6 citations · 15 across the 15 of their papers we have counts for
14 papers · 1 filter
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…
Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
Behrooz Tahmasebi, Melanie Weber
Enforcing exact symmetry in machine learning models often yields significant gains in scientific applications, serving as a powerful inductive bias. However, recent work suggests t…
Geometric Algorithms for Neural Combinatorial Optimization with Constraints
Nikolaos Karalias, Akbar Rafiey, Yifei Xu +4
Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a cen…
Learning with Exact Invariances in Polynomial Time
Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka +1
We study the statistical-computational trade-offs for learning with exact invariances (or symmetries) using kernel regression. Traditional methods, such as data augmentation, group…
A Universal Class of Sharpness-Aware Minimization Algorithms
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri +2
Recently, there has been a surge in interest in developing optimization algorithms for overparameterized models as achieving generalization is believed to require algorithms with s…
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromise…
Sample Complexity Bounds for Estimating Probability Divergences under Invariances
Behrooz Tahmasebi, Stefanie Jegelka
Group-invariant probability distributions appear in many data-generative models in machine learning, such as graphs, point clouds, and images. In practice, one often needs to estim…
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Behrooz Tahmasebi, Stefanie Jegelka
In practice, encoding invariances into models improves sample complexity. In this work, we study this phenomenon from a theoretical perspective. In particular, we provide minimax o…
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka
While message passing Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in the…