Exploiting graph embedding in support vector machines. - Georgios Arvanitidis; Anastasios Tefas (MLSP, 2012)

A Locally Adaptive Normal Distribution. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (NIPS, 2016)

Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (GSI, 2017)

Latent Space Oddity: on the Curvature of Deep Generative Models. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (ICLR (Poster), 2018)

Geodesic Clustering in Deep Generative Models. - Tao Yang 010; Georgios Arvanitidis; Dongmei Fu; Xiaogang Li 002; Søren Hauberg (CoRR, 2018)

Fast and Robust Shortest Paths on Manifolds Learned from Data. - Georgios Arvanitidis; Søren Hauberg; Philipp Hennig; Michael Schober (AISTATS, 2019)

Variational Autoencoders with Riemannian Brownian Motion Priors. - Dimitrios Kalatzis; David Eklund; Georgios Arvanitidis; Søren Hauberg (ICML, 2020)

Geometrically Enriched Latent Spaces. - Georgios Arvanitidis; Søren Hauberg; Bernhard Schölkopf (AISTATS, 2021)

Bayesian Quadrature on Riemannian Data Manifolds. - Christian Fröhlich; Alexandra Gessner; Philipp Hennig; Bernhard Schölkopf; Georgios Arvanitidis (ICML, 2021)

Learning Riemannian Manifolds for Geodesic Motion Skills. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Gerhard Neumann; Leonel Dario Rozo (Robotics: Science and Systems, 2021)

A prior-based approximate latent Riemannian metric. - Georgios Arvanitidis; Bogdan Georgiev; Bernhard Schölkopf (CoRR, 2021)

On the Impact of Stable Ranks in Deep Nets. - Bogdan Georgiev; Lukas Franken; Mayukh Mukherjee; Georgios Arvanitidis (CoRR, 2021)

Pulling back information geometry. - Georgios Arvanitidis; Miguel González Duque; Alison Pouplin; Dimitrios Kalatzis; Søren Hauberg (AISTATS, 2022)

A prior-based approximate latent Riemannian metric. - Georgios Arvanitidis; Bogdan M. Georgiev; Bernhard Schölkopf (AISTATS, 2022)

On Data Manifolds Entailed by Structural Causal Models. - Ricardo Dominguez-Olmedo; Amir-Hossein Karimi; Georgios Arvanitidis; Bernhard Schölkopf (ICML, 2023)

Riemannian Laplace approximations for Bayesian neural networks. - Federico Bergamin; Pablo Moreno-Muñoz; Søren Hauberg; Georgios Arvanitidis (NeurIPS, 2023)

On the curvature of the loss landscape. - Alison Pouplin; Hrittik Roy; Sidak Pal Singh; Georgios Arvanitidis (CoRR, 2023)

Reactive motion generation on learned Riemannian manifolds. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Gerhard Neumann; Leonel Rozo (Int. J. Robotics Res., 2023)

Neural Contractive Dynamical Systems. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Nadia Figueroa; Gerhard Neumann; Leonel Rozo (ICLR, 2024)

Counterfactual Explanations via Riemannian Latent Space Traversal. - Paraskevas Pegios; Aasa Feragen; Andreas Abildtrup Hansen; Georgios Arvanitidis (CoRR, 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)

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature. - Bernardo Williams; Hanlin Yu; Hoang Phuc Hau Luu; Georgios Arvanitidis; Arto Klami (UAI, 2025)

Monge SAM: Robust Reparameterization-Invariant Sharpness-Aware Minimization Based on Loss Geometry. - Albert Kjøller Jacobsen; Georgios Arvanitidis (CoRR, 2025)

Learning geometry and topology via multi-chart flows. - Hanlin Yu; Søren Hauberg; Marcelo Hartmann; Arto Klami; Georgios Arvanitidis (CoRR, 2025)

Connecting Neural Models Latent Geometries with Relative Geodesic Representations. - Hanlin Yu; Berfin Inal; Georgios Arvanitidis; Søren Hauberg; Francesco Locatello; Marco Fumero (CoRR, 2025)