30 citations · 110 across the 11 of their papers we have counts for
5 papers · 1 filter
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Homayun Afrabandpey, Tomi Peltola, Juho Piironen +2
A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and…
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets
Homayun Afrabandpey, Tomi Peltola, Samuel Kaski
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work f…
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections
Tomi Peltola
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, interpre…
Machine Teaching of Active Sequential Learners
Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee +1
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teac…
Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction
Homayun Afrabandpey, Tomi Peltola, Samuel Kaski
Regression under the "small , large " conditions, of small sample size and large number of features in the learning data set, is a recurring setting in which learning…