# Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control

arXiv: https://arxiv.org/abs/2010.11037 · HTML: https://mlsanity.com/paper/2010.11037/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 30 of 38)

- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Transfer Learning for Contextual Multi-armed Bandits](https://mlsanity.com/paper/2211.12612/) (2022)
- [Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure](https://mlsanity.com/paper/2401.12272/) (2024)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Transfer Learning Beyond Bounded Density Ratios](https://mlsanity.com/paper/2403.11963/) (2024)
- [Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis](https://mlsanity.com/paper/2407.11034/) (2024)
- [Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift](https://mlsanity.com/paper/2306.16406/) (2023)
- [Guided Adversarial Robust Transfer Learning with Source Mixing](https://mlsanity.com/paper/2309.06534/) (2023)
- [Transfer Q-learning](https://mlsanity.com/paper/2202.04709/) (2022)
- [Transfer Learning for Kernel-based Regression](https://mlsanity.com/paper/2310.13966/) (2023)
- [Bayesian Transfer Learning](https://mlsanity.com/paper/2312.13484/) (2023)
- [AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression](https://mlsanity.com/paper/2403.13565/) (2024)
- [Transfer learning for tensor Gaussian graphical models](https://mlsanity.com/paper/2211.09391/) (2022)
- [Multi-dimensional domain generalization with low-rank structures](https://mlsanity.com/paper/2309.09555/) (2023)
- [Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior](https://mlsanity.com/paper/2404.03764/) (2024)
- [Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation](https://mlsanity.com/paper/2411.15624/) (2024)
- [Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer](https://mlsanity.com/paper/2501.10870/) (2025)
- [Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters](https://mlsanity.com/paper/2507.07941/) (2025)
- [Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction](https://mlsanity.com/paper/2405.15600/) (2024)
- [Multi-Source Transfer Learning of Sparse Single-Index Models](https://mlsanity.com/paper/2606.29658/) (2026)
- [Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace](https://mlsanity.com/paper/2607.08123/) (2026)
- [Structural transfer learning of non-Gaussian DAG](https://mlsanity.com/paper/2310.10239/) (2023)
- [An adaptive transfer learning perspective on classification in non-stationary environments](https://mlsanity.com/paper/2405.18091/) (2024)
- [Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis](https://mlsanity.com/paper/2409.17704/) (2024)
- [Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2504.04812/) (2025)
- [Transfer Learning for High-dimensional Reduced Rank Time Series Models](https://mlsanity.com/paper/2504.15691/) (2025)
- [Transfer Learning for Matrix Completion](https://mlsanity.com/paper/2507.02248/) (2025)
- [Adaptive Multi-task Learning for Multi-sector Portfolio Optimization](https://mlsanity.com/paper/2507.16433/) (2025)

## Abstract (arXiv)

Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and each auxiliary graph is characterized by the sparsity of a divergence matrix. An estimation algorithm, Trans-CLIME, is proposed and shown to attain a faster convergence rate than the minimax rate in the single study setting. Furthermore, a debiased Trans-CLIME estimator is introduced and shown to be element-wise asymptotically normal. It is used to construct a multiple testing procedure for edge detection with false discovery rate control. The proposed estimation and multiple testing procedures demonstrate superior numerical performance in simulations and are applied to infer the gene networks in a target brain tissue by leveraging the gene expressions from multiple other brain tissues. A significant decrease in prediction errors and a significant increase in power for link detection are observed.