tabular-regression

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

1 dataset 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

14 shown of 14 papers with code (19 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.

• 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.
• Probabilistic Calibration by Design for Neural Network Regression
  18 Mar 2024 2 repositories listed
  To address the miscalibration issue of neural networks, various methods have been proposed to improve calibration, including post-hoc methods that adjust predictions after training and regularization methods that act…
• A Framework and Benchmark for Deep Batch Active Learning for Regression
  17 Mar 2022 2 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.
• Synthetic-Powered Predictive Inference
  19 May 2025 1 repository listed
  When the score distributions are well aligned, SPPI yields substantially tighter and more informative prediction sets than standard conformal prediction.
• Decoding-based Regression
  31 Jan 2025 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  Language models have recently been shown capable of performing regression tasks wherein numeric predictions are represented as decoded strings.
• APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
  14 Dec 2024 1 repository listed
  Tabular data are fundamental in common machine learning applications, ranging from finance to genomics and healthcare.
• Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression
  9 Oct 2024 1 repository listed
  Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets.
• Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
  27 Sep 2024 1 repository listed
  When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool.
• 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.
• 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.
• Improving Neural Additive Models with Bayesian Principles
  26 May 2023 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks.
• SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
  2 Dec 2022 1 repository listed
  Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery.
• Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
  21 Jun 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating…
• Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
  23 May 2022 1 repository listed Syntology ran 0 of 12 samples · 12 unverified
  We also find that IBUG can achieve improved probabilistic performance by using different base GBRT models, and can more flexibly model the posterior distribution of a prediction than competing methods.

Syntology lines on 6 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.