arXiv Machine Learning July 10, 2026 By Gaurav Arya, Mathieu Huot, Moritz Schauer, Alexander K. Lew, Feras A. Saad

GradInf: Gradient Estimation as Probabilistic Inference

Read the original on arXiv Machine Learning →

arXiv:2607. 07840v1 Announce Type: cross Abstract: Gradient estimation -- the task of computing the gradient of the expected value of a probabilistic program -- has diverse applications in scientific computing, but is notoriously difficult because of issues such as high-dimensional integration, discrete random choices, and complex stochastic dependencies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

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 AI

Jun 10

Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models

arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.

By Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake, Thomas L. Griffiths

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.

By Yuda Shao, Zhiling Gu, Shan Yu

computer-vision

arXiv Machine Learning

Jul 28

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

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.

By Kart-Leong Lim, Xudong Jiang

arXiv Machine Learning

Aug 26

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

arXiv:2608.24195v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through a...

By Soham Chatterjee, Rwitobroto Dey, Smarajit Bose

benchmarks safety

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

benchmarks

One email a morning, machine-written

Related stories

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

Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models

Structured Nonparametric Variational Inference for Dependent Latent Modeling

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

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

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