[2603.21042] Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding

Statistics > Methodology arXiv:2603.21042 (stat) [Submitted on 22 Mar 2026] Title:Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding Authors:Shuoxun Xu, Zhanhao Yan, Lexin Li View a PDF of the paper titled Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding, by Shuoxun Xu and 2 other authors View PDF HTML (experimental) Abstract:Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a focus on improving sample efficiency under limited fMRI-stimulus paired data and substantial subject heterogeneity. We propose a lightweight alignment framework equipped with two statistical learning components: inverse semi-supervised learning that leverages abundant unpaired stimulus embeddings through inverse mapping and residual debiasing, and meta transfer learning that borrows strength from pretrained models across subjects via sparse aggregation and residual correction. Both methods operate exclusively at the alignment stage while keeping encoders and decoders frozen, allowing for efficient computation, modular deployment, and rigorous theoretical analysis. We establish finite-sample generalization bounds and safety guarantees, and demonstrate competitive empirical performance on the large-scale fMRI-...