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Mixture of In-Context Prompters for Tabular PFNs
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Recent benchmarks found In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular learning cannot run without severely compromising performance, due to its quadratic space and time complexity w.r.t. dataset size. We propose MIXTUREPFN, which both extends nearest-neighbor sampling to the state-of-the-art ICL for tabular learning model and uses bootstrapping to finetune said model on the inference-time dataset. MIXTUREPFN is the Condorcet winner across 36 diverse tabular datasets against 19 strong deep learning and tree-based baselines, achieving the highest mean rank among Top-10 aforementioned algorithms with statistical significance.
Forward citations
Cited by 3 Pith papers
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Iceberg: Enhancing HLS Modeling with Synthetic Data
Pretraining on LLM-generated HLS programs plus GNN weak labels reduces few-shot latency prediction error by 86% on six real-world applications.
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On Finetuning Tabular Foundation Models
Full finetuning of TabPFNv2 outperforms in-context learning and partial finetuning on medium tabular datasets, and its gains come from sharper query-key attention that better reflects target similarity.
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Position: The Future of Bayesian Prediction Is Prior-Fitted
PFNs, which amortize Bayesian inference by training on datasets sampled from a prior, are likely to supersede MCMC and variational inference for most prediction tasks, the authors argue.
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