Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

Liang, Jiajun; Zhang, Qian; Deng, Wei; Song, Qifan; Lin, Guang

doi:10.1080/10618600.2024.2380051

Home/Archive/DOE OSTI

DOE OSTI · 2578675

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

Liang, Jiajun·Zhang, Qian·Deng, Wei·Song, Qifan·Lin, Guang

View original source ↗Export metadata ↓

Abstract

Not provided.

Explore related subjects

Mathematics

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Liang, Jiajun, Zhang, Qian, Deng, Wei, Song, Qifan, Lin, Guang. 2024-09-19. Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. https://doi.org/10.1080/10618600.2024.2380051

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2025

Bayesian model selection for neural networks

Explore the source record for details and available documents.

Mathematics↗

DOE OSTI2024

Continuous and Discrete Baroclinic Modes in Continuously Varying Stratification

Not provided.

Mathematics↗

DOE OSTI2024

Global Magni4icence, or: 4G Networks

The global magnificent four theory is the homological version of a maximally supersymmetric $(8+1)$-dimensional gauge theory on a Calabi-Yau fourfold fibered over a circle. In the case of a toric fourfold we conjecture the formula for its twisted Witten index. String-theoretically we count the BPS states of a system of $D0$-$D2$-$D4$-$D6$-$D8$-branes on the Calabi-Yau fourfold in the presence of a large Neveu-Schwarz $B$-field. Mathematically, we develop the equivariant $K$-theoretic DT4 theory, by constructing the four-valent vertex with generic plane partition asymptotics. Physically, the vertex is a supersymmetric localization of a non-commutative gauge theory in $8+1$ dimensions.

Mathematics↗