{"ID":5935893,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03005","arxiv_id":"2607.03005","title":"Transfer Learning in High-dimensional Ising Models","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.","short_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 pr...","url_abs":"https://arxiv.org/abs/2607.03005","url_pdf":"https://arxiv.org/pdf/2607.03005v1","authors":"[\"Joonho Kim\",\"Seyoung Park\"]","published":"2026-07-03T06:39:54Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}