arXiv Machine Learning July 28, 2026 By Kart-Leong Lim, Xudong Jiang

Fisher Information based Stochastic Gradient Ascent for Online Learning of Dirichlet Process Mixture and Theory

Read the original on arXiv Machine Learning →

arXiv:2412. 08951v3 Announce Type: replace Abstract: Scalable algorithms of posterior approximation allow Bayesian nonparametrics such as Dirichlet process mixture to scale up to larger dataset at fractional cost.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 28

PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM

arXiv:2607. 24583v1 Announce Type: new Abstract: Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost.

By Kart-Leong Lim

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arXiv Machine Learning

Jun 9

Large-scale empirical tuning and comparison of default optimizers for variational inference

arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.

By Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian

benchmarks

arXiv Machine Learning

Jul 7

Gradient Descent as Implicit EM in Distance-Based Neural Models

arXiv:2512. 24780v2 Announce Type: replace Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking.

By Alan Oursland

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arXiv Machine Learning

Jun 26

Stochastic Gradient Optimization with Model-Assisted Sampling

arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.

By Jonne Pohjankukka, Jukka Heikkonen

benchmarks

arXiv Machine Learning

Jul 17

Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.

By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee

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arXiv Machine Learning

Jun 17

The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient

arXiv:2602. 11557v2 Announce Type: replace Abstract: A variety of widely used optimization methods like SignSGD and Muon can be interpreted as instances of steepest descent under different norm-induced geometries.

By Jichu Li, Xuan Tang, Difan Zou

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