6 citations · 15 across the 15 of their papers we have counts for
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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…
Efficient Learning and Symmetry Discovery under Exact Invariances
Ashkan Soleymani, Behrooz Tahmasebi, Patrick Jaillet +1
Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understood. Even for classical superv…
Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting
Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
Coded computing has emerged as a powerful paradigm for mitigating the impact of straggling workers in distributed computing systems. However, existing coded-computing schemes are p…
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…