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Alireza Heidari

2 accepted papers

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
2019

Approximate Inference in Structured Instances with Noisy Categorical Observations

UAI 2019poster

We study the problem of recovering the latent ground truth labeling of a structured instance with categorical random variables in the presence of noisy observations. We present a new approximate algorithm for graphs with categorical variables that achieves low Hamming error in the presence of noisy…

Cited by 9SourcePDFScholar