33 citations · 37 across the 9 of their papers we have counts for
11 papers
An Active-Bottleneck Mechanism for Weak-to-Strong Generalization
Mohammad Zeinalpour, Amir Najafi
Weak-to-strong generalization (W2SG) occurs when a student trained on a teacher's predictions outperforms that teacher. We study when this happens under fully converged, ridgeless…
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…
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…
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 $…
Out-Of-Domain Unlabeled Data Improves Generalization
Amir Hossein Saberi, Amir Najafi, Alireza Heidari +3
We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially ro…
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Amir Hossein Saberi, Amir Najafi, Seyed Abolfazl Motahari +1
In this paper, we find a sample complexity bound for learning a simplex from noisy samples. Assume a dataset of size is given which includes i.i.d. samples drawn from a uniform…