Tag: stein
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Efficient Learning of Stationary Diffusions with Stein-type Discrepancies
Efficient Learning of Stationary Diffusions with Stein-type Discrepancies arXiv:2601.16597v1 Announce Type: new Abstract: Learning a stationary diffusion amounts to estimating the parameters of a stochastic differential equation whose stationary distribution matches a target distribution. We build on the recently introduced kernel deviation from stationarity (KDS), which enforces stationarity by evaluating expectations of the diffusion’s generator…
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Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks arXiv:2507.08261v1 Announce Type: new Abstract: Batch normalization (BN) is a ubiquitous operation in deep neural networks used primarily to achieve stability and regularization during network training. BN involves feature map centering and scaling using sample means and variances, respectively. Since these statistics…
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The Polynomial Stein Discrepancy for Assessing Moment Convergence
The Polynomial Stein Discrepancy for Assessing Moment Convergence arXiv:2412.05135v1 Announce Type: new Abstract: We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalable Bayesian sampling algorithms, such…