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stat.ML2026
Depth Separation for Neural Networks under Gaussian Measure
Cynthia Bortolotto, Francisco Ganacim, Roberto I. Oliveira
We revisit the problem of depth separation for neural networks, as studied by Eldan and Shamir, Daniely, and others. Specifically, we consider the natural setting of approxim…
stat.ML2026
Dataset Pruning from First Principles: A Label-Free Linear Programming Approach
Rodrigo Schuller, Francisco Ganacim
Dataset pruning reduces a large training set to a representative subset while preserving model performance. Existing geometry-based methods typically assume that nearby points in e…