69 citations · 1.1k across the 120 of their papers we have counts for
13 papers · 1 filter
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…
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies…
Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander +2
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called like…
Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
Manuel J. A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé +4
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is…
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Muhammad Ammad-ud-din, Suleiman A. Khan, Disha Malani +4
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sam…