ArticlePatterns (New York, N.Y.)2022
Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Probabilistic single-particle cryo-EM ab initio 3D reconstruction in SIMPLE.Acta crystallographica. Section D, Structural biology · 2025Article
- Quantifying extreme failure scenarios in transportation systems with graph learning.Patterns (New York, N.Y.) · 2025Article
- Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.Data in brief · 2022Article
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Authors and funding
5 authors.
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Abstract
Classification has been a major task for building intelligent systems because it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions-either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which impedes the design and evaluation of accurate classifiers. Atlhough transfer learning can improve the learning in target domains by incorporating data from relevant source domains, it has received little attention for performance assessment, notably in error estimation. Here, we investigate knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a class of Bayesian minimum mean-square error estimators for optimal Bayesian transfer learning, which enables rigorous evaluation of classification error under uncertainty in small-sample settings. Using Monte Carlo importance sampling, we illustrate the outstanding performance of the proposed estimator for a broad family of classifiers that span diverse learning capabilities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.