Tabular Deep Learning papers, 2023–2026

Key findings

• Tabular Deep Learning fell from 150 papers in 2023 to 33 in 2026, so interest is cooling.
• NeurIPS publishes the most Tabular Deep Learning work: 133 papers from 2023 to 2026, 62% of the four-venue total.
• The fastest-growing overlap is with Simulation for Driving: papers tagged with both rose from 3 in 2023 to 6 in 2026.
• Among the 5 topics under Graph and Structured Data, it ranks #5 by 2026 paper count; the fastest grower in the group, Time Series, is up 3.2x versus 0.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Coherent Routing in Decision Trees: Phase-Interference Learning for Interpretable Tabular Prediction
• ICML 2026Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
• ICLR 2026Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
• CVPR 2026MultiModalPFN Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
• NeurIPS 2025ConTextTab: A Semantics-Aware Tabular In-Context Learner
• ICML 2025Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data
• ICLR 2025Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
• NeurIPS 2024WikiDBs: A Large-Scale Corpus Of Relational Databases From Wikidata
• ICML 2024BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
• ICLR 2024GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
• NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
• ICML 2023Compressing Tabular Data via Latent Variable Estimation
• ICLR 2023TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing
• NeurIPS 2026LLM-Enhanced Random Forests in Orthogonal Hyperbolic Subspaces for Tabular Learning
• ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
• NeurIPS 2025Feature-aware Modulation for Learning from Temporal Tabular Data
• ICML 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attention
• ICLR 2025SAVA: Scalable Learning-Agnostic Data Valuation
• NeurIPS 2024Evaluating language models as risk scores
• ICML 2024Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images

All Tabular Deep Learning papers

150 papers, page 2 of 3

51. NeurIPS 2023Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
52. NeurIPS 2023Attacks on Online Learners: a Teacher-Student Analysis
53. NeurIPS 2023BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
54. NeurIPS 2023Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
55. NeurIPS 2023Benchmarking Distribution Shift in Tabular Data with TableShift
56. NeurIPS 2023Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
57. NeurIPS 2023BIOT: Biosignal Transformer for Cross-data Learning in the Wild
58. NeurIPS 2023CBD: A Certified Backdoor Detector Based on Local Dominant Probability
59. NeurIPS 2023Characteristic Circuits
60. NeurIPS 2023Characterizing Out-of-Distribution Error via Optimal Transport
61. NeurIPS 2023Collaborative Learning via Prediction Consensus
62. NeurIPS 2023Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time
63. NeurIPS 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
64. NeurIPS 2023Convolution Monge Mapping Normalization for learning on sleep data
65. NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
66. NeurIPS 2023Data Quality in Imitation Learning
67. NeurIPS 2023Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances
68. NeurIPS 2023Data-Informed Geometric Space Selection
69. NeurIPS 2023Deep Fractional Fourier Transform
70. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
71. NeurIPS 2023Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
72. NeurIPS 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
73. NeurIPS 2023Differentiable sorting for censored time-to-event data.
74. NeurIPS 2023Differentially Private Image Classification by Learning Priors from Random Processes
75. NeurIPS 2023Distributed Personalized Empirical Risk Minimization
76. NeurIPS 2023DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
77. NeurIPS 2023Feature Learning for Interpretable, Performant Decision Trees
78. NeurIPS 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
79. NeurIPS 2023Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
80. NeurIPS 2023Flow Factorized Representation Learning
81. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
82. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
83. NeurIPS 2023Geometric Transformer with Interatomic Positional Encoding
84. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
85. NeurIPS 2023Harnessing Hard Mixed Samples with Decoupled Regularizer
86. NeurIPS 2023High dimensional, tabular deep learning with an auxiliary knowledge graph
87. NeurIPS 2023HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
88. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
89. NeurIPS 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
90. NeurIPS 2023Joint Data-Task Generation for Auxiliary Learning
91. NeurIPS 2023Joint Training of Deep Ensembles Fails Due to Learner Collusion
92. NeurIPS 2023Labeling Neural Representations with Inverse Recognition
93. NeurIPS 2023Language Models are Weak Learners
94. NeurIPS 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
95. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
96. NeurIPS 2023Meet in the Middle: A New Pre-training Paradigm
97. NeurIPS 2023Mitigating the Effect of Incidental Correlations on Part-based Learning
98. NeurIPS 2023NCDL: A Framework for Deep Learning on non-Cartesian Lattices
99. NeurIPS 2023New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
100. NeurIPS 2023Noether Embedding: Efficient Learning of Temporal Regularities

FAQ

How many Tabular Deep Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

214 in total: 150 in 2023, 17 in 2024, 14 in 2025, 33 in 2026.

How many Tabular Deep Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 9 Tabular Deep Learning papers, versus 5 the year before.

How many Tabular Deep Learning papers are at ICLR 2026?

ICLR 2026 has 1 Tabular Deep Learning papers, versus 6 the year before.

How many Tabular Deep Learning papers are at ICML 2026?

ICML 2026 has 22 Tabular Deep Learning papers, versus 3 the year before.

How many Tabular Deep Learning papers are at CVPR 2026?

CVPR 2026 has 1 Tabular Deep Learning papers.

Which conference publishes the most Tabular Deep Learning papers?

NeurIPS, with 133 papers from 2023 to 2026.