23 citations · 64 across the 6 of their papers we have counts for
6 papers
Human Strategic Steering Improves Performance of Interactive Optimization
Fabio Colella, Pedram Daee, Jussi Jokinen +2
A central concern in an interactive intelligent system is optimization of its actions, to be maximally helpful to its human user. In recommender systems for instance, the action is…
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…
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…
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…