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Found 14 papers, 4 papers with code
On the hardness of learning under symmetries
We study the problem of learning equivariant neural networks via gradient descent.
Effective Structural Encodings via Local Curvature Profiles
We further show that combining local structural encodings, such as LCP, with global positional encodings improves downstream performance, suggesting that they capture complementary geometric information.
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature
Several rewiring approaches utilizing graph characteristics, such as curvature or the spectrum of the graph Laplacian, have been proposed.
Curvature-based Clustering on Graphs
We consider several discrete curvature notions and analyze the utility of the resulting algorithms.
Continuum Limits of Ollivier's Ricci Curvature on data clouds: pointwise consistency and global lower bounds
Let $\mathcal{M} \subseteq \mathbb{R}^d$ denote a low-dimensional manifold and let $\mathcal{X}= \{ x_1, \dots, x_n \}$ be a collection of points uniformly sampled from $\mathcal{M}$.
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension
We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text.
On a class of geodesically convex optimization problems solved via Euclidean MM methods
We study geodesically convex (g-convex) problems that can be written as a difference of Euclidean convex functions.
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.
Robust Large-Margin Learning in Hyperbolic Space
In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space.
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning
For canonical graphs, the algorithm's prediction provably matches classical results.
Projection-free nonconvex stochastic optimization on Riemannian manifolds
We present algorithms for both purely stochastic optimization and finite-sum problems.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis
When analyzing empirical data, we often find that global linear models overestimate the number of parameters required.
Riemannian Optimization via Frank-Wolfe Methods
Both tasks involve geodesically convex interval constraints, for which we show that the Riemannian "linear" oracle required by RFW admits a closed-form solution; this result may be of independent interest.
