A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Gradient boosting with randomized trees reduces discontinuities and complexity.
Boost-R uses gradient boosted trees for analyzing recurrence data.
Infinitesimal gradient boosting is a new algorithm derived from gradient boosting.
As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning techniques, though the computational expensiveness severely limits its usage. Stochastic gradient boosting could be adopted to accelerates gr…
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…
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…
Gradient boosting improved with lassoed trees achieves faster convergence.
agtboost speeds up gradient tree boosting with automatic complexity adjustment.
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…
New algorithm improves convergence of gradient boosting trees.
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 …
A new method for automatic gradient tree boosting using information theory.
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classificatio…
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…
Gradient boosting adapted for vector inputs.
Multivariate boosted trees improve forecasting and control by capturing correlated predictions.
sGBM speeds up gradient boosting by parallelizing and adapting base learners.
Proposes new attribution methods for trees with regularization.
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…
We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based on Online Gradient Boosting theory is proved and suggests that ResNet could achieve Online Gradient Boosting regret bounds through neural n…
DBDT uses deep boosting decision trees for fraud detection.
Treeffuser predicts tabular data distributions using gradient-boosted trees.
AGBoost uses attention weights to improve GBM for regression problems.
Functional central limit theorem for kernel gradient flow and infinitesimal gradient boosting
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
Paper studies ensemble probabilistic regression trees for smooth approximations.
StructureBoost improves gradient boosting for complex categorical variables efficiently.
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…
DBT combines diffusion models and boosting for supervised learning.
SketchBoost accelerates GBDT for multioutput problems up to 40x.
UnmaskingTrees improves tabular data imputation and generation using gradient-boosted decision trees.
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…
Classifier evasion consists in finding for a given instance the nearest instance such that the classifier predictions of and are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixe…
LoBoost improves local conformal prediction for gradient-boosted trees without extra data splits.
Develops a new method for decision trees using categorical variable structure.
New test improves tree ensemble pruning for better model performance.
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…
Gradient boosted trees outperform other models in predicting corporate bankruptcy.
Forest tree species mapped with high accuracy using satellite data.
In many applications of supervised learning, multiple classification or regression outputs have to be predicted jointly. We consider several extensions of gradient boosting to address such problems. We first propose a straightforward adaptation of gradient boosting exploiting multiple output regression trees as base le…
In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision trees to a single neural network with one hidden layer and an input transformation. We then relax some properties of this network such as thresh…
Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonp…
SnapBoost uses random base hypothesis classes to improve gradient boosting performance.
Unified comparison of gradient boosting algorithms for insurance claims.
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness o…
The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.
Dynamic CBDT improves treatment effect estimation in clinical data.
Gradient boosting can be seen as Gaussian process inference.