Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Implicitly Adaptive Importance Sampling

Abstract: Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we present an implicit adaptive importance sampling method that applies to complicated distributions which are not available in closed form. The method iteratively matches the moments of a set of Monte Carlo draws to weighted moments based on importance weights. We apply the method to Bayesian leave-one-out cross-validation and show that it performs better than many existing parametric adaptive importance sampling methods while being computationally inexpensive.

A system for context-dependent user modeling

Abstract: We present a system for learning and utilizing context-dependent user models The user models attempt to capture the interests of a user and link the interests to the situation of the user The models are used for making recommendations to applications and services on what might interest the user in her current situation In the design process we have analyzed several mock-ups of new mobile, context-aware services and applications The mock-ups spanned rather diverse domains, which helped us to ensure that the system is applicable to a wide range of tasks, such as modality recommendations (e.g., switching to speech output when driving a car), service category recommendations (e.g., journey planners at a bus stop), and recommendations of group members (e.g., people with whom to share a car) The structure of the presented system is highly modular First of all, this ensures that the algorithms that are used to build the user models can be easily replaced Secondly, the modularity makes it easier to evaluate how well different algorithms perform in different domains The current implementation of the system supports rule based reasoning and tree augmented naive Bayesian classifiers (TAN) The system consists of three components, each of which has been implemented as a web service The entire system has been deployed and is in use in the EU IST project MobiLife In this paper, we detail the components that are part of the system and introduce the interactions between the components In addition, we briefly discuss the quality of the recommendations that our system produces.

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

Abstract: Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information Supplementary data are available at Bioinformatics online.