Transfer learning under latent space model

• URL: http://arxiv.org/abs/2509.15797v1
• Date: Fri, 19 Sep 2025 09:25:54 GMT
• Title: Transfer learning under latent space model
• Authors: Kuangnan Fang, Ruixuan Qin, Xinyan Fan,
• Abstract summary: We propose a transfer learning method that leverages information from networks with latent variables similar to those in the target network.<n>In each stage, we derive sufficient identification conditions and design tailored projected gradient algorithms for estimation.<n> Simulation studies and analyses of two real datasets demonstrate the effectiveness of the proposed methods.
• Score: 1.2665468879312296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent space model plays a crucial role in network analysis, and accurate estimation of latent variables is essential for downstream tasks such as link prediction. However, the large number of parameters to be estimated presents a challenge, especially when the latent space dimension is not exceptionally small. In this paper, we propose a transfer learning method that leverages information from networks with latent variables similar to those in the target network, thereby improving the estimation accuracy for the target. Given transferable source networks, we introduce a two-stage transfer learning algorithm that accommodates differences in node numbers between source and target networks. In each stage, we derive sufficient identification conditions and design tailored projected gradient descent algorithms for estimation. Theoretical properties of the resulting estimators are established. When the transferable networks are unknown, a detection algorithm is introduced to identify suitable source networks. Simulation studies and analyses of two real datasets demonstrate the effectiveness of the proposed methods.

Related papers

• Contrastive Network Representation Learning [20.236657764682715]
  Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties.<n>We propose a novel contrastive learning-based statistical approach for network edge embedding.<n>We show that our method achieves the minimax optimal convergence rate for edge representation learning.
  arXiv  Detail & Related papers  (2025-09-14T15:25:59Z)
• Fusing CFD and measurement data using transfer learning [49.1574468325115]
  We introduce a non-linear method based on neural networks combining simulation and measurement data via transfer learning.<n>In a first step, the neural network is trained on simulation data to learn spatial features of the distributed quantities.<n>The second step involves transfer learning on the measurement data to correct for systematic errors between simulation and measurement by only re-training a small subset of the entire neural network model.
  arXiv  Detail & Related papers  (2025-07-28T07:21:46Z)
• Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing [0.0]
  Concept drift detection is a field dedicated to identifying statistical shifts that is underutilized in monitoring neural networks.<n>We introduce an application of the $chi2$ Goodness of Fit Hypothesis Test as a drift detection meta-algorithm applied to a multilayer perceptron, a convolutional neural network, and a transformer trained for machine vision.
  arXiv  Detail & Related papers  (2025-05-07T11:04:47Z)
• Transfer Learning Under High-Dimensional Network Convolutional Regression Model [20.18595334666282]
  We propose a high-dimensional transfer learning framework based on network convolutional regression ( NCR)<n>Our methodology includes a two-step transfer learning algorithm that addresses domain shift between source and target networks.<n> Empirical evaluations, including simulations and a real-world application using Sina Weibo data, demonstrate substantial improvements in prediction accuracy.
  arXiv  Detail & Related papers  (2025-04-28T16:52:28Z)
• Features are fate: a theory of transfer learning in high-dimensional regression [23.840251319669907]
  We show that when the target task is well represented by the feature space of the pre-trained model, transfer learning outperforms training from scratch.<n>For this model, we establish rigorously that when the feature space overlap between the source and target tasks is sufficiently strong, both linear transfer and fine-tuning improve performance.
  arXiv  Detail & Related papers  (2024-10-10T17:58:26Z)
• Network Gradient Descent Algorithm for Decentralized Federated Learning [0.2867517731896504]
  We study a fully decentralized federated learning algorithm, which is a novel descent gradient algorithm executed on a communication-based network. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator's statistical efficiency.
  arXiv  Detail & Related papers  (2022-05-06T02:53:31Z)
• A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer [55.20627066525205]
  We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models. Our proposed VB approach can obtain good improvements on target devices, and consistently outperforms 13 state-of-the-art knowledge transfer algorithms.
  arXiv  Detail & Related papers  (2021-10-16T15:54:01Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Pre-Trained Models for Heterogeneous Information Networks [57.78194356302626]
  We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
  arXiv  Detail & Related papers  (2020-07-07T03:36:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.