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Found 11 papers, 5 papers with code
Interactive AI with a Theory of Mind
Understanding each other is the key to success in collaboration.
Probabilistic Formulation of the Take The Best Heuristic
The strengths of the probabilistic formulation, in addition to providing a bounded rational account of the learning of the heuristic, include natural extensibility with additional cognitively plausible constraints and prior information, and the possibility to embed the heuristic as a subpart of a larger probabilistic model.
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.
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model.
Machine Teaching of Active Sequential Learners
We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses.
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
In human-in-the-loop machine learning, the user provides information beyond that in the training data.
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.
Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging.
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.
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
