Tabular ML

ML: Machine Learning

Tabular machine learning studies prediction, discovery, and explanation from structured data with numerical, categorical, text, and missing values. Common approaches include gradient-boosted decision trees, neural networks with feature embeddings and attention, pretrained or in-context models, and automated feature engineering. Benchmarks compare these methods on classification, regression, anomaly detection, and specialized tasks, but no model family consistently leads across dataset sizes, feature types, and settings. A central challenge is making results generalize beyond clean IID benchmarks while balancing accuracy with robustness, interpretability, computational cost, and careful evaluation.

Latest papers

116 papers17 papers in the last four weeks, up 143% on the four weeks before. 0.1% of all new papers.

Benchmarking Optimizers for MLPs in Tabular Deep Learning
Apr 16, 2026Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov +1Multilayer Perceptrons Deep Learning Optimization
α Yandex · β HSE University
Analog Optical Inference on Million-Record Mortgage Data
Apr 14, 2026Sofia Berloff, Pavel Koptev, Konstantin MalkovAnalog Computing Photonic Computing
Department of Mathematics, University of York, York, UK · ai1Technologies, 27 Beach Rd Suite 5A Monmouth Beach, NJ 07750, USA
Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction
Apr 11, 2026Adil Derrazi, Javad Pourmostafa Roshan SharamiTabular Prediction Tabular ML
CSAI Department, Tilburg University, The Netherlands
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
Apr 8, 2026Dihong Jiang, Ruoqi Cao, Zhiyuan Dang +8Tabular ML Tabular Prediction
Huawei Cloud · School of Intelligence Science and Technology, Peking University
Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
Apr 6, 2026James Hu, Mahdi GhelichiTabular ML Tabular ICL
Model Development Innovation, Risk Management TD Bank, Toronto, Canada
Which Leakage Types Matter? A Quantitative Landscape Across 2,047 Benchmark Datasets
Apr 5, 2026Simon RothData Leakage Tabular ML
Cooperative Coevolution versus Monolithic Evolutionary Search for Semi-Supervised Tabular Classification
Apr 1, 2026Jamal ToutouhSemi-Supervised Learning Tabular ML
ITIS, Universidad de Málaga Spain
Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms
Mar 22, 2026Ali AkramTabular ML
CliftonLarsonAllen (CLA)
Enhancing LIME using Neural Decision Trees
Mar 21, 2026Mohamed Aymen Bouyahia, Argyris KalogeratosInterpretable ML Explainable Artificial Intelligence
ENS Paris-Saclay, Centre Borelli, CNRS, Gif-Sur-Yvette, France · Le Crédit Lyonnais, Paris, France
LassoFlexNet: Flexible Neural Architecture for Tabular Data
Mar 21, 2026Kry Yik Chau Lui, Cheng Chi, Kishore Basu +1Tabular ML Tabular Representation Learning
RBC Borealis · RBC
CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
Mar 18, 2026Isabella Marasco, Davide Evangelista, Elena Loli Piccolomini +1Continual Learning Tabular ML
Department of Computer Science and Engineering University of Bologna Bologna, 40126, Italy
Deep Tabular Representation Corrector
Mar 17, 2026Hangting Ye, Peng Wang, Wei Fan +4Tabular ML Tabular Representation Learning
School of Artificial Intelligence, Jilin University, China. · School of Computer Science, University of Auckland, New Zealand. · Chinese University of Hong Kong, Shenzhen, China. +3
TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins
Mar 16, 2026Shovon Niverd Pereira, Krishna Khadka, Yu LeiFeature Interaction Modeling Data-Free Knowledge Distillation
Department of Computer Science and Engineering, The University of Texas at Arlington
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
Mar 14, 2026Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer +1Graph Robustness Tabular ML
Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain · Institute of IT Management and Digitization Research (IFID), FOM University of Applied Sciences in Economics and Management, Düsseldorf, Germany
Where Do Flow Semantics Reside? A Protocol-Native Tabular Pretraining Paradigm for Encrypted Traffic Classification
Mar 9, 2026Sizhe Huang, Zitong Li, Shujie YangEncrypted Traffic Classification Encrypted Network Traffic Analysis
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
TML-Bench: Benchmark for Data Science Agents on Tabular ML Tasks
Mar 5, 2026Mykola PinchukAI Agent Benchmarks Agent Benchmarks
Independent Researcher, San Jose, USA
An interpretable prototype parts-based neural network for medical tabular data
Mar 5, 2026Jacek Karolczak, Jerzy StefanowskiInterpretable ML Prototype-Based Classification
Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60-695 Poznań, Poland
SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
Mar 4, 2026Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan +6Class-Incremental Learning Tabular ML
Department of Computer Science, Virginia Tech, Blacksburg, VA, USA · Department of Computer Science, University of Texas at Dallas, Dallas, TX, USA · DEVCOM Army Research Laboratory, Adelphi, MD, USA +1
Vectorized Dynamic Histograms for Sparse Oblique Forests
Feb 27, 2026Ariel Lubonja, Jungsang Yoon, Haoyin Xu +6Gradient-Boosted Decision Trees Tabular ML
Johns Hopkins University Baltimore, Maryland, USA · Google Zurich, Switzerland
KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
Feb 26, 2026Mingming Zhang, Pengfei Shi, Zhiqing Xiao +8Tabular ML Tabular Prediction
Zhejiang University Hangzhou, China · Ant Group Hangzhou, China
C $^{2}$ TC: A Training-Free Framework for Efficient Tabular Data Condensation
Feb 25, 2026Sijia Xu, Fan Li, Xiaoyang Wang +2Dataset Condensation Data-Efficient Learning
University of New South Wales, Australia · Shanghai Jiao Tong University, China
Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children's Health
Feb 23, 2026Joyanta Jyoti MondalTabular ML Logistic Regression
Department of Computer and Information Sciences, University of Delaware, United States
FAMOSE: A ReAct Approach to Automated Feature Discovery
Feb 19, 2026Keith Burghardt, Jienan Liu, Sadman Sakib +2Automated Feature Engineering Large Language Model-Based Feature Engineering
Amazon.com, Inc., 410 Terry Ave N, Seattle, WA 98109, United States.
Grables: When Graph Structure Helps Tabular Prediction
Feb 3, 2026Tamara Cucumides, Floris GeertsTabular ML Tabular Prediction
Department of Computer Science, University of Antwerp. Belgium.
Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises
Aug 30, 2025Lu Han, Xiuying WangFinancial Services Interpretable ML
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China · School of Computer Science, University of Sydney, NSW 2006, Australia
Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
May 1, 2025Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls BenjaminEvent Time Prediction Transformer
Exelon Corporation, Chicago, IL, USA

Apr 16, 2026J/K move · Enter open · S save

Benchmarking Optimizers for MLPs in Tabular Deep Learning

Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov, Artem Babenko

α Yandex · β HSE University

Multilayer Perceptrons Deep Learning Optimization Neural Network Optimization Muon Optimizer Tabular ML

Open paper AskPDF arXiv

MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models. Unlike architecture design, however, the choice of optimizer for tabular DL has not been examined systematically, despite new optimizers showing promise in other domains. To fill this gap, we benchmark 15 optimizers on 17 tabular datasets for training MLP-based models in the standard supervised learning setting under a shared experiment protocol. Our main finding is that the Muon optimizer consistently outperforms AdamW, and thus should be considered a strong and practical choice for practitioners and researchers, if the associated training efficiency overhead is affordable. Additionally, we find exponential moving average of model weights to be a simple yet effective technique that improves AdamW on vanilla MLPs, though its effect is less consistent across model variants.