kernel Stein Discrepancy
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New research clarifies unbounded kernel properties in machine learning
A new paper published on arXiv explores the properties of unbounded kernels in machine learning and statistics. The research addresses a significant gap in understanding the relationships between different expressivity …
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New HyperMC framework optimizes SGMCMC hyperparameters
Researchers have developed HyperMC, a novel framework for optimizing hyperparameters in Stochastic Gradient Markov Chain Monte Carlo (SGMCMC) methods. This approach utilizes a multi-fidelity tuning strategy, combining H…
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New method improves Kernel Stein Discrepancy estimation
Researchers have developed a new method for estimating Kernel Stein Discrepancy (KSD), a technique used for comparing sample distributions and assessing approximate inference. The study identifies the Hilbert-Schmidt no…
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Nyström Kernel Stein Discrepancy Tests Accelerate Goodness-of-Fit Testing
Researchers have developed Nyström Kernel Stein Discrepancy (KSD) tests that significantly improve the efficiency of goodness-of-fit testing. Traditional KSD estimators have quadratic runtime and computationally intract…
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New Polynomial Stein Discrepancy method improves Bayesian inference sample quality assessment
Researchers have introduced the Polynomial Stein Discrepancy (PSD), a new method to evaluate the quality of samples generated by Bayesian inference algorithms. This approach aims to overcome the scalability and dimensio…