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An Analysis of Selection Bias Issue for Online Advertising
In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space.
Selective Inference for Sparse High-Order Interaction Models
Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e. g., $> 10^{17}$).
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model.
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
The main obstacle of this problem is in the difficulty of taking into account the selection bias, i. e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases.
Homotopy Continuation Approaches for Robust SV Classification and Regression
An advantage of our homotopy approach is that it can be interpreted as simulated annealing, a common approach for finding a good local optimal solution in non-convex optimization problems.
Safe Feature Pruning for Sparse High-Order Interaction Models
An SFS rule has a property that, if a feature satisfies the rule, then the feature is guaranteed to be non-active in the LASSO solution, meaning that it can be safely screened-out prior to the LASSO training process.
Safe Sample Screening for Support Vector Machines
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result.
