{"total":92,"rows":[{"id":"2511.08667","title":"TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models","created":"2025-11-11","cited_by_count":132,"area":null},{"id":"2410.18164","title":"TabDPT: Scaling Tabular Foundation Models on Real Data","created":"2024-10-23","cited_by_count":113,"area":null},{"id":"2410.24210","title":"TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling","created":"2024-10-31","cited_by_count":113,"area":null},{"id":"2510.21204","title":"Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models","created":"2025-10-24","cited_by_count":77,"area":null},{"id":"2506.15692","title":"MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement","created":"2025-05-27","cited_by_count":58,"area":"agents"},{"id":"2502.17361","title":"A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities","created":"2025-02-24","cited_by_count":41,"area":null},{"id":"2502.02527","title":"TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems","created":"2025-02-04","cited_by_count":36,"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":"2506.10707","title":"ConTextTab: A Semantics-Aware Tabular In-Context Learner","created":"2025-06-12","cited_by_count":34,"area":null},{"id":"2505.14415","title":"Table Foundation Models: on knowledge pre-training for tabular learning","created":"2025-05-20","cited_by_count":19,"area":null},{"id":"2505.18125","title":"TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields","created":"2025-05-23","cited_by_count":18,"area":null},{"id":"2501.19195","title":"Rethinking Early Stopping: Refine, Then Calibrate","created":"2025-01-31","cited_by_count":17,"area":null},{"id":"2508.10053","title":"xRFM: Accurate, scalable, and interpretable feature learning models for tabular data","created":"2025-08-12","cited_by_count":16,"area":null},{"id":"2510.06162","title":"TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts","created":"2025-10-07","cited_by_count":16,"area":null},{"id":"2603.03805","title":"Relational In-Context Learning via Synthetic Pre-training with Structural Prior","created":"2026-03-04","cited_by_count":14,"area":null},{"id":"2506.08982","title":"On Finetuning Tabular Foundation Models","created":"2025-06-10","cited_by_count":13,"area":null},{"id":"2608.25774","title":"EXAONE Tabular 1.0 : Technical Report","created":"2026-08-26","cited_by_count":10,"area":null},{"id":"2512.11779","title":"Conditional Coverage Diagnostics for Conformal Prediction","created":"2025-12-12","cited_by_count":8,"area":null},{"id":"2606.07345","title":"TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention","created":"2026-06-05","cited_by_count":8,"area":null},{"id":"2609.17488","title":"LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence","created":"2026-09-15","cited_by_count":8,"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":"2511.03570","title":"TabGemma: Text-Based Tabular ICL via LLM using Continued Pretraining and Retrieval","created":"2025-11-05","cited_by_count":6,"area":null},{"id":"2603.29928","title":"ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules","created":"2026-03-31","cited_by_count":6,"area":null},{"id":"2409.16653","title":"The Credibility Transformer","created":"2024-09-25","cited_by_count":5,"area":null},{"id":"2502.20260","title":"Understanding the Limits of Deep Tabular Methods with Temporal Shift","created":"2025-02-27","cited_by_count":5,"area":null},{"id":"2604.15297","title":"Benchmarking Optimizers for MLPs in Tabular Deep Learning","created":"2026-04-16","cited_by_count":5,"area":null},{"id":"2505.05226","title":"Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2505.16226","title":"Realistic Evaluation of TabPFN v2 in Open Environments","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2508.09888","title":"Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?","created":"2025-08-13","cited_by_count":4,"area":null},{"id":"2605.04363","title":"Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment","created":"2026-05-06","cited_by_count":4,"area":null},{"id":"2609.04540","title":"Mitra-v2 Technical Report","created":"2026-09-03","cited_by_count":4,"area":null},{"id":"2505.13421","title":"Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard","created":"2025-05-19","cited_by_count":3,"area":"lr"},{"id":"2506.22429","title":"Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks","created":"2025-06-27","cited_by_count":3,"area":null},{"id":"2609.22866","title":"Causilo Technical Report","created":"2026-09-19","cited_by_count":3,"area":null},{"id":"2502.03608","title":"(GG) MoE vs. MLP on Tabular Data","created":"2025-02-05","cited_by_count":2,"area":"moe"},{"id":"2503.06985","title":"Learning Decision Trees as Amortized Structure Inference","created":"2025-03-10","cited_by_count":2,"area":null},{"id":"2504.08712","title":"Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks","created":"2025-04-11","cited_by_count":2,"area":null},{"id":"2505.14312","title":"MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2509.04430","title":"Unveiling the Role of Data Uncertainty in Tabular Deep Learning","created":"2025-09-04","cited_by_count":2,"area":null},{"id":"2510.04660","title":"An Attention-based Feature Memory Design for Energy-Efficient Continual Learning","created":"2025-10-06","cited_by_count":2,"area":null},{"id":"2602.03945","title":"Grables: When Graph Structure Helps Tabular Prediction","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2605.10616","title":"MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2605.30188","title":"CalArena: A Large-Scale Post-Hoc Calibration Benchmark","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2504.04935","title":"RCCFormer: A Robust Crowd Counting Network Based on Transformer","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2505.15657","title":"LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2508.06548","title":"Factor Augmented Supervised Learning with Text Embeddings","created":"2025-08-06","cited_by_count":1,"area":null},{"id":"2511.15941","title":"iLTM: Integrated Large Tabular Model","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2512.03678","title":"Feature-aware Modulation for Learning from Temporal Tabular Data","created":"2025-12-03","cited_by_count":1,"area":null},{"id":"2601.06645","title":"A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking","created":"2026-01-10","cited_by_count":1,"area":null},{"id":"2602.02523","title":"TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis","created":"2026-01-25","cited_by_count":1,"area":null},{"id":"2602.21717","title":"C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation","created":"2026-02-25","cited_by_count":1,"area":null},{"id":"2604.06814","title":"OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale","created":"2026-04-08","cited_by_count":1,"area":null},{"id":"2605.07335","title":"CellScientist: From Execution Feedback to Auditable Model-Revision Trajectories for Cellular Perturbation Prediction","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.28418","title":"Explaining Tabular Foundation Model Differences Through Meta-Features","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2606.30452","title":"Exploring Differences Between Tabular Enterprise Data and Public Benchmarks","created":"2026-06-29","cited_by_count":1,"area":null},{"id":"2607.03659","title":"A Fair Benchmarking of Deep Relational Database Learning Models","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2608.03565","title":"Enhancing Tabular Learners with Context-Aware Semantic Embeddings","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2608.18849","title":"GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models","created":"2026-08-19","cited_by_count":1,"area":null},{"id":"2608.27882","title":"SOMTab: Set-Order Mamba for Efficient Tabular In-Context Learning","created":"2026-08-28","cited_by_count":1,"area":"ssm"},{"id":"2506.02406","title":"Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2508.13657","title":"In-Context Decision Making for Optimizing Complex AutoML Pipelines","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2511.15350","title":"Multi-layer Stack Ensembles for Time Series Forecasting","created":"2025-11-19","cited_by_count":0,"area":null},{"id":"2602.07358","title":"UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2602.24230","title":"V-ECE: Estimating General Expected Calibration Errors","created":"2026-02-27","cited_by_count":0,"area":null},{"id":"2605.05780","title":"Von Neumann Networks","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.06047","title":"TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.13922","title":"XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.14764","title":"Compositional Sparsity as an Inductive Bias for Neural Architecture Design","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.28089","title":"BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.29763","title":"TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2606.30336","title":"FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2606.31474","title":"TabPATE: Differentially Private Tabular In-Context Learning Without Public Data","created":"2026-06-30","cited_by_count":0,"area":"safety"},{"id":"2607.05389","title":"InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics","created":"2026-07-06","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":"2607.18278","title":"FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2608.00608","title":"From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy","created":"2026-08-01","cited_by_count":0,"area":null},{"id":"2608.09162","title":"Tabular Numeric Stretch Transformation","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.18919","title":"Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.02766","title":"Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.03106","title":"Scaling Laws, Tabular Data and Actuarial Ratemaking Models","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.03880","title":"Xiaomi-TabLDM: A Tabular Foundation Model Technical Report","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.06080","title":"PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell Us","created":"2026-09-05","cited_by_count":0,"area":null},{"id":"2609.07441","title":"TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables","created":"2026-09-07","cited_by_count":0,"area":null},{"id":"2609.16309","title":"Agentic Search Spaces for Tabular Machine Learning","created":"2026-09-14","cited_by_count":0,"area":"agents"},{"id":"2609.23574","title":"PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2610.01435","title":"Distillation of Tabular Foundation Models into Efficient Predictors","created":"2026-10-01","cited_by_count":0,"area":"distill"},{"id":"2610.01725","title":"RelICL: Training-free Relational Learning with Tabular Foundation Models","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.12005","title":"Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits","created":"2026-10-08","cited_by_count":0,"area":"lr"},{"id":"2610.12904","title":"GIST: Generator-state-Informed Supervision for Tabular Foundation Models","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.13909","title":"Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.17065","title":"TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19206","title":"DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}