30 citations · 110 across the 11 of their papers we have counts for
11 papers
Interactive AI with a Theory of Mind
Mustafa Mert Çelikok, Tomi Peltola, Pedram Daee +1
Understanding each other is the key to success in collaboration. For humans, attributing mental states to others, the theory of mind, provides the crucial advantage. We argue for f…
Probabilistic Formulation of the Take The Best Heuristic
Tomi Peltola, Jussi Jokinen, Samuel Kaski
The framework of cognitively bounded rationality treats problem solving as fundamentally rational, but emphasises that it is constrained by cognitive architecture and the task envi…
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…
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
Pedram Daee, Tomi Peltola, Aki Vehtari +1
In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facili…
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder +6
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which…
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Luana Micallef, Iiris Sundin, Pekka Marttinen +5
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can impr…
Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction
Pedram Daee, Tomi Peltola, Marta Soare +1
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as…
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…