Proposes a simple neural network model similar to gradient boosted decision trees.
problem Building a neural network equivalent to gradient boosted decision trees.
method Converts an ensemble of decision trees to a neural network, relaxes properties, and trains a simple neural network model.
result The proposed Hammock model achieves similar performance to gradient boosted decision trees.
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…
Residual Networks are shown to be equivalent to boosting feature representation.
problem Improving feature representation in deep learning models.
method Proved ResNet's equivalence to Online Gradient Boosting and proposed decision tree residual modules.
result ResNet can achieve Online Gradient Boosting regret bounds through architectural changes.
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.
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.
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.
DBDT uses deep boosting decision trees for fraud detection.
problem Fraud detection in imbalanced data.
method Gradient boosting with neural networks (SDT), AUC maximization.
result DBDT significantly improves fraud detection performance.
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.
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.
SketchBoost accelerates GBDT for multioutput problems up to 40x.
problem Efficiently training GBDT for multioutput problems with high-dimensional outputs.
method Approximate computation of scoring function for faster decision tree splitting.
result SketchBoost speeds up GBDT training by up to 40 times.
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…
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.
Develops a new method for decision trees using categorical variable structure.
problem Lack of structure in treating categorical variables as predictors.
method Introduces a mathematical framework to represent categorical structure and generalizes decision trees to utilize this structure.
result Improves prediction accuracy on weather data using the new method.
Converts GBDT trees to neural networks for online updates.
problem Performance loss in converting GBDT trees to neural networks.
method Converts existing GBDT implementations to neural network architectures, allowing online updates of decision splits.
result Learning bounds for neural network architecture with updated splits.
We generate counterfactual explanations for tree-based boosting ensembles.
problem Understanding how tree-based models make predictions.
method Extending a method for random forests to GBDTs, accounting for tree sequential dependency and negative gradients.
result A method to generate counterfactual explanations for GBDTs.
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…
Develops a fair tree boosting method for tabular data.
problem Lack of fair classifiers based on decision trees in tabular data.
method Adversarial gradient tree boosting that minimizes adversarial neural network's ability to predict sensitive attributes.
result Achieves higher accuracy while maintaining fairness.
FPGA-based logic architecture speeds up GBDT training 259x.
problem Training efficiency and power consumption in GBDT models.
method Implemented logic architecture on FPGA, compared with software libraries.
result Training speed 26-259x faster, power efficiency 90-1,104x higher.
agtboost speeds up gradient tree boosting with automatic complexity adjustment.
problem Speeding up and simplifying gradient tree boosting computations.
method Adaptive gradient tree boosting with automatic complexity adjustment and feature importance.
result Significant decrease in computation time and simplification of model complexity.
Improves GBDT accuracy with differential privacy.
problem Balancing privacy and accuracy in GBDT models.
method Adaptive gradient control and novel boosting framework for privacy budget allocation.
result Achieves better model accuracy with differential privacy.
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…
StructureBoost improves gradient boosting for complex categorical variables efficiently.
problem Efficiently handling complex categorical variables with known structure.
method Two methods to overcome computational obstacles in SCDT enumeration for structured categorical variables.
result StructureBoost outperforms existing packages on complex categorical problems.
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.
DBT combines diffusion models and boosting for supervised learning.
problem Supervised learning problems.
method Diffusion Boosting paradigm and Diffusion Boosted Trees (DBT).
result DBT outperforms deep neural network-based diffusion models and is effective on real-world classification tasks.
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
problem Lack of well-calibrated predictive uncertainties in gradient boosting machines.
method Variational inference with soft decision trees.
result Variational soft GBMs provide useful uncertainty estimates and maintain good predictive performance.
The paper proposes a method to assess and improve data quality using GBDT training dynamics.
problem Improving data quality in datasets with noisy labels and varying contributions.
method Metrics computed from training dynamics of Gradient Boosting Decision Trees (GBDTs).
result The method achieved the best results compared to other approaches.
Decision trees perform well in complex interactions, even when interactions are not fully accounted for.
problem Interpreting complex interactions in machine learning models.
method Experiments on datasets and two methods for robust GLMs.
result Tree depth compensates for model misspecification, enhancing performance in complex scenarios.
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…
UnmaskingTrees improves tabular data imputation and generation using gradient-boosted decision trees.
problem Traditional methods outperform advanced deep learning techniques on tabular data imputation benchmarks.
method UnmaskingTrees employs gradient-boosted decision trees to incrementally unmask features for imputation and generation.
result UnmaskingTrees outperforms state-of-the-art methods on tabular imputation and generation benchmarks.
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.
Dynamic CBDT improves treatment effect estimation in clinical data.
problem Estimating heterogeneous treatment effects in observational data with high accuracy and interpretability.
method Dynamic Regularized Causal Boosted Decision Trees (CBDT) integrating variance regularization and calibration.
result Significantly improved estimation accuracy and reliable coverage of true treatment effects.
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.
Boosting meta-trees improve decision tree performance.
problem Overfitting in decision trees.
method Boosting approach to construct multiple meta-trees.
result Ensembles of meta-trees prevent overfitting.
ENTMOOT integrates tree models for better decision-making and optimization.
problem Difficult optimization of tree models and lack of reliable uncertainty measures.
method Integrates already trained tree models into optimization problems with a reliable uncertainty measure.
result Proves globally optimal solutions for optimization problems.
Unified comparison of gradient boosting algorithms for insurance claims.
problem Improving predictive accuracy and computational efficiency in insurance claim prediction.
method Unified notation and comprehensive numerical study comparing 12 gradient boosting algorithms on 5 datasets.
result No trade-off between model adequacy and predictive accuracy.
DOFEN improves DNN performance on tabular data benchmarks.
problem DOFEN tackles the performance gap between DNNs and tree-based models on tabular data.
method DOFEN uses a two-level rODT forest ensembling process inspired by oblivious decision trees.
result DOFEN achieves state-of-the-art results on the Tabular Benchmark.
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.
Research creates a machine learning model for predicting TAVI patient mortality.
problem Lack of robust risk scores for TAVI patients.
method Gradient boosting on decision trees, feature analysis and selection, model validation.
result Model outperforms existing risk scores with AUC of 0.83.
New algorithm speeds up robustness verification for tree-based models.
problem Formal robustness verification of tree-based models, especially ensembles.
method Reformulated as max-clique problem on a multi-partite graph with bounded boxicity; developed efficient multi-level verification algorithm.
result Tight lower bounds on robustness of decision tree ensembles, hundreds of times faster than previous approach.
Unified method for local GBDT feature contributions.
problem Need for local model interpretation for GBDT.
method Unified computation mechanism for instance-level feature contributions.
result Unified computation mechanism for GBDT feature contributions.
Paper improves GBDT accuracy in federated learning.
problem Efficiency and accuracy issues in federated GBDT training.
method Uses locality-sensitive hashing for tree boosting, with relaxed privacy constraints.
result Significantly improves predictive accuracy compared to local data training.
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.
Efficiently prunes features in boosted decision trees for large datasets.
problem High cost of training decision trees in each round for large-scale datasets.
method Develops a highly efficient algorithm for computing exact greedy-optimal decision trees using multi-arm bandit ideas.
result Empirically demonstrates near-optimal performance compared to state-of-the-art methods and derived lower bounds.
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.
LoBoost improves local conformal prediction for gradient-boosted trees without extra data splits.
problem Quantifying uncertainty in gradient-boosted tree predictions.
method Model-native local conformal prediction using leaf structure.
result Competitive interval quality and improved test MSE with large calibration speedups.
New test improves tree ensemble pruning for better model performance.
problem Lack of robust theoretical justification for penalty terms in tree ensembles.
method Developed a novel hypothesis test for tree ensemble split quality.
result Significant reduction in out-of-sample loss using the new test.
OMLT combines ML and optimization for solving complex problems.
problem Solving complex decision-making problems in computer science and engineering.
method OMLT integrates neural networks and gradient-boosted trees into optimization problems using machine learning.
result OMLT seamlessly integrates with Pyomo and solves real-world problems.
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.