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20132026
most citedRobustness to Adversarial Perturbations in Learning from Incomplete Data

33 citations · 37 across the 9 of their papers we have counts for

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stat.ML2025

Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes

Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl Motahari +1

In this paper, we establish sample complexity bounds for learning high-dimensional simplices in from noisy data. Specifically, we consider i.i.d. samples uniform…

stat.ML2025

Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations

Arefe Boushehrian, Amir Najafi

Learning distribution families over is a fundamental problem in unsupervised learning and statistics. A central question in this setting is whether a given family of…