Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation. - Giovanni Luca Marchetti; Marco Moletta; Gustaf Tegnér; Peiyang Shi; Anastasiia Varava; Alexander Kravchenko; Danica Kragic (ICAR, 2021)

Active Nearest Neighbor Regression Through Delaunay Refinement. - Alexander Kravberg; Giovanni Luca Marchetti; Vladislav Polianskii; Anastasiia Varava; Florian T. Pokorny; Danica Kragic (ICML, 2022)

Back to the Manifold: Recovering from Out-of-Distribution States. - Alfredo Reichlin; Giovanni Luca Marchetti; Hang Yin 001; Ali Ghadirzadeh; Danica Kragic (IROS, 2022)

Voronoi density estimator for high-dimensional data: Computation, compactification and convergence. - Vladislav Polianskii; Giovanni Luca Marchetti; Alexander Kravberg; Anastasiia Varava; Florian T. Pokorny; Danica Kragic (UAI, 2022)

An Efficient and Continuous Voronoi Density Estimator. - Giovanni Luca Marchetti; Vladislav Polianskii; Anastasiia Varava; Florian T. Pokorny; Danica Kragic (AISTATS, 2023)

Equivariant Representation Learning via Class-Pose Decomposition. - Giovanni Luca Marchetti; Gustaf Tegnér; Anastasiia Varava; Danica Kragic (AISTATS, 2023)

Learning Geometric Representations of Objects via Interaction. - Alfredo Reichlin; Giovanni Luca Marchetti; Hang Yin 001; Anastasiia Varava; Danica Kragic (ECML/PKDD (4), 2023)

Equivariant Representation Learning in the Presence of Stabilizers. - Luis Armando Pérez Rey; Giovanni Luca Marchetti; Danica Kragic; Dmitri Jarnikov; Mike Holenderski (ECML/PKDD (4), 2023)

Equivariant Representations for Non-Free Group Actions. - Luis Armando Pérez Rey; Giovanni Luca Marchetti; Danica Kragic; Dmitri Jarnikov; Mike Holenderski (CoRR, 2023)

Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks. - Giovanni Luca Marchetti; Christopher J. Hillar; Danica Kragic; Sophia Sanborn (COLT, 2024)

Hyperbolic Delaunay Geometric Alignment. - Aniss Aiman Medbouhi; Giovanni Luca Marchetti; Vladislav Polianskii; Alexander Kravberg; Petra Poklukar; Anastasia Varava; Danica Kragic (ECML/PKDD (3), 2024)

HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees. - Alejandro García-Castellanos; Aniss Aiman Medbouhi; Giovanni Luca Marchetti; Erik J. Bekkers; Danica Kragic (CoRR, 2024)

On Symmetries and Metrics in Geometric Inference. - Giovanni Luca Marchetti (2024)

On the Geometry and Optimization of Polynomial Convolutional Networks. - Vahid Shahverdi; Giovanni Luca Marchetti; Kathlén Kohn (AISTATS, 2025)

Geometry of Lightning Self-Attention: Identifiability and Dimension. - Nathan W. Henry; Giovanni Luca Marchetti; Kathlén Kohn (ICLR, 2025)

Relative Representations: Topological and Geometric Perspectives. - Alejandro García-Castellanos; Giovanni Luca Marchetti; Danica Kragic; Martina Scolamiero (UniReps, 2025)

Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach. - Giovanni Luca Marchetti; Gabriele Cesa; Kumar Pratik; Arash Behboodi (Trans. Mach. Learn. Res., 2025)

An Invitation to Neuroalgebraic Geometry. - Giovanni Luca Marchetti; Vahid Shahverdi; Stefano Mereta; Matthew Trager; Kathlén Kohn (CoRR, 2025)

Learning on a Razor's Edge: the Singularity Bias of Polynomial Neural Networks. - Vahid Shahverdi; Giovanni Luca Marchetti; Kathlén Kohn (CoRR, 2025)

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks. - Daniel Kunin; Giovanni Luca Marchetti; Feng Chen; Dhruva Karkada; James B. Simon; Michael Robert DeWeese; Surya Ganguli; Nina Miolane (CoRR, 2025)