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arXiv Machine Learning

Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding

arXiv:2508. 11657v2 Announce Type: replace-cross Abstract: Objective: Sparse Bayesian learning provides an effective framework to solve high-dimensional problems in brain signal decoding.

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arXiv Machine Learning
Sep 17

Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance

The paper introduces a sparse Bayesian time‑varying regression framework that models pairwise EEG channel interactions and performs temporal feature selection for P300 brain‑computer interfaces. Using a relaxed‑thresholded Gaussian process prior, the method achieves a median character‑level accuracy of 96.4% on a public P300 speller dataset and outperforms both statistical and deep learning baselines. It also yields subgroup‑specific gains, notably for participants who abstain from alcohol, and improves median BCI‑Utility by over 10%, reaching peak throughput after only six sequence repetitions.

By Guoxuan Ma, Yuan Zhong, Moyan Li, Yuxiao Nie, Jian Kang
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arXiv Machine Learning
Aug 4

Recursive Gaussian Processes and the Bayesian Brain

arXiv:2608. 00503v1 Announce Type: cross Abstract: Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce.

By Moumita Das, Dipanjan Ray, Sourabh Bhattacharya
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arXiv Machine Learning
Jun 4

Sparse Bayesian Deep Functional Learning with Structured Region Selection

arXiv:2602. 20651v3 Announce Type: replace Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presenting both challenges and opportunities for functional data analysis.

By Xiaoxian Zhu, Yingmeng Li, Shuangge Ma, Mengyun Wu
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arXiv AI
Aug 19

Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data

The paper proposes using the $q$Gaussian distribution, derived from Tsallis entropy maximization, to address the shortcomings of Gaussian assumptions in sparse learning with correlated and heterogeneous data. It introduces a new framework that adapts numerical equilibrium methods to composite optimization problems, applying it to the Hager‑Zhang conjugate gradient algorithm to create a stable, efficient sparse learning algorithm. The work offers both theoretical insights into alternative statistical distributions and practical tools for data analysis in fields like biostatistics.

By Kai Yang, Masoud Asgharian, Celia M. T. Greenwood
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