Transfer Learning Through Conditional Quantile Matching

2026-02-02

AI Summary

This work proposes a multi-source transfer learning method for regression tasks under severe target-domain data scarcity, without requiring assumptions of covariate or label shift. The approach constructs conditional generative models for each heterogeneous source domain and aligns their distributions to the target domain through conditional quantile matching, thereby enabling high-quality data augmentation. Theoretical analysis establishes the convergence rate of the quantile-matching estimator and derives a tighter excess risk bound for the empirical risk minimizer trained on the augmented data. Experimental results demonstrate that the proposed method significantly outperforms both target-only baselines and existing transfer learning approaches on both synthetic and real-world datasets.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We introduce a transfer learning framework for regression that leverages heterogeneous source domains to improve predictive performance in a data-scarce target domain. Our approach learns a conditional generative model separately for each source domain and calibrates the generated responses to the target domain via conditional quantile matching. This distributional alignment step corrects general discrepancies between source and target domains without imposing restrictive assumptions such as covariate or label shift. The resulting framework provides a principled and flexible approach to high-quality data augmentation for downstream learning tasks in the target domain. From a theoretical perspective, we show that an empirical risk minimizer (ERM) trained on the augmented dataset achieves a tighter excess risk bound than the target-only ERM under mild conditions. In particular, we establish new convergence rates for the quantile matching estimator that governs the transfer bias-variance tradeoff. From a practical perspective, extensive simulations and real data applications demonstrate that the proposed method consistently improves prediction accuracy over target-only learning and competing transfer learning methods.

Problem

• transfer learning
• regression
• data scarcity
• domain adaptation
• heterogeneous sources

Innovation

• transfer learning
• conditional quantile matching
• distributional alignment
• data augmentation
• excess risk bound