AI RESEARCH
Joint Model and Data Sparsification via the Marginal Likelihood
arXiv CS.LG
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ArXi:2605.29908v1 Announce Type: cross Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic relevance determination (ARD), offers a practical Bayesian mechanism for feature sparsity via marginal likelihood optimization. Yet, its reliance on a homoscedastic noise model renders it sensitive to data contaminations such as outliers or misspecified noise, harming model fit and predictions.