XGenBoost: Synthesizing Small and Large Tabular Datasets with XGBoost

• URL: http://arxiv.org/abs/2603.06904v1
• Date: Fri, 06 Mar 2026 21:45:57 GMT
• Title: XGenBoost: Synthesizing Small and Large Tabular Datasets with XGBoost
• Abstract summary: We present XGenBoost, a pair of generative models based on XGBoost.<n>We show that our proposed architectures outperform previous neural- and tree-based generative models.
• Score: 2.509907053583601
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tree ensembles such as XGBoost are often preferred for discriminative tasks in mixed-type tabular data, due to their inductive biases, minimal hyperparameter tuning, and training efficiency. We argue that these qualities, when leveraged correctly, can make for better generative models as well. As such, we present XGenBoost, a pair of generative models based on XGBoost: i) a Denoising Diffusion Implicit Model (DDIM) with XGBoost as score-estimator suited for smaller datasets, and ii) a hierarchical autoregressive model whose conditionals are learned via XGBoost classifiers, suited for large-scale tabular synthesis. The architectures follow from the natural constraints imposed by tree-based learners, e.g., in the diffusion model, combining Gaussian and multinomial diffusion to leverage native categorical splits and avoid one-hot encoding while accurately modelling mixed data types. In the autoregressive model, we use a fixed-order factorization, a hierarchical classifier to impose ordinal inductive biases when modelling numerical features, and de-quantization based on empirical quantile functions to model the non-continuous nature of most real-world tabular datasets. Through two benchmarks, one containing smaller and the other larger datasets, we show that our proposed architectures outperform previous neural- and tree-based generative models for mixed-type tabular synthesis at lower training cost.

Related papers

• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching.<n>We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy.<n>Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models [10.88959673845634]
  TabEBM is a class-conditional generative method using Energy-Based Models (EBMs) Our experiments show that TabEBM generates synthetic data with higher quality and better statistical fidelity than existing methods.
  arXiv  Detail & Related papers  (2024-09-24T14:25:59Z)
• Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
  We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-04-26T06:00:27Z)
• A Hybrid of Generative and Discriminative Models Based on the Gaussian-coupled Softmax Layer [5.33024001730262]
  We propose a method to train a hybrid of discriminative and generative models in a single neural network. We demonstrate that the proposed hybrid model can be applied to semi-supervised learning and confidence calibration.
  arXiv  Detail & Related papers  (2023-05-10T05:48:22Z)
• Deep incremental learning models for financial temporal tabular datasets with distribution shifts [0.9790236766474201]
  The framework uses a simple basic building block (decision trees) to build self-similar models of any required complexity. We demonstrate our scheme using XGBoost models trained on the Numerai dataset and show that a two layer deep ensemble of XGBoost models over different model snapshots delivers high quality predictions.
  arXiv  Detail & Related papers  (2023-03-14T14:10:37Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets [0.0]
  We show that existing deep generative models perform poorly compared to linear approaches generating synthetic samples from the convex space of the minority class. We propose a deep generative model, ConvGeN combining the idea of convex space learning and deep generative models.
  arXiv  Detail & Related papers  (2022-06-20T14:42:06Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• LowFER: Low-rank Bilinear Pooling for Link Prediction [4.110108749051657]
  We propose a factorized bilinear pooling model, commonly used in multi-modal learning, for better fusion of entities and relations. Our model naturally generalizes decomposition Tucker based TuckER model, which has been shown to generalize other models. We evaluate on real-world datasets, reaching on par or state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-08-25T07:33:52Z)
• On the Discrepancy between Density Estimation and Sequence Generation [92.70116082182076]
  log-likelihood is highly correlated with BLEU when we consider models within the same family. We observe no correlation between rankings of models across different families.
  arXiv  Detail & Related papers  (2020-02-17T20:13:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.