Transfer Learning in High-dimensional Ising Models

• URL: http://arxiv.org/abs/2607.03005v1
• Date: Fri, 03 Jul 2026 06:39:54 GMT
• Title: Transfer Learning in High-dimensional Ising Models
• Abstract summary: Trans-Ising is a transfer learning method that combines a loss-based source screening rule with a two-stage estimation procedure.<n>We demonstrate that Trans-Ising achieves lower estimation errors than both target-only estimation and naive data pooling.
• Score: 4.952373271400918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learning method that combines a loss-based source screening rule with a two-stage estimation procedure. The method first identifies informative auxiliary sources using held-out target pseudolikelihood to prevent negative transfer. It then computes an initial estimator via pooled nodewise $\ell_1$-regularized logistic regression, followed by a target-only correction step using a folded-concave penalty. Theoretically, we establish fixed-node $\ell_2$ and $\ell_1$ error bounds, exact graph selection consistency, and the conditional consistency of the screening rule. Through extensive simulations and real-data analyses, we demonstrate that Trans-Ising achieves lower estimation errors than both target-only estimation and naive data pooling.

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