Author

N. Le

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Preprint Jul 2026

Finite-horizon quantile martingale posteriors: raw-urn laws and matrix-gain regression

Martingale posteriors quantify uncertainty by forward-imputing observations from one-step-ahead predictive distributions, but implementations stop after finitely many imputations. For the empirical P\'olya-urn posterior of a quantile the law of the stopped state is derived. The quantile of the stopped urn measure keeps...

N. Le · 0 citations

Jul 2026

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

Geometry-Spectral Rectification (GSR), a theoretically grounded framework that treats long-tailed learning as a spectral regularization problem, is proposed, offering a superior trade-off between computational efficiency and robust generalization in long-tailed settings.

Q. Tran, Ngoc-Hai Nguyen, Quan Dao et al. · arXiv.org · 0 citations