{"total":360,"rows":[{"id":"2208.01066","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","created":"2022-08-01","cited_by_count":715,"area":null},{"id":"2211.15661","title":"What learning algorithm is in-context learning? Investigations with linear models","created":"2022-11-28","cited_by_count":684,"area":null},{"id":"2207.01848","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","created":"2022-07-05","cited_by_count":674,"area":null},{"id":"2502.05564","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","created":"2025-02-08","cited_by_count":289,"area":null},{"id":"2305.02997","title":"When Do Neural Nets Outperform Boosted Trees on Tabular Data?","created":"2023-05-04","cited_by_count":223,"area":null},{"id":"2309.13638","title":"Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve","created":"2023-09-24","cited_by_count":178,"area":null},{"id":"2311.12424","title":"Looped Transformers are Better at Learning Learning Algorithms","created":"2023-11-21","cited_by_count":149,"area":null},{"id":"2207.04179","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","created":"2022-07-09","cited_by_count":127,"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":"2212.04458","title":"General-Purpose In-Context Learning by Meta-Learning Transformers","created":"2022-12-08","cited_by_count":105,"area":null},{"id":"2311.01933","title":"ForecastPFN: Synthetically-Trained Zero-Shot Forecasting","created":"2023-11-03","cited_by_count":104,"area":null},{"id":"2205.13320","title":"Towards Learning Universal Hyperparameter Optimizers with Transformers","created":"2022-05-26","cited_by_count":81,"area":null},{"id":"2305.17535","title":"PFNs4BO: In-Context Learning for Bayesian Optimization","created":"2023-05-27","cited_by_count":73,"area":null},{"id":"2204.04875","title":"Learning to Induce Causal Structure","created":"2022-04-11","cited_by_count":68,"area":null},{"id":"2305.11097","title":"Statistical Foundations of Prior-Data Fitted Networks","created":"2023-05-18","cited_by_count":65,"area":null},{"id":"2304.05366","title":"The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning","created":"2023-04-11","cited_by_count":63,"area":null},{"id":"2402.00809","title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI","created":"2024-02-01","cited_by_count":62,"area":null},{"id":"2406.05207","title":"Retrieval & Fine-Tuning for In-Context Tabular Models","created":"2024-06-07","cited_by_count":59,"area":null},{"id":"2307.12375","title":"In-Context Learning Learns Label Relationships but Is Not Conventional Learning","created":"2023-07-23","cited_by_count":51,"area":null},{"id":"2506.06039","title":"Do-PFN: In-Context Learning for Causal Effect Estimation","created":"2025-06-06","cited_by_count":51,"area":null},{"id":"2506.07918","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","created":"2025-06-09","cited_by_count":49,"area":null},{"id":"2402.11137","title":"TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks","created":"2024-02-17","cited_by_count":47,"area":null},{"id":"2411.08249","title":"Retrieval Augmented Time Series Forecasting","created":"2024-11-12","cited_by_count":46,"area":"rag"},{"id":"2402.03170","title":"Is Mamba Capable of In-Context Learning?","created":"2024-02-05","cited_by_count":45,"area":"ssm"},{"id":"2305.14701","title":"Modeling rapid language learning by distilling Bayesian priors into artificial neural networks","created":"2023-05-24","cited_by_count":42,"area":null},{"id":"2202.03528","title":"TACTiS: Transformer-Attentional Copulas for Time Series","created":"2022-02-07","cited_by_count":38,"area":null},{"id":"2310.20447","title":"Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks","created":"2023-10-31","cited_by_count":37,"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":"2405.16156","title":"Mixture of In-Context Prompters for Tabular PFNs","created":"2024-05-25","cited_by_count":33,"area":null},{"id":"2305.15930","title":"End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes","created":"2023-05-25","cited_by_count":32,"area":null},{"id":"2506.10914","title":"Foundation Models for Causal Inference via Prior-Data Fitted Networks","created":"2025-06-12","cited_by_count":32,"area":null},{"id":"2605.20119","title":"Toto 2.0: Time Series Forecasting Enters the Scaling Era","created":"2026-05-19","cited_by_count":30,"area":null},{"id":"2502.16294","title":"TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data","created":"2025-02-22","cited_by_count":29,"area":null},{"id":"2406.05216","title":"TabPFGen -- Tabular Data Generation with TabPFN","created":"2024-06-07","cited_by_count":28,"area":null},{"id":"2312.12275","title":"Emergence of In-Context Reinforcement Learning from Noise Distillation","created":"2023-12-19","cited_by_count":27,"area":null},{"id":"2404.16795","title":"In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization","created":"2024-04-25","cited_by_count":26,"area":null},{"id":"2411.10634","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","created":"2024-11-15","cited_by_count":25,"area":null},{"id":"2305.02644","title":"Neuralizer: General Neuroimage Analysis without Re-Training","created":"2023-05-04","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":"2404.16367","title":"Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers","created":"2024-04-25","cited_by_count":24,"area":null},{"id":"2407.10005","title":"Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond","created":"2024-07-13","cited_by_count":24,"area":null},{"id":"2405.14982","title":"In-context Time Series Predictor","created":"2024-05-23","cited_by_count":23,"area":null},{"id":"2410.15320","title":"Amortized Probabilistic Conditioning for Optimization, Simulation and Inference","created":"2024-10-20","cited_by_count":23,"area":null},{"id":"2310.19961","title":"ExPT: Synthetic Pretraining for Few-Shot Experimental Design","created":"2023-10-30","cited_by_count":22,"area":null},{"id":"2311.14645","title":"A General Framework for User-Guided Bayesian Optimization","created":"2023-11-24","cited_by_count":22,"area":null},{"id":"2310.10971","title":"Context-Aware Meta-Learning","created":"2023-10-17","cited_by_count":21,"area":null},{"id":"2405.03547","title":"Position: Leverage Foundational Models for Black-Box Optimization","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2411.02064","title":"Amortized Bayesian Experimental Design for Decision-Making","created":"2024-11-04","cited_by_count":21,"area":null},{"id":"2509.21489","title":"GraphPFN: A Prior-Data Fitted Graph Foundation Model","created":"2025-09-25","cited_by_count":21,"area":null},{"id":"2406.07457","title":"Estimating the Hallucination Rate of Generative AI","created":"2024-06-11","cited_by_count":20,"area":null},{"id":"2505.23947","title":"Position: The Future of Bayesian Prediction Is Prior-Fitted","created":"2025-05-29","cited_by_count":20,"area":null},{"id":"2312.08598","title":"MotherNet: Fast Training and Inference via Hyper-Network Transformers","created":"2023-12-14","cited_by_count":19,"area":null},{"id":"2402.06971","title":"In-Context Data Distillation with TabPFN","created":"2024-02-10","cited_by_count":19,"area":null},{"id":"2511.02818","title":"Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning","created":"2025-11-04","cited_by_count":19,"area":null},{"id":"2602.04029","title":"PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models","created":"2026-02-03","cited_by_count":19,"area":"synth"},{"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":"2405.19883","title":"From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems","created":"2024-05-30","cited_by_count":17,"area":null},{"id":"2406.11233","title":"Probing the Decision Boundaries of In-context Learning in Large Language Models","created":"2024-06-17","cited_by_count":17,"area":null},{"id":"2410.09385","title":"Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models","created":"2024-10-12","cited_by_count":17,"area":null},{"id":"2510.25502","title":"TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting","created":"2025-10-29","cited_by_count":17,"area":null},{"id":"2409.16118","title":"TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models","created":"2024-09-24","cited_by_count":16,"area":null},{"id":"2509.07143","title":"Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models","created":"2025-09-08","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":"2302.03067","title":"Memory-Based Meta-Learning on Non-Stationary Distributions","created":"2023-02-06","cited_by_count":15,"area":null},{"id":"2311.08362","title":"Transformers can optimally learn regression mixture models","created":"2023-11-14","cited_by_count":15,"area":null},{"id":"2405.15302","title":"Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism","created":"2024-05-24","cited_by_count":15,"area":"lr"},{"id":"2502.06601","title":"Amortized In-Context Bayesian Posterior Estimation","created":"2025-02-10","cited_by_count":15,"area":null},{"id":"2402.17423","title":"Reinforced In-Context Black-Box Optimization","created":"2024-02-27","cited_by_count":14,"area":null},{"id":"2406.08973","title":"XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning","created":"2024-06-13","cited_by_count":14,"area":"deeprl"},{"id":"2505.20003","title":"TabPFN: One Model to Rule Them All?","created":"2025-05-26","cited_by_count":14,"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":"2406.12409","title":"Translation Equivariant Transformer Neural Processes","created":"2024-06-18","cited_by_count":13,"area":null},{"id":"2410.05629","title":"Vector-ICL: In-context Learning with Continuous Vector Representations","created":"2024-10-08","cited_by_count":13,"area":null},{"id":"2502.02463","title":"Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2503.05985","title":"Black Box Causal Inference: Effect Estimation via Meta Prediction","created":"2025-03-07","cited_by_count":13,"area":null},{"id":"2504.17660","title":"Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models","created":"2025-04-24","cited_by_count":13,"area":null},{"id":"2507.05526","title":"Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning","created":"2025-07-07","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":"2506.03128","title":"Zero-Shot Time Series Forecasting with Covariates via In-Context Learning","created":"2025-06-03","cited_by_count":12,"area":null},{"id":"2506.21387","title":"Early Stopping Tabular In-Context Learning","created":"2025-06-26","cited_by_count":12,"area":null},{"id":"2602.18266","title":"A Probabilistic Framework for LLM-Based Model Discovery","created":"2026-02-20","cited_by_count":12,"area":null},{"id":"2403.10923","title":"Interpretable Machine Learning for TabPFN","created":"2024-03-16","cited_by_count":11,"area":null},{"id":"2306.09819","title":"Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels","created":"2023-06-16","cited_by_count":10,"area":null},{"id":"2406.03707","title":"What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2406.05064","title":"Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning","created":"2024-06-07","cited_by_count":10,"area":"offlinerl"},{"id":"2502.20525","title":"Revisiting Kernel Attention with Correlated Gaussian Process Representation","created":"2025-02-27","cited_by_count":10,"area":null},{"id":"2509.05186","title":"Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations","created":"2025-09-05","cited_by_count":10,"area":null},{"id":"2509.24762","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2601.21725","title":"Procedural Pretraining: Warming Up Language Models with Abstract Data","created":"2026-01-29","cited_by_count":10,"area":null},{"id":"2604.19028","title":"Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors","created":"2026-04-21","cited_by_count":10,"area":null},{"id":"2605.06510","title":"Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models","created":"2026-05-07","cited_by_count":10,"area":null},{"id":"2608.25774","title":"EXAONE Tabular 1.0 : Technical Report","created":"2026-08-26","cited_by_count":10,"area":null},{"id":"2505.22308","title":"Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning","created":"2025-05-28","cited_by_count":9,"area":"lr"},{"id":"2506.07049","title":"FairPFN: A Tabular Foundation Model for Causal Fairness","created":"2025-06-08","cited_by_count":9,"area":null},{"id":"2510.25154","title":"TabMGP: Martingale Posterior with TabPFN","created":"2025-10-29","cited_by_count":9,"area":null},{"id":"2602.03018","title":"From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection","created":"2026-02-03","cited_by_count":9,"area":null},{"id":"2405.10093","title":"LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting","created":"2024-05-16","cited_by_count":8,"area":null},{"id":"2407.05732","title":"FairPFN: Transformers Can do Counterfactual Fairness","created":"2024-07-08","cited_by_count":8,"area":null},{"id":"2407.15351","title":"LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation","created":"2024-07-22","cited_by_count":8,"area":null},{"id":"2410.01565","title":"Bayes' Power for Explaining In-Context Learning Generalizations","created":"2024-10-02","cited_by_count":8,"area":null}],"next_cursor":"100"}