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Found 4 papers, 0 papers with code
Out-Of-Domain Unlabeled Data Improves Generalization
From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in $\mathbb{R}^d$, where in addition to the $m$ independent and labeled samples from the true distribution, a set of $n$ (usually with $n\gg m$) out of domain and unlabeled samples are given as well.
On sampling from data with duplicate records
Our goal is to develop a procedure that samples uniformly from the set of entities present in the database in the presence of duplicates.
Record fusion: A learning approach
We use this feature vector alongwith the ground-truth information to learn a classifier for each of the attributes of the database.
Approximate Inference in Structured Instances with Noisy Categorical Observations
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.
