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Found 7 papers, 3 papers with code
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
In practical settings, classification datasets are obtained through a labelling process that is usually done by humans.
Unsupervised domain adaptation with non-stochastic missing data
Moreover, we compare the target error of our Adaptation-imputation framework and the "ideal" target error of a UDA classifier without missing target components.
Unsupervised domain adaptation with imputation
Motivated by practical applications, we consider unsupervised domain adaptation for classification problems, in the presence of missing data in the target domain.
Deep Character-Level Click-Through Rate Prediction for Sponsored Search
Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.
Dynamic Matrix Factorization with Priors on Unknown Values
In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.
A bag-of-paths framework for network data analysis
This probability captures a notion of relatedness between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths.
