Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation

• URL: http://arxiv.org/abs/2311.00055v1
• Date: Tue, 31 Oct 2023 18:03:54 GMT
• Title: Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation
• Authors: Han-Jia Ye, Qi-Le Zhou, De-Chuan Zhan
• Abstract summary: We propose Tabular data Pre-Training via Meta-representation (TabPTM) A deep neural network is then trained to associate these meta-representations with dataset-specific classification confidences. Experiments validate that TabPTM achieves promising performance in new datasets, even under few-shot scenarios.
• Score: 67.30538142519067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tabular data is prevalent across various machine learning domains. Yet, the inherent heterogeneities in attribute and class spaces across different tabular datasets hinder the effective sharing of knowledge, limiting a tabular model to benefit from other datasets. In this paper, we propose Tabular data Pre-Training via Meta-representation (TabPTM), which allows one tabular model pre-training on a set of heterogeneous datasets. Then, this pre-trained model can be directly applied to unseen datasets that have diverse attributes and classes without additional training. Specifically, TabPTM represents an instance through its distance to a fixed number of prototypes, thereby standardizing heterogeneous tabular datasets. A deep neural network is then trained to associate these meta-representations with dataset-specific classification confidences, endowing TabPTM with the ability of training-free generalization. Experiments validate that TabPTM achieves promising performance in new datasets, even under few-shot scenarios.

Related papers

• TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation [91.50296404732902]
  We introduce TabDiff, a joint diffusion framework that models all multi-modal distributions of tabular data in one model. Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data. TabDiff achieves superior average performance over existing competitive baselines, with up to $22.5%$ improvement over the state-of-the-art model on pair-wise column correlation estimations.
  arXiv  Detail & Related papers  (2024-10-27T22:58:47Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting [23.461204546005387]
  TabMDA is a novel method for manifold data augmentation on tabular data. It exploits a pre-trained in-context model, such as TabPFN, to map the data into an embedding space. We evaluate TabMDA on five standard classifiers and observe significant performance improvements across various datasets.
  arXiv  Detail & Related papers  (2024-06-03T21:51:13Z)
• Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data [35.61663559675556]
  Cross-dataset pretraining has shown notable success in various fields. In this study, we introduce a cross-table pretrained Transformer, XTFormer, for versatile downstream tabular prediction tasks. Our methodology is pretraining XTFormer to establish a "meta-function" space that encompasses all potential feature-target mappings.
  arXiv  Detail & Related papers  (2024-06-01T03:24:31Z)
• Making Pre-trained Language Models Great on Tabular Prediction [50.70574370855663]
  The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. We present TP-BERTa, a specifically pre-trained LM for tabular data prediction. A novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names.
  arXiv  Detail & Related papers  (2024-03-04T08:38:56Z)
• Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces [43.67453625260335]
  We propose a novel approach to few-shot learning involving knowledge sharing between datasets with heterogeneous feature spaces. FLAT learns low-dimensional embeddings of datasets and their individual columns, which facilitate knowledge transfer and generalization to previously unseen datasets. A decoder network parametrizes the predictive target network, implemented as a Graph Attention Network, to accommodate the heterogeneous nature of tabular datasets.
  arXiv  Detail & Related papers  (2023-11-16T17:45:59Z)
• Generative Table Pre-training Empowers Models for Tabular Prediction [71.76829961276032]
  We propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. TapTap can generate high-quality synthetic tables to support various applications, including privacy protection, low resource regime, missing value imputation, and imbalanced classification. It can be easily combined with various backbone models, including LightGBM, Multilayer Perceptron (MLP) and Transformer.
  arXiv  Detail & Related papers  (2023-05-16T06:37:38Z)
• XTab: Cross-table Pretraining for Tabular Transformers [29.419276738753968]
  XTab is a framework for cross-table pretraining of tabular transformers on datasets from various domains. We show that XTab consistently boosts the generalizability, learning speed, and performance of multiple tabular transformers. We achieve superior performance than other state-of-the-art tabular deep learning models on various tasks such as regression, binary, and multiclass classification.
  arXiv  Detail & Related papers  (2023-05-10T12:17:52Z)
• SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning [5.5616364225463055]
  We introduce a new framework, Subsetting features of Tabular data (SubTab) In this paper, we introduce a new framework, Subsetting features of Tabular data (SubTab) We argue that reconstructing the data from the subset of its features rather than its corrupted version in an autoencoder setting can better capture its underlying representation.
  arXiv  Detail & Related papers  (2021-10-08T20:11:09Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

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.