activity
20172026
most citedDuality and higher Buscher rules in p-form gauge theory and linearized gravity

7 citations · 18 across the 7 of their papers we have counts for

collaborators

16 papers

q-bio.QM2026

Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data

Yue Zhang, Nandini Amit Gadhia, Georgios Karagiannis +1

Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings where nonlinear interactions…

hep-th2022★ 5 cited

Duality, generalized global symmetries and jet space isometries

Athanasios Chatzistavrakidis, Georgios Karagiannis, Arash Ranjbar

We revisit universal features of duality in linear and nonlinear relativistic scalar and Abelian 1-form theories with single or multiple fields, which exhibit ordinary or generaliz…

gr-qc2021

Axion gravitodynamics, Lense-Thirring effect, and gravitational waves

Athanasios Chatzistavrakidis, Georgios Karagiannis, George Manolakos +1

We investigate physical implications of a gravitational analog of axion electrodynamics with a parity-violating gravitoelectromagnetic theta term. This is related to the Nieh-Yan t…

hep-th2021

Tensor Galileons as Lovelock theories

Georgios Karagiannis

We review the construction of Galileon interactions involving a single two-column mixed-symmetry tensor of arbitrary degree in flat spacetime of arbitrary dimensions, in a reverse…

hep-th2020★ 7 cited

Duality and higher Buscher rules in p-form gauge theory and linearized gravity

Athanasios Chatzistavrakidis, Georgios Karagiannis, Arash Ranjbar

We perform an in-depth analysis of the transformation rules under duality for couplings of theories containing multiple scalars, -form gauge fields, linearized gravitons or $(p,…

stat.ML2020

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction

Wei Deng, Qi Feng, Georgios Karagiannis +2

Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction f…