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Found 9 papers, 1 papers with code
An Efficient Shapley Value Computation for the Naive Bayes Classifier
In the case of the naive Bayes classifier, and to our knowledge, there is no ``analytical" formulation of Shapley values.
Co-clustering based exploratory analysis of mixed-type data tables
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks.
Model Based Co-clustering of Mixed Numerical and Binary Data
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix.
Open challenges for Machine Learning based Early Decision-Making research
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
Un modèle Bayésien de co-clustering de données mixtes
The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function.
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms.
Cats & Co: Categorical Time Series Coclustering
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models.
Random Forest for the Contextual Bandit Problem - extended version
The dependence of the sample complexity upon the number of contextual variables is logarithmic.
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models
Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination, date and time of calls.
