Fields- Workshop on Manifold and Graph-Based Learning
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Title: Stability and Generalization of Graph Neural Networks
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Title: Examples of Use of Multi-scale Techniques for Manifold Learning
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Title: Signal sampling by averages on Dirichlet spaces
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Title: Metric representations: Algorithms and Geometry
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Title: The Mathematics of Privacy and Synthetic Data
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Title: A tutorial on Manifold Learning for real data
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Title: Distributed algorithms for graph inverse filtering and wiener filtering
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Title: Learning graph signals and operators from sparse space-time samples
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Title: Statistical-to-Computational Gaps: The Low-Degree method and beyond
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Title: Geometry of Molecular Conformations in Cryo-EM
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Title: Sketch-and-solve approaches to k-means clustering by semidefinite programming
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Title: Multiscale Graph Basis Dictionaries
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Title: From Differentiable Reasoning to Self-supervised Embodied Active Learning
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Title: A Mini-Course On: Wasserstein Embeddings in Geometric Deep Learning (Part D)
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Title: A Mini-Course On: Wasserstein Embeddings in Geometric Deep Learning (Part C)
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Title: Self organizing mappings on the Grassmannian and Flag manifolds with applications to real world data
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Title: Measure Estimation in the Barycentric Coding Model: Geometry, Statistics, and Algorithms
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Title: Complex Structures on EEG data
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Title: Continuum Limits and Graph Cuts for Fermat Graph Laplacians
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Title: Statistical learning theory of deep neural networks for data on a low-dimensional manifold
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