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.

An Improved Admissible Heuristic for Learning Optimal Bayesian Networks

Abstract: Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents independently. As a result, the heuristic may contain many directed cycles and result in a loose bound. This paper introduces an improved admissible heuristic that tries to avoid directed cycles within small groups of variables. A sparse representation is also introduced to store only the unique optimal parent choices. Empirical results show that the new techniques significantly improved the efficiency and scalability of A* and BFBnB on most of datasets tested in this paper.

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

Abstract: 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 in precision medicine, where obtaining additional data can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the relevance of the covariates, or of values of the regression coefficients, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of the proposed method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.

Improving accuracy in back-of-device multitouch typing: a clustering-based approach to keyboard updating

Abstract: Recent work has shown that a multitouch sensor attached to the back of a handheld device can allow rapid typing engaging all ten fingers. However, high error rates remain a problem, because the user can not see or feel key-targets on the back. We propose a machine learning approach that can significantly improve accuracy. The method considers hand anatomy and movement ranges of fingers. The key insight is a combination of keyboard and hand models in a hierarchical clustering method. This enables dynamic re-estimation of key-locations while typing to account for changes in hand postures and movement ranges of fingers. We also show that accuracy can be further improved with language models. Results from a user study show improvements of over 40% compared to the previously deployed "naive" approach. We examine entropy as a touch precision metric with respect to typing experience. We also find that the QWERTY layout is not ideal. Finally, we conclude with ideas for further improvements.

Measuring social relations with multiple datasets

Abstract: Because people have different levels of engagement with each other, measuring social relations is difficult. In this work, we propose a method of measuring social relations with multiple datasets and demonstrate the differences with empirical evidence. Our empirical findings demonstrate that people use different communication media channels differently. Therefore, we suggest that in order to understand social structures, one should use several kinds of data sources and not just depend on a single dataset. Our datasets include mobile phone data gathered with handset-based measurements and data from OtaSizzle online social media services. By means of social network analysis, we show that the online social media services have a different friendship network than the networks based on mobile phone communication. The mobile phone communication networks, however, have a very similar structure. These results are encouraging as previous research also indicates differences in the communication networks.