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Found 8 papers, 1 papers with code
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning
Modeling PU data requires certain assumptions on the labeling mechanism that describes which positive observations are assigned a label.
Joint empirical risk minimization for instance-dependent positive-unlabeled data
In our approach we investigate minimizer of an empirical counterpart of a joint risk which depends on both posterior probability of inclusion in a positive class as well as on a propensity score.
Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios
The opposite case when ERM minimizer designed for the case-control case is applied for single-sample data is also considered and similar conclusions are drawn.
Enhancing naive classifier for positive unlabeled data based on logistic regression approach
We argue that for analysis of Positive Unlabeled (PU) data under Selected Completely At Random (SCAR) assumption it is fruitful to view the problem as fitting of misspecified model to the data.
Double logistic regression approach to biased positive-unlabeled data
The significant limitation of almost all existing methods lies in assuming that the propensity score function is constant (SCAR assumption), which is unrealistic in many practical situations.
Improving Lasso for model selection and prediction
These results confirm that, at least for normal linear models, our algorithm seems to be the benchmark for the theory of model selection as it is constructive, computationally efficient and leads to consistent model selection under weak assumptions.
Model Selection
Statistics Theory
Methodology
Statistics Theory
Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function.
Combined l_1 and greedy l_0 penalized least squares for linear model selection
For the traditional setting (n >p) we give Sanov-type bounds on the error probabilities of the ordering--selection algorithm.
