arXiv Machine Learning
Jul 1
Probing Memorization of Tabular In-Context Learning
arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.
Understanding Context Sampling in TabPFN on Small Tabular Datasets
arXiv:2607. 26628v1 Announce Type: new Abstract: TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates.
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arXiv Machine Learning
1d ago
Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN
arXiv:2608. 16429v1 Announce Type: new Abstract: Foundational models for tabular data have made significant progress in recent years, with TabICLv2 reporting state-of-the-art performance on several tabular classification tasks.
arXiv Machine Learning
5d ago
Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
arXiv:2608. 12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context.
arXiv AI
Jun 11
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
arXiv:2606. 11473v1 Announce Type: cross Abstract: Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training set is supplied as context, and predictions for test queries are produced in a single forward pass.
arXiv AI
Jun 9
Repetition Mismatch: Why Data Mixture Experiments Don't Scale and How to Fix Them
arXiv:2606. 07597v1 Announce Type: cross Abstract: Pre-training data mixtures are commonly tuned by running small-scale experiments and extrapolating to the target training budget.
arXiv AI
Jul 13
Self-Guided Test-Time Training for Long-Context LLMs
arXiv:2607. 09415v1 Announce Type: cross Abstract: Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs.
arXiv Machine Learning
Jun 9
LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
arXiv:2606. 08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.
arXiv Machine Learning
Aug 4
Why Large Language Models Fail at Tabular Prediction
arXiv:2608. 02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data.
arXiv AI
1d ago
DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations
arXiv:2405. 07780v3 Announce Type: replace-cross Abstract: This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced.
arXiv Machine Learning
23h ago
Understanding the Surprising Generalization Properties of Tabular Foundation Models
arXiv:2608. 17957v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs without updating its weights.
arXiv AI
Jun 12
Meta-Learning Transformers to Improve In-Context Generalization
arXiv:2507. 05019v2 Announce Type: replace-cross Abstract: In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates.
arXiv AI
Jun 16
Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
arXiv:2510. 16882v4 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) is a commonly used technique to adapt large language models (LLMs) to downstream tasks.