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Sampling Informative Positives Pairs in Contrastive Learning
Contrastive Learning is a paradigm for learning representation functions that recover useful similarity structure in a dataset based on …
M. Weber, P. Bachman
PDF Project
Global optimality for Euclidean CCCP under Riemannian convexity
We study geodesically convex problems that can be written as a difference of Euclidean convex functions. This structure arises in key …
M. Weber, S. Sra
Preprint Project
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension
The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of …
W. Hua, Y. Zhang, Z. Chen, J. Li, M. Weber
PDF
Mixed-Membership Community Detection via Line Graph Curvature
Community detection is a classical method for understanding the structure of relational data. In this paper, we study the problem of …
Y. Tian, Z. Lubberts, M. Weber
PDF Project
Identifying biases in legal data: An algorithmic fairness perspective
The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the …
J. Sargent, M. Weber
Preprint
Robust Large-Margin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning in hyperbolic spaces, driven by their ability to represent …
M. Weber, M. Zaheer, A. Singh Rawat, A. Menon, S. Kumar
Preprint PDF Project Video
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning
The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. …
M. Weber
Preprint PDF Project Video
Forman's Ricci curvature - From networks to hypernetworks
Networks and their higher order generalizations, such as hypernetworks or multiplex networks are ever more popular models in the …
E. Saucan, M. Weber
Preprint PDF Project
Curvature and Representation Learning: Identifying Embedding Spaces for Relational Data
We consider the problem of learning representations of relational data in spaces of constant sectional curvature, i.e., Euclidean, …
M. Weber, M. Nickel
PDF Project
Detecting the Coarse Geometry of Networks
Clustering and sampling are key methods for the study of relational data. Learning efficient representations of such data relies on the …
M. Weber, J. Jost, E. Saucan
Preprint Project