Helge Langseth
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/
Courses
- IT3030 - Deep Learning
- TDT4171 - Artificial Intelligence Methods
- DT8122 - Probabilistic Artificial Intelligence
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
2022
- (2022) Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches. Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART). vol. 3.
2021
- (2021) Probabilistic Models with Deep Neural Networks. Entropy. vol. 23 (1).
- (2021) Machine Learning in Financial Market Surveillance: A Survey. IEEE Access. vol. 9.
2020
- (2020) Analyzing concept drift: A case study in the financial sector. Intelligent Data Analysis. vol. 24 (3).
- (2020) Variational Inference over Nonstationary Data Streams for Exponential Family Models. Mathematics. vol. 8 (11).
- (2020) Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. Proceedings of Machine Learning Research (PMLR). vol. 124.
2019
- (2019) AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems. vol. 163.
- (2019) Learning similarity measures from data. Progress in Artificial Intelligence.
- (2019) Forecasting Intra-Hour Imbalances in Electric Power Systems. Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33 (1).
- (2019) Application of data-driven models in the analysis of marine power systems. Applied Ocean Research. vol. 92.
2018
- (2018) A deep network model for paraphrase detection in short text messages. Information Processing & Management. vol. 54.
- (2018) AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems. vol. 163.
- (2018) Effective hate-speech detection in Twitter data using recurrent neural networks. Applied intelligence (Boston). vol. 48 (12).
- (2018) Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. International Journal of Approximate Reasoning. vol. 100.
- (2018) A Review of Inference Algorithms for Hybrid Bayesian Networks. The journal of artificial intelligence research. vol. 62.
2017
- (2017) Financial data analysis with PGMs using AMIDST. IEEE International Conference on Data Mining Workshops, ICDMW.
- (2017) Content-Based Social Recommendation with Poisson Matrix Factorization. Lecture Notes in Computer Science (LNCS). vol. 10534 LNAI.
- (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) Bayesian Models of Data Streams with Hierarchical Power Priors. JMLR Workshop and Conference Proceedings. vol. 70.