High-dimensional Parameter Transfer With Fused-Regularizer

2025-12-31 · Source: JMLR · Field: Science & Research — Mathematics & Computational Sciences, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A novel one-step estimator, featuring a fused-regularizer and a target-data-oriented constraint, addresses the high-dimensional parameter transfer problem for M-estimators from heterogeneous sources. This method, detailed in a 2026 publication (27(99):1−54), significantly improves parameter estimation accuracy by robustly capturing knowledge from source data, even amidst various data distribution shifts. The proposed estimator provides a nonasymptotic bound for target parameter estimation error, guaranteeing performance at least as good as estimators trained solely on target data. It also achieves the minimax-optimal rate under less stringent conditions than existing approaches. Furthermore, the technique extends to a distributed setting, requiring only one communication round with source parameter estimators while preserving the centralized version's accuracy. Extensive simulations and real-world data analyses confirm its effectiveness.

Key takeaway

For research scientists developing high-dimensional M-estimators, consider integrating the fused-regularizer one-step estimator to enhance parameter estimation accuracy. This approach allows you to robustly utilize knowledge from diverse data sources, even with significant distribution shifts, ensuring superior performance compared to target-only models. You can also deploy this efficiently in distributed environments with minimal communication overhead.

Key insights

The estimator robustly transfers high-dimensional parameters from heterogeneous sources, achieving optimal accuracy despite distribution shifts.

Principles

• Parameter transfer improves estimation accuracy.
• Robustness to data distribution shifts is key.
• Distributed settings can maintain accuracy.

Method

Proposes a one-step estimator with a fused-regularizer and a target-data-oriented constraint to capture parameter knowledge from heterogeneous sources. Extends to a distributed setting with one communication round.

In practice

• Apply to high-dimensional M-estimators.
• Use for heterogeneous data sources.
• Implement in distributed computing environments.

Topics

• Parameter Transfer
• High-dimensional M-estimators
• Fused Regularization
• Distribution Shift
• Distributed Learning
• Nonasymptotic Bounds

Code references

• ZLHe0/TransMission

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by JMLR.