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Abolfazl Motahari

3 accepted papers

2024

Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization

NeurIPS 2024poster

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 $T\ge2$ data distributions $P_1,\ldots,P_T$ undergoing a gradual shift, where each pair of consecutive measures $P_i,P_{i…

Cited by 0SourcePDFScholar
2024

Out-Of-Domain Unlabeled Data Improves Generalization

ICLR 2024spotlight

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly…

Cited by 1SourcePDFScholar
2023

Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes

ICML 2023poster

In this paper, we propose sample complexity bounds for learning a simplex from noisy samples. A dataset of size $n$ is given which includes i.i.d. samples drawn from a uniform distribution over an unknown arbitrary simplex in $\mathbb{R}^K$, where samples are assumed to be corrupted by a multi-varia…

Cited by 2SourcePDFScholar