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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 1Source ↗PDF ↗Scholar ↗
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 9Source ↗PDF ↗Scholar ↗