#prior-data-fitted-networks News & Analysis

2 articles tagged with #prior-data-fitted-networks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 117/10

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Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors

Researchers propose a novel uncertainty quantification method for Prior-Data Fitted Networks (PFNs), emerging foundation models for tabular data prediction, using martingale posteriors to provide calibrated confidence estimates. The technique is tuning-free, computationally efficient, and mathematically proven to converge, addressing a significant limitation in PFNs' practical applicability.

AIBullisharXiv – CS AI · Apr 106/10

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In-Context Decision Making for Optimizing Complex AutoML Pipelines

Researchers propose PS-PFN, an advanced AutoML method that extends traditional algorithm selection and hyperparameter optimization to handle modern ML pipelines with fine-tuning and ensembling. Using posterior sampling and prior-data fitted networks for in-context learning, the approach outperforms existing bandit and AutoML strategies on benchmark tasks.