3 papers
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…
stat.ML2024
Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
Amir Hossein Saberi, Amir Najafi, Ala Emrani +5
The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $…