{"total":100,"rows":[{"id":"2304.12210","title":"A Cookbook of Self-Supervised Learning","created":"2023-04-24","cited_by_count":250,"area":null},{"id":"2205.09328","title":"TransTab: Learning Transferable Tabular Transformers Across Tables","created":"2022-05-19","cited_by_count":132,"area":null},{"id":"2305.06090","title":"XTab: Cross-table Pretraining for Tabular Transformers","created":"2023-05-10","cited_by_count":81,"area":null},{"id":"2404.09491","title":"Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning","created":"2024-04-15","cited_by_count":81,"area":null},{"id":"2304.09355","title":"To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review","created":"2023-04-19","cited_by_count":72,"area":null},{"id":"2303.14080","title":"Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data","created":"2023-03-24","cited_by_count":69,"area":null},{"id":"2206.15306","title":"Transfer Learning with Deep Tabular Models","created":"2022-06-30","cited_by_count":52,"area":null},{"id":"2405.15613","title":"Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach","created":"2024-05-24","cited_by_count":50,"area":null},{"id":"2303.00918","title":"STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables","created":"2023-03-02","cited_by_count":49,"area":null},{"id":"2207.03208","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","created":"2022-07-07","cited_by_count":43,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2303.05506","title":"TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization","created":"2023-03-09","cited_by_count":32,"area":null},{"id":"2407.07582","title":"TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data","created":"2024-07-10","cited_by_count":32,"area":null},{"id":"2209.08060","title":"PTab: Using the Pre-trained Language Model for Modeling Tabular Data","created":"2022-09-15","cited_by_count":30,"area":null},{"id":"2407.03257","title":"Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later","created":"2024-07-03","cited_by_count":30,"area":null},{"id":"2302.12028","title":"Novel Class Discovery: an Introduction and Key Concepts","created":"2023-02-22","cited_by_count":24,"area":null},{"id":"2305.12081","title":"MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement","created":"2023-05-20","cited_by_count":24,"area":null},{"id":"2310.07338","title":"From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models","created":"2023-10-11","cited_by_count":24,"area":null},{"id":"2311.05877","title":"A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning","created":"2023-11-10","cited_by_count":24,"area":null},{"id":"2307.04308","title":"Towards Cross-Table Masked Pretraining for Web Data Mining","created":"2023-07-10","cited_by_count":21,"area":null},{"id":"2408.10548","title":"Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution","created":"2024-08-20","cited_by_count":19,"area":null},{"id":"2410.12034","title":"A Survey on Deep Tabular Learning","created":"2024-10-15","cited_by_count":18,"area":null},{"id":"2310.07756","title":"Self-supervised Representation Learning From Random Data Projectors","created":"2023-10-11","cited_by_count":15,"area":null},{"id":"2402.01204","title":"A Survey on Self-Supervised Learning for Non-Sequential Tabular Data","created":"2024-02-02","cited_by_count":15,"area":null},{"id":"2410.05016","title":"T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data","created":"2024-10-07","cited_by_count":14,"area":null},{"id":"2401.02143","title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2405.07414","title":"Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains","created":"2024-05-13","cited_by_count":12,"area":null},{"id":"2401.01641","title":"Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences","created":"2024-01-03","cited_by_count":11,"area":null},{"id":"2304.03543","title":"HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets","created":"2023-04-07","cited_by_count":10,"area":null},{"id":"2305.10308","title":"Rethinking Data Augmentation for Tabular Data in Deep Learning","created":"2023-05-17","cited_by_count":10,"area":null},{"id":"2408.11063","title":"Tabular Transfer Learning via Prompting LLMs","created":"2024-08-09","cited_by_count":10,"area":null},{"id":"2210.15865","title":"Completely Heterogeneous Federated Learning","created":"2022-10-28","cited_by_count":9,"area":null},{"id":"2401.04266","title":"Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking","created":"2024-01-08","cited_by_count":9,"area":null},{"id":"2205.08358","title":"Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data","created":"2022-05-17","cited_by_count":7,"area":null},{"id":"2307.12198","title":"NCART: Neural Classification and Regression Tree for Tabular Data","created":"2023-07-23","cited_by_count":7,"area":null},{"id":"2207.02773","title":"DIWIFT: Discovering Instance-wise Influential Features for Tabular Data","created":"2022-07-06","cited_by_count":6,"area":null},{"id":"2209.01217","title":"A Method for Discovering Novel Classes in Tabular Data","created":"2022-09-02","cited_by_count":6,"area":null},{"id":"2209.13824","title":"Label Distribution Learning via Implicit Distribution Representation","created":"2022-09-28","cited_by_count":6,"area":null},{"id":"2306.09222","title":"Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization","created":"2023-06-15","cited_by_count":6,"area":null},{"id":"2505.19825","title":"Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding","created":"2025-05-26","cited_by_count":6,"area":null},{"id":"2309.00855","title":"DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal","created":"2023-09-02","cited_by_count":5,"area":null},{"id":"2511.06961","title":"Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings","created":"2025-11-10","cited_by_count":5,"area":null},{"id":"2601.11311","title":"FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning","created":"2026-01-16","cited_by_count":5,"area":null},{"id":"2211.10549","title":"Local Contrastive Feature learning for Tabular Data","created":"2022-11-19","cited_by_count":4,"area":null},{"id":"2309.17339","title":"Scaling Experiments in Self-Supervised Cross-Table Representation Learning","created":"2023-09-29","cited_by_count":4,"area":null},{"id":"2507.09837","title":"A Pre-training Framework for Relational Data with Information-theoretic Principles","created":"2025-07-14","cited_by_count":4,"area":null},{"id":"2305.15843","title":"TabGSL: Graph Structure Learning for Tabular Data Prediction","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2403.00963","title":"Tree-Regularized Tabular Embeddings","created":"2024-03-01","cited_by_count":3,"area":null},{"id":"2404.17489","title":"Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation","created":"2024-04-26","cited_by_count":3,"area":null},{"id":"2605.18147","title":"Foundation Models for Credit Risk Prediction: A Game Changer?","created":"2026-05-18","cited_by_count":3,"area":null},{"id":"2302.14013","title":"Revisiting Self-Training with Regularized Pseudo-Labeling for Tabular Data","created":"2023-02-27","cited_by_count":2,"area":null},{"id":"2307.09321","title":"Exploiting Field Dependencies for Learning on Categorical Data","created":"2023-07-18","cited_by_count":2,"area":null},{"id":"2403.02846","title":"FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models","created":"2024-03-05","cited_by_count":2,"area":null},{"id":"2403.15790","title":"Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets","created":"2024-03-23","cited_by_count":2,"area":null},{"id":"2409.06123","title":"Contrastive Federated Learning with Tabular Data Silos","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2502.18847","title":"TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2503.15089","title":"Continual Contrastive Learning on Tabular Data with Out of Distribution","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2202.01672","title":"SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification","created":"2022-02-03","cited_by_count":1,"area":null},{"id":"2202.09248","title":"Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge","created":"2022-02-18","cited_by_count":1,"area":null},{"id":"2210.00825","title":"Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data","created":"2022-10-03","cited_by_count":1,"area":null},{"id":"2306.15661","title":"Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs","created":"2023-06-27","cited_by_count":1,"area":null},{"id":"2308.00177","title":"Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity","created":"2023-07-31","cited_by_count":1,"area":null},{"id":"2310.06380","title":"CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidence","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2312.07753","title":"Polynomial-based Self-Attention for Table Representation learning","created":"2023-12-12","cited_by_count":1,"area":null},{"id":"2402.18164","title":"Autoencoder-based General Purpose Representation Learning for Customer Embedding","created":"2024-02-28","cited_by_count":1,"area":null},{"id":"2405.11884","title":"Vertical Federated Learning Hybrid Local Pre-training","created":"2024-05-20","cited_by_count":1,"area":null},{"id":"2412.12864","title":"Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset","created":"2024-12-17","cited_by_count":1,"area":null},{"id":"2603.16569","title":"Deep Tabular Representation Corrector","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2605.11846","title":"Martingale-Consistent Self-Supervised Learning","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2605.23241","title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2206.08564","title":"MET: Masked Encoding for Tabular Data","created":"2022-06-17","cited_by_count":0,"area":null},{"id":"2208.03249","title":"Parameter Averaging for Feature Ranking","created":"2022-08-05","cited_by_count":0,"area":null},{"id":"2212.00892","title":"Progressive Feature Upgrade in Semi-supervised Learning on Tabular Domain","created":"2022-12-01","cited_by_count":0,"area":null},{"id":"2304.13538","title":"ElegansNet: a brief scientific report and initial experiments","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2305.19290","title":"Global Layers: Non-IID Tabular Federated Learning","created":"2023-05-29","cited_by_count":0,"area":null},{"id":"2310.07598","title":"Survey on Imbalanced Data, Representation Learning and SEP Forecasting","created":"2023-10-11","cited_by_count":0,"area":null},{"id":"2311.09962","title":"Self-supervised learning of multi-omics embeddings in the low-label, high-data regime","created":"2023-11-16","cited_by_count":0,"area":null},{"id":"2401.14442","title":"Improving Antibody Humanness Prediction using Patent Data","created":"2024-01-25","cited_by_count":0,"area":null},{"id":"2402.03740","title":"BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2408.06743","title":"Class-aware and Augmentation-free Contrastive Learning from Label Proportion","created":"2024-08-13","cited_by_count":0,"area":null},{"id":"2412.06265","title":"Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics","created":"2024-12-09","cited_by_count":0,"area":null},{"id":"2412.10941","title":"APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2502.10095","title":"Representation Learning on Out of Distribution in Tabular Data","created":"2025-02-14","cited_by_count":0,"area":null},{"id":"2502.11205","title":"Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2508.13672","title":"ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2510.02017","title":"FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2510.02349","title":"An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2512.09322","title":"Self-Supervised Learning with Gaussian Processes","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2601.15751","title":"Tabular Incremental Inference","created":"2026-01-22","cited_by_count":0,"area":null},{"id":"2604.07940","title":"A Systematic Framework for Tabular Data Disentanglement","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2607.00113","title":"SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.10077","title":"TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2607.27027","title":"TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.02348","title":"Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.24381","title":"When Does Self-Supervised Pretraining Help Tabular Models? A Study of Label Scarcity and Missing Data","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.01262","title":"Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.32764","title":"AnchorMixGAN: Anchor-Aligned Generative Semi-Supervision for DDoS Detection in Cloud-Integrated IoT Networks","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.13818","title":"TabMF: Learning Dataset Representations with Tabular Foundation Models","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.15235","title":"MoMaTune: Model, Match, Then Tune Language Models for Numerical Records","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}