AdaTrans: Adaptive Transfer Learning for High-Dimensional Regression

Conceptos Básicos

Adaptive transfer learning methods AdaTrans enable feature- and sample-specific transfer in high-dimensional regression, outperforming existing methods.

Resumen

The content introduces AdaTrans, a method for adaptive transfer learning in high-dimensional regression. It discusses feature-wise and sample-wise adaptive transfer, optimal weight selection with known and unknown informative levels, theoretical analysis, empirical experiments, and real-world applications. The method outperforms existing techniques by adapting to the specific characteristics of the data.

1. Introduction to Transfer Learning

   • Definition of transfer learning.
   • Importance in high-dimensional data analysis.

2. Feature-wise Adaptive Transfer Learning

   • Proposal of F-AdaTrans for detecting non-transferable features.
   • Convergence rates and effectiveness demonstrated through simulations.

3. Sample-wise Adaptive Transfer Learning

   • Introduction of S-AdaTrans for handling dense non-transferable signals.
   • Estimation error bounds and performance under different scenarios.

4. Data-driven Weight Selection

   • Methods for determining weights when informative levels are unknown.

5. Empirical Experiments

   • Evaluation of AdaTrans on synthetic data settings.
   • Comparison with existing methods like TransGLM and Lasso.

6. Conclusion and Future Directions

Estadísticas

The convergence rate of the proposed estimator is shown to be of the order κF p s log s/N, where N = nT + KnS is the full sample size.

Citas

"We achieve this by employing a novel fused-penalty."
"The non-asymptotic rates are established, which recover existing near-minimax optimal rates in special cases."

Ideas clave extraídas de

AdaTrans

by Zelin He,Yin... a las arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13565.pdf

Consultas más profundas

How does AdaTrans handle scenarios with unknown informative levels

AdaTrans handles scenarios with unknown informative levels by providing a data-driven approach to determine the weights. In cases where the transferable structure or informative level is not known, AdaTrans uses optimization techniques to compute the weights based on initial estimators and tuning parameters. By solving an optimization problem, AdaTrans can adaptively adjust the weights assigned to different features or samples, allowing for flexibility in handling unknown levels of informativeness.

What are the implications of the weight choice on the overall performance

The weight choice in AdaTrans has significant implications on the overall performance of the method. Optimal weight selection plays a crucial role in identifying transferable information from source tasks and incorporating it into the target task effectively. The choice of weights directly impacts how well AdaTrans can filter out non-transferable signals while preserving valuable information for estimation. By assigning appropriate weights based on known or estimated informative levels, AdaTrans can achieve better accuracy and robustness in high-dimensional regression tasks.

How can AdaTrans be extended to other domains beyond high-dimensional regression

AdaTrans can be extended to other domains beyond high-dimensional regression by adapting its framework to suit different types of machine learning problems. For instance:

Classification: AdaTrans could be modified to handle classification tasks by adjusting the loss functions and penalty terms accordingly.
Time Series Analysis: In time series forecasting, AdaTrans could incorporate temporal dependencies and sequential patterns into its adaptive transfer learning process.
Image Recognition: For image recognition tasks, AdaTrans could be tailored to extract relevant features from images across different datasets using adaptive weighting strategies.
By customizing the methodology and algorithms within AdaTrans, it can be applied effectively across various domains requiring transfer learning capabilities.