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arxivstat.MLcs.LGmath.ST2026-07-21

The Tractability Landscape of Sampling with Inexact Scores

Anming Gu, Kevin Tian, Hubert Yang, Yusong Zhu

We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved target family. Our main result shows that any weaker error than the sub-Gaussian assumption used by [YW26] rules out the tractability of unbiased sampling. This strengthens the conclusion of [CCSW26] to be algorithm-agnostic, and to hold for a wider range of error assumptions.

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We study the expected improvement (EI) policy for minimizing a deterministic objective function $f$ on a nonempty compact set $\mathcal X \subset\mathbb R^d$. We assume that $f$ belongs to the RKHS $\mathcal H_k$ of a continuous positive-semidefinite kernel $k$ on $\mathcal X$. F…

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Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent

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arxivmath.STcs.LGstat.MEstat.ML2026-07-31

Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering

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arxivcs.LGcs.AImath.OCstat.ML2026-07-23

A Defense of the Quadratic Model

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arxivcs.LGstat.ML2026-07-24

An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection

Romain Hermary, Nesryne Mejri, Djamila Aouada

Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score, and MCC are widely used, their values convey different meanings depending on the anomaly ratio. In…

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