semi-homomorphic encryption and stochastic approximation.
result SecureGBM achieves AUC within 3% of non-secure LightGBM, maintaining performance.
New method controls gradient error for sparse MRFs.
problem Efficient learning for sparse discrete MRFs with NP-hard inference.
method Stochastic proximal gradient (SPG) with controlled gradient approximation error.
result Novel bounds control gradient approximation quality.
Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters…
Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger, constant stepsizes and preserving linear convergence rates. However, current variance reduced SGD methods require either high memory usage or an exact gradient computation (using the entire dataset)…
Hybrid model for online nonlinear prediction using LSTM and soft GBDT.
problem Online nonlinear prediction with manual feature selection and model selection issues.
method End-to-end architecture with LSTM for feature extraction and soft GBDT for regression, jointly optimized.
result Significant performance improvements over conventional methods on real datasets.
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.
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.
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.
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.
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.
In this survey, we discuss several different types of gradient boosting algorithms and illustrate their mathematical frameworks in detail: 1. introduction of gradient boosting leads to 2. objective function optimization, 3. loss function estimations, and 4. model constructions. 5. application of boosting in ranking.
Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updates is often not made in both applied and methodological research, and it is thus implicitly assumed that the difference is irrelevant. The g…
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.
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.
New algorithm improves convergence of gradient boosting trees.
problem Global convergence of Newton boosting in tabular machine learning.
method Introduces Gradient Regularized Newton Descent for GBDTs, proving linear convergence for smooth, strongly convex losses and O(k21) rate for general convex losses. result Achieves globally convergent second-order GBDT algorithm with rate matching first-order boosting.
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of lea…
PGBM creates probabilistic predictions efficiently.
problem Creating probabilistic predictions for large-scale data.
method Approximates leaf weights as random variables, learns moments via stochastic tree ensemble update equations.
result PGBM offers significant speedup and accuracy improvements over existing methods.
New method initializes MLPs for tabular data with tree-based feature interactions.
problem No dedicated neural network architecture for tabular data.
method Sparse tree-based initialization for MLPs.
result New method outperforms default MLP initialization and gradient 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.
SurvivalBoost improves prediction of event times in competing risks scenarios.
problem Predicting event times in scenarios with multiple possible outcomes.
method Developed a strictly proper censoring-adjusted scoring rule for stochastic optimization of competing risks.
result SurvivalBoost outperforms 12 state-of-the-art models across various metrics.
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…
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…
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.
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.
Optimizes CNNs by directing gradients along output channels.
problem Improving generalization error in CNNs.
method Output-channel directed re-weighted L2 or Sobolev metrics.
result Improves generalization error by optimizing gradients.
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.
Gradient boosting improved with lassoed trees achieves faster convergence.
problem Improving gradient boosting convergence in large nonparametric spaces.
method Lassoed gradient boosted trees with early stopping.
result Achieves faster than n−1/4 L2 convergence rate. sGBM speeds up gradient boosting by parallelizing and adapting base learners.
problem Infeasibility of parallelizing GBM training and sub-optimal performance in online settings.
method Integrates multiple differentiable base learners, jointly optimizing them with linear speed-up.
result sGBM achieves higher time efficiency and better accuracy than traditional GBM.
New GPU algorithm boosts machine learning with larger datasets.
problem Limited GPU memory restricts training data size.
method Out-of-core GPU gradient boosting algorithm.
result Training larger datasets on GPUs without accuracy loss.
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…
Python library for boosting statistical relational models.
problem Expressing learning and inference problems in statistical relational models.
method Adapting scikit-learn interface for boosted statistical relational models.
result Provides examples for using srlearn.
Gradient boosting adapted for vector inputs.
problem Applying gradient boosting to multi-class classification problems.
method Extended gradient boosting framework to vector inputs using histogram-based decision trees.
result Efficient algorithm for vector-valued objectives.
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.