no code implementations • 1 Dec 2019 • Mustafa Mert Çelikok, Tomi Peltola, Pedram Daee, Samuel Kaski
Understanding each other is the key to success in collaboration.
1 code implementation • 1 Nov 2019 • Tomi Peltola, Jussi Jokinen, Samuel Kaski
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.
1 code implementation • 21 Oct 2019 • Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski
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.
no code implementations • 26 Feb 2019 • Homayun Afrabandpey, Tomi Peltola, Samuel Kaski
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics.
no code implementations • 5 Oct 2018 • 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, interpretable explanation model.
1 code implementation • NeurIPS 2019 • Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
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.
1 code implementation • 13 Oct 2017 • Pedram Daee, Tomi Peltola, Aki Vehtari, Samuel Kaski
In human-in-the-loop machine learning, the user provides information beyond that in the training data.
no code implementations • 9 May 2017 • Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski, Pekka Marttinen
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
1 code implementation • 10 Dec 2016 • Pedram Daee, Tomi Peltola, Marta Soare, Samuel Kaski
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging.
no code implementations • 8 Dec 2016 • Homayun Afrabandpey, Tomi Peltola, Samuel Kaski
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.
no code implementations • 7 Dec 2016 • Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-Ud-Din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski
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.