Tag: log
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Learning Multinomial Logits in $O(n log n)$ time
Learning Multinomial Logits in $O(n log n)$ time arXiv:2601.04423v1 Announce Type: cross Abstract: A Multinomial Logit (MNL) model is composed of a finite universe of items $[n]={1,…, n}$, each assigned a positive weight. A query specifies an admissible subset — called a slate — and the model chooses one item from that slate with probability…
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Statistical-computational gap in multiple Gaussian graph alignment
Statistical-computational gap in multiple Gaussian graph alignment arXiv:2512.00610v1 Announce Type: new Abstract: We investigate the existence of a statistical-computational gap in multiple Gaussian graph alignment. We first generalize a previously established informational threshold from Vassaux and Massouli’e (2025) to regimes where the number of observed graphs $p$ may also grow with the number of nodes…
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Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference arXiv:2505.21721v1 Announce Type: new Abstract: We prove that, given a mean-field location-scale variational family, black-box variational inference (BBVI) with the reparametrization gradient converges at an almost dimension-independent rate. Specifically, for strongly log-concave and log-smooth targets, the number of iterations for BBVI with a sub-Gaussian family to achieve…
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Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis
Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I…
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Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance arXiv:2501.02298v1 Announce Type: new Abstract: Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the $mathcal{W}_2$-distance rely on stringent assumptions about the data…