1 citations · 1 across the 4 of their papers we have counts for
6 papers
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation
Behrooz Tahmasebi, Melanie Weber
Modern machine learning models increasingly operate on variable-size structured data, such as sets and graphs. A central requirement is \emph{size generalization}: the ability of a…
Sparse Data Augmentation for Optimization with Provable Guarantees
Behrooz Tahmasebi, Melanie Weber
In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformation…
Adaptive Symmetry Discovery for Dynamical System Identification
Behrooz Tahmasebi, Melanie Weber
Dynamical systems model trajectory data generated by fixed underlying dynamics, with applications ranging from biology to physics. Especially in scientific settings, dynamical syst…
Data Augmentation: A Fourier Analysis Perspective
Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka
Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the input space, one augments the training…
Structured Regularization for Constrained Optimization on the SPD Manifold
Andrew Cheng, Melanie Weber
Matrix-valued optimization tasks, including those involving symmetric positive definite (SPD) matrices, arise in a wide range of applications in machine learning, data science and…
Disciplined Geodesically Convex Programming
Andrew Cheng, Vaibhav Dixit, Melanie Weber
Convex programming plays a fundamental role in machine learning, data science, and engineering. Testing convexity structure in nonlinear programs relies on verifying the convexity…