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Found 8 papers, 1 papers with code
Feasible and Desirable Counterfactual Generation by Preserving Human Defined Constraints
Through user studies, we demonstrate that incorporating causal constraints during CF generation results in significantly better explanations in terms of feasibility and desirability for participants.
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior.
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics.
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition
Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction.
Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction
The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters.
Visualizations Relevant to The User By Multi-View Latent Variable Factorization
The factorization is a generative model in which the display is parameterized as a part of the factorization and the other factors explain away the aspects not expressible in a two-dimensional display.
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification.
