Georgios Arvanitidis

According to our database, Georgios Arvanitidis authored at least 25 papers between 2012 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Connecting Neural Models Latent Geometries with Relative Geodesic Representations.

Hanlin Yu, Berfin Inal, Georgios Arvanitidis, Søren Hauberg, Francesco Locatello, Marco Fumero

CoRR, June, 2025

Learning geometry and topology via multi-chart flows.

Hanlin Yu, Søren Hauberg, Marcelo Hartmann, Arto Klami, Georgios Arvanitidis

CoRR, May, 2025

Monge SAM: Robust Reparameterization-Invariant Sharpness-Aware Minimization Based on Loss Geometry.

Albert Kjøller Jacobsen, Georgios Arvanitidis

CoRR, February, 2025

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature.

Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

2024

Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions.

Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Rozo

CoRR, 2024

Counterfactual Explanations via Riemannian Latent Space Traversal.

Paraskevas Pegios, Aasa Feragen, Andreas Abildtrup Hansen, Georgios Arvanitidis

CoRR, 2024

Neural Contractive Dynamical Systems.

Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, Leonel Rozo

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Reactive motion generation on learned Riemannian manifolds.

Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Rozo

Int. J. Robotics Res., September, 2023

On the curvature of the loss landscape.

Alison Pouplin, Hrittik Roy, Sidak Pal Singh, Georgios Arvanitidis

CoRR, 2023

Riemannian Laplace approximations for Bayesian neural networks.

Federico Bergamin, Pablo Moreno-Muñoz, Søren Hauberg, Georgios Arvanitidis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Data Manifolds Entailed by Structural Causal Models.

Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2023

2022

A prior-based approximate latent Riemannian metric.

Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Pulling back information geometry.

Georgios Arvanitidis, Miguel González Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

On the Impact of Stable Ranks in Deep Nets.

Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee, Georgios Arvanitidis

CoRR, 2021

A prior-based approximate latent Riemannian metric.

Georgios Arvanitidis, Bogdan Georgiev, Bernhard Schölkopf

CoRR, 2021

Learning Riemannian Manifolds for Geodesic Motion Skills.

Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Dario Rozo

Proceedings of the Robotics: Science and Systems XVII, Virtual Event, July 12-16, 2021., 2021

Bayesian Quadrature on Riemannian Data Manifolds.

Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis

Proceedings of the 38th International Conference on Machine Learning, 2021

Geometrically Enriched Latent Spaces.

Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Variational Autoencoders with Riemannian Brownian Motion Priors.

Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Fast and Robust Shortest Paths on Manifolds Learned from Data.

Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Geodesic Clustering in Deep Generative Models.

Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, Søren Hauberg

CoRR, 2018

Latent Space Oddity: on the Curvature of Deep Generative Models.

Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data.

Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg

Proceedings of the Geometric Science of Information - Third International Conference, 2017

2016

A Locally Adaptive Normal Distribution.

Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2012

Exploiting graph embedding in support vector machines.

Georgios Arvanitidis, Anastasios Tefas

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012