Tomi Peltola

发表

Finite Adaptation and Multistep Moves in the Metropolis-Hastings Algorithm for Variable Selection in Genome-Wide Association Analysis

Aki Vehtari, P. Marttinen, Tomi Peltola, 2012, PloS one.

Interactive user intent modeling for eliciting priors of a normal linear model

Samuel Kaski, Giulio Jacucci, Tomi Peltola, 2016 .

Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans

Aki Vehtari, Mikko Sams, Enrico Glerean, 2017, PloS one.

Phenotype-driven identification of epithelial signalling clusters

Samuel Kaski, J. Klefström, E. Marques, 2018, Scientific Reports.

A decision-theoretic approach for model interpretability in Bayesian framework

Aki Vehtari, Samuel Kaski, Homayun Afrabandpey, 2019, Machine Learning.

Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study).

Kimmo Kaski, Tomi Peltola, Reino Laatikainen, 2012, Journal of proteome research.

Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study)

C. Forsblom, P. Groop, P. Soininen, 2011, Metabolomics.

Arterial stiffness and vascular complications in patients with type 1 diabetes: The Finnish Diabetic Nephropathy (FinnDiane) Study

D. Gordin, C. Forsblom, P. Groop, 2012, Annals of medicine.

Interactive AI with a Theory of Mind pdf

Pedram Daee, Tomi Peltola, Mustafa Mert Çelikok, 2019, ArXiv.

Depression, depressive symptoms and treatments in women who have recently given birth: UK cohort study

Samuel Kaski, K. Walters, S. Hardoon, 2018, BMJ Open.

Machine Teaching of Active Sequential Learners

Samuel Kaski, Pedram Daee, M. Çelikok, 2018, NeurIPS.

The interplay between lipoprotein phenotypes, adiponectin, and alcohol consumption

M. Savolainen, M. Ala-Korpela, M. Jauhiainen, 2012, Annals of medicine.

Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets

Samuel Kaski, Homayun Afrabandpey, Tomi Peltola, 2019, IJCAI.

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge

Samuel Kaski, Chen He, Giulio Jacucci, 2017, Bioinform..

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

Aki Vehtari, Samuel Kaski, Pedram Daee, 2017, IUI.

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

Samuel Kaski, Pedram Daee, Tomi Peltola, 2016, Machine Learning.

Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction

Samuel Kaski, Homayun Afrabandpey, Tomi Peltola, 2016, UMAP.

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

Samuel Kaski, Giulio Jacucci, Tomi Peltola, 2016, IUI.

Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction

Aki Vehtari, Tomi Peltola, Veikko Salomaa, 2014, BMA@UAI.

Probabilistic Formulation of the Take The Best Heuristic pdf

Samuel Kaski, Jussi Jokinen, Tomi Peltola, 2019, CogSci.

Phenotype-driven identification of epithelial signalling clusters

Samuel Kaski, Tomi Peltola, Juha Klefström, 2018, Scientific Reports.

Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data

Aki Vehtari, Tomi Peltola, Markus Perola, 2012, PloS one.

Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans

Aki Vehtari, R. Hari, M. Sams, 2017, PloS one.

Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach

Aki Vehtari, Samuel Kaski, Juho Piironen, 2019, ArXiv.

Artificial Intelligence for chemical risk assessment

Andrew Worth, Tomi Peltola, John Paul Gosling, 2020, Computational toxicology.

Finite Adaptation and Multistep Moves in the Metropolis-Hastings Algorithm for Variable Selection in Genome-Wide Association Analysis Supplementary Text S 1

A. Vehtari, Tomi Peltola, Pekka Marttinen, 2012 .