tabular-classification

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

29 shown of 29 papers with code (48 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• TabTransformer: Tabular Data Modeling Using Contextual Embeddings
  11 Dec 2020 12 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 4 pointer-only (licence)
  We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning.
• Revisiting Deep Learning Models for Tabular Data
  22 Jun 2021 11 repositories listed Syntology ran 4 of 22 samples · 18 unverified
  The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets.
• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
  5 Jul 2022 7 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification…
• JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
  17 Feb 2025 2 repositories listed Syntology ran 0 of 19 samples · 19 unverified
  We introduce a simple method for probabilistic predictions on tabular data based on Large Language Models (LLMs) called JoLT (Joint LLM Process for Tabular data).
• TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
  17 Feb 2024 2 repositories listed Syntology ran 10 of 17 samples · 7 unverified
  Notably, TabPFN achieves very strong performance on small tabular datasets but is not designed to make predictions for datasets of size larger than 1000.
• GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
  18 Jul 2022 2 repositories listed Syntology ran 2 of 14 samples · 12 unverified
  We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF).
• The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling
  1 Jan 2022 2 repositories listed
  There is an increasing interest in the application of deep learning architectures to tabular data.
• Generative Imputation and Stochastic Prediction
  22 May 2019 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
• Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
  3 Mar 2025 1 repository listed
  Through this work, we address the challenges of efficiently handling large datasets via PFN-based models, paving the way for faster and more effective tabular data classification training and prediction process.
• TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach
  21 Feb 2025 1 repository listed
  Tabular data, prevalent in relational databases and spreadsheets, is fundamental across fields like healthcare, engineering, and finance.
• What exactly has TabPFN learned to do?
  13 Feb 2025 1 repository listed
  TabPFN [Hollmann et al., 2023], a Transformer model pretrained to perform in-context learning on fresh tabular classification problems, was presented at the last ICLR conference.
• Efficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification
  27 Jan 2025 1 repository listed
  Algorithmic level developments like Convolutional Neural Networks, transformers, attention mechanism, Retrieval Augmented Generation and so on have changed Artificial Intelligence.
• How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
  13 Dec 2024 1 repository listed
  A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller prediction models.
• Bayesian Concept Bottleneck Models with LLM Priors
  21 Oct 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy.
• Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations
  10 Oct 2024 1 repository listed
  Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features…
• PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
  3 Sep 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  In practice, we are often faced with small-sized tabular data.
• Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
  19 Jul 2024 1 repository listed
  Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks.
• Transformers with Stochastic Competition for Tabular Data Modelling
  18 Jul 2024 1 repository listed
  We demonstrate that, through the incorporation of these elements, our model yields high performance and marks a significant advancement in the application of deep learning to tabular data.
• Pairwise Difference Learning for Classification
  28 Jun 2024 1 repository listed
  Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression.
• ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
  25 Jun 2024 1 repository listed
  This particularly holds for the combination of query strategies with different learning algorithms into active learning pipelines and examining the impact of the learning algorithm choice.
• MSBoost: Using Model Selection with Multiple Base Estimators for Gradient Boosting
  17 Jun 2024 1 repository listed
  Gradient boosting is a widely used machine learning algorithm for tabular regression, classification and ranking.
• MotherNet: Fast Training and Inference via Hyper-Network Transformers
  14 Dec 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  The child network generated by MotherNet outperforms neural networks trained using gradient descent on small datasets, and is comparable to predictions by TabPFN and standard ML methods like Gradient Boosting.
• Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
  8 Oct 2023 1 repository listed
  This research heralds a paradigm shift in machine learning, paving the way for a new era of robust and precise classification across diverse real-world applications.
• Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
  16 Sep 2023 1 repository listed
  As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests.
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models
  19 Oct 2022 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  We study the application of large language models to zero-shot and few-shot classification of tabular data.
• Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
  1 Dec 2021 1 repository listed
  Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.
• CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations
  18 Aug 2021 1 repository listed
  We believe an actionable recourse should be created based on sound counterfactual explanations originating from the distribution of the ground-truth data and linked to the domain knowledge.
• SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
  29 Jun 2021 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data.
• Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
  4 May 2020 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that…

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.