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Found 2 papers, 2 papers with code
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study
In this paper, we revisit the robustness of the minimum error entropy (MEE) criterion, a widely used objective in statistical signal processing to deal with non-Gaussian noises, and investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common.
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail.
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