Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets. - Melanie Weber 001; Jürgen Jost; Emil Saucan (Axioms, 2016)

Can one see the shape of a network? - Melanie Weber 001; Emil Saucan; Jürgen Jost (CoRR, 2016)

EGAD: ultra-fast functional analysis of gene networks. - Sara Ballouz; Melanie Weber 001; Paul Pavlidis; Jesse A. Gillis (Bioinform., 2017)

Characterizing complex networks with Forman-Ricci curvature and associated geometric flows. - Melanie Weber 001; Emil Saucan; Jürgen Jost (J. Complex Networks, 2017)

Curvature-based Methods for Brain Network Analysis. - Melanie Weber 001; Johannes Stelzer; Emil Saucan; Alexander Naitsat; Gabriele Lohmann; Jürgen Jost (CoRR, 2017)

Frank-Wolfe methods for geodesically convex optimization with application to the matrix geometric mean. - Melanie Weber 001; Suvrit Sra (CoRR, 2017)

Forman's Ricci Curvature - From Networks to Hypernetworks. - Emil Saucan; Melanie Weber 001 (COMPLEX NETWORKS (1), 2018)

Coarse geometry of evolving networks. - Melanie Weber 001; Emil Saucan; Jürgen Jost (J. Complex Networks, 2018)

Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis. - F. Patricia Medina; Linda Ness; Melanie Weber 001; Karamatou Yacoubou Djima (CoRR, 2018)

The Oracle of DLphi. - Dominik Alfke; Weston Baines; Jan Blechschmidt; Mauricio J. del Razo Sarmina; Amnon Drory; Dennis Elbrächter; Nando Farchmin; Matteo Gambara; Silke Glas; Philipp Grohs; Peter Hinz; Danijel Kivaranovic; et al. (CoRR, 2019)

Nonconvex stochastic optimization on manifolds via Riemannian Frank-Wolfe methods. - Melanie Weber 001; Suvrit Sra (CoRR, 2019)

Neighborhood Growth Determines Geometric Priors for Relational Representation Learning. - Melanie Weber 001 (AISTATS, 2020)

Robust large-margin learning in hyperbolic space. - Melanie Weber 001; Manzil Zaheer; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (NeurIPS, 2020)

Identifying biases in legal data: An algorithmic fairness perspective. - Jackson Sargent; Melanie Weber 001 (CoRR, 2021)

On Geometric Optimization, Learning and Control - Melanie Weber 001 (2021)

Mixed-membership community detection via line graph curvature. - Yu Tian; Zachary Lubberts; Melanie Weber 001 (NeurReps, 2022)

On a class of geodesically convex optimization problems solved via Euclidean MM methods. - Suvrit Sra; Melanie Weber 001 (CoRR, 2022)

Computing Brascamp-Lieb Constants through the lens of Thompson Geometry. - Melanie Weber 001; Suvrit Sra (CoRR, 2022)

LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension. - Wenyue Hua; Yuchen Zhang; Zhe Chen; Josie Li; Melanie Weber 001 (CoRR, 2022)

Global optimality for Euclidean CCCP under Riemannian convexity. - Melanie Weber 001; Suvrit Sra (ICML, 2023)

Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature. - Lukas Fesser; Melanie Weber 001 (LoG, 2023)

Augmentations of Forman's Ricci Curvature and their Applications in Community Detection. - Lukas Fesser; Sergio Serrano de Haro Iváñez; Karel Devriendt; Melanie Weber 001; Renaud Lambiotte (CoRR, 2023)

Continuum Limits of Ollivier's Ricci Curvature on data clouds: pointwise consistency and global lower bounds. - Nicolás García Trillos; Melanie Weber 001 (CoRR, 2023)

Curvature-based Clustering on Graphs. - Yu Tian; Zachary Lubberts; Melanie Weber 001 (CoRR, 2023)

Riemannian Optimization via Frank-Wolfe Methods. - Melanie Weber 001; Suvrit Sra (Math. Program., 2023)

Exploiting Data Geometry in Machine Learning. - Melanie Weber 001 (AAAI, 2024)

Structured Regularization for SPD Optimization with Side Information. - Andrew Cheng; Melanie Weber 001 (Allerton, 2024)

Effective Structural Encodings via Local Curvature Profiles. - Lukas Fesser; Melanie Weber 001 (ICLR, 2024)

On the hardness of learning under symmetries. - Bobak T. Kiani; Thien Le; Hannah Lawrence; Stefanie Jegelka; Melanie Weber 001 (ICLR, 2024)

Unitary Convolutions for Learning on Graphs and Groups. - Bobak T. Kiani; Lukas Fesser; Melanie Weber 001 (NeurIPS, 2024)

Hardness of Learning Neural Networks under the Manifold Hypothesis. - Bobak T. Kiani; Jason Wang; Melanie Weber 001 (NeurIPS, 2024)

Disciplined Geodesically Convex Programming. - Andrew Cheng; Vaibhav Dixit; Melanie Weber 001 (CoRR, 2024)

Structured Regularization for Constrained Optimization on the SPD Manifold. - Andrew Cheng; Melanie Weber 001 (CoRR, 2024)

Graph Pooling via Ricci Flow. - Amy Feng; Melanie Weber 001 (Trans. Mach. Learn. Res., 2024)

Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups. - Zakhar Shumaylov; Peter Zaika; James Rowbottom; Ferdia Sherry; Melanie Weber 001; Carola-Bibiane Schönlieb (ICLR, 2025)

Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks. - Menglin Yang 001; Yifei Zhang; Jialin Chen; Melanie Weber 001; Rex Ying (WWW (Companion Volume), 2025)

Geometric Machine Learning. - Melanie Weber 001 (AI Mag., 2025)

Enhancing the Utility of Higher-Order Information in Relational Learning. - Raphael Pellegrin; Lukas Fesser; Melanie Weber 001 (CoRR, 2025)

Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing. - Lukas Fesser; Melanie Weber 001 (CoRR, 2025)

Shared Global and Local Geometry of Language Model Embeddings. - Andrew Lee 001; Melanie Weber 001; Fernanda B. Viégas; Martin Wattenberg (CoRR, 2025)

Position: Beyond Euclidean - Foundation Models Should Embrace Non-Euclidean Geometries. - Neil He; Jiahong Liu 001; Buze Zhang; Ngoc Bui; Ali Maatouk; Menglin Yang 001; Irwin King; Melanie Weber 001; Rex Ying (CoRR, 2025)

Automated Manifold Learning for Reduced Order Modeling. - Imran Nasim; Melanie Weber 001 (CoRR, 2025)