helge.langseth@ntnu.no +47 73596488
Background and activities
Area of research
My research is on computational structures for helping people making clever decision when faced with uncertainty. In paricular, I work with
- Probabilistic graphical models, in particular Bayesian networks
- Decision support systems
- Bayesian methods
- Machine learning
Research group: Intelligent systems
Homepage: www.idi.ntnu.no/~helgel/
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2017) A Parallel Algorithm for Bayesian Network Structure Learning from Large Data Sets. Knowledge-Based Systems. vol. 117.
- (2017) Scaling up Bayesian variational inference using distributed computing clusters. International Journal of Approximate Reasoning. vol. 88.
- (2017) MAP inference in dynamic hybrid Bayesian networks. Progress in Artificial Intelligence. vol. 6 (2).
- (2016) d-VMP: Distributed Variational Message Passing. Journal of machine learning research. vol. 55.
- (2016) Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Journal of machine learning research. vol. 52.
- (2016) Parallel filter-based feature selection based on balanced incomplete block designs. Frontiers in Artificial Intelligence and Applications. vol. 285.
- (2015) Dynamic Bayesian modeling for risk prediction in credit operations. Frontiers in Artificial Intelligence and Applications. vol. 278.
- (2015) Modeling concept drift: A probabilistic graphical model based approach. Lecture Notes in Computer Science. vol. 9385.
- (2015) Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning. IEEE Transactions on Smart Grid. vol. 6 (4).
- (2015) Scalable learning of probabilistic latent models for collaborative filtering. Decision Support Systems. vol. 74.
- (2015) Parallelization of the PC Algorithm. Lecture Notes in Computer Science. vol. 9422.
- (2015) Learning conditional distributions using mixtures of truncated basis functions. Lecture Notes in Computer Science. vol. 9161.
- (2015) Parallel importance sampling in conditional linear gaussian networks. Lecture Notes in Computer Science. vol. 9422.
- (2015) MPE inference in Conditional Linear Gaussian Networks. Lecture Notes in Computer Science. vol. 9161.
- (2014) Learning mixtures of truncated basis functions from data. International Journal of Approximate Reasoning. vol. 55 (4).
- (2014) Learning to Rank for Personalized Fashion Recommender Systems via Implicit Feedback. Lecture Notes in Computer Science. vol. 8891.
- (2014) Requirement Engineering for a Small Project with Pre-Specified Scope. NIK: Norsk Informatikkonferanse.
- (2014) A classification-based approach to monitoring the safety of dynamic systems. Reliability Engineering & System Safety. vol. 121.
- (2013) Fast approximate inference in hybrid Bayesian networks using dynamic discretisation. Lecture Notes in Computer Science. vol. 7930.
- (2012) A latent model for collaborative filtering. International Journal of Approximate Reasoning. vol. 53 (4).