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2020 – today
- 2024
[j31]Bjørnar Vassøy, Helge Langseth:
Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation. Artif. Intell. Rev. 57(4): 101 (2024)- [i18]Håkon Hanisch Kjærnli, Lluis Mas-Ribas, Aida Ashrafi, Gleb Sizov, Helge Langseth, Odd Erik Gundersen:
Probing the Robustness of Time-series Forecasting Models with CounterfacTS. CoRR abs/2403.03508 (2024) - 2023
- [j30]Sofia Aftab
, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco:
Deep Contextual Grid Triplet Network for Context-Aware Recommendation. IEEE Access 11: 97522-97537 (2023) - [j29]Ludvig Killingberg, Helge Langseth:
The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c39]Yanzhe Bekkemoen, Helge Langseth:
ASAP: Attention-Based State Space Abstraction for Policy Summarization. ACML 2023: 137-152 - [c38]Odd Erik Gundersen, Saeid Shamsaliei, Håkon Sletten Kjærnli, Helge Langseth:
On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness. ACM-REP 2023: 37-61 - [c37]David Baumgartner, Helge Langseth, Heri Ramampiaro, Kenth Engø-Monsen:
mTADS: Multivariate Time Series Anomaly Detection Benchmark Suites. IEEE Big Data 2023: 588-597 - [c36]Ludvig Killingberg, Helge Langseth:
Bayesian Exploration in Deep Reinforcement Learning. NAIS 2023 - [c35]Bjørnar Vassøy, Helge Langseth, Benjamin Kille:
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. RecSys 2023: 871-876 - [i17]Bjørnar Vassøy, Helge Langseth:
Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation. CoRR abs/2305.09330 (2023) - [i16]Bjørnar Vassøy, Helge Langseth, Benjamin Kille:
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. CoRR abs/2308.15230 (2023) - [i15]Inga Strümke, Helge Langseth:
Lecture Notes in Probabilistic Diffusion Models. CoRR abs/2312.10393 (2023) - 2022
- [c34]Shweta Tiwari, Gavin Bell, Helge Langseth, Heri Ramampiaro:
Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches. ICAART (3) 2022: 975-983 - [c33]Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen:
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM 2022: 205-216 - [i14]Emil Blixt Hansen, Helge Langseth, Nadeem Iftikhar, Simon Bøgh:
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process. CoRR abs/2208.05208 (2022) - 2021
- [j28]Shweta Tiwari, Heri Ramampiaro, Helge Langseth:
Machine Learning in Financial Market Surveillance: A Survey. IEEE Access 9: 159734-159754 (2021) - [j27]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. Entropy 23(1): 117 (2021) - [i13]Yanzhe Bekkemoen, Helge Langseth:
Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities. CoRR abs/2105.02653 (2021) - 2020
- [j26]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24(3): 665-688 (2020) - [j25]Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach, Helge Langseth:
Learning similarity measures from data. Prog. Artif. Intell. 9(2): 129-143 (2020) - [c32]Tárik S. Salem, Helge Langseth, Heri Ramampiaro:
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. UAI 2020: 1179-1187 - [i12]Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach, Helge Langseth:
Learning similarity measures from data. CoRR abs/2001.05312 (2020) - [i11]Tárik S. Salem, Helge Langseth, Heri Ramampiaro:
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. CoRR abs/2007.09670 (2020)
2010 – 2019
- 2019
- [j24]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - [c31]Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth:
Forecasting Intra-Hour Imbalances in Electric Power Systems. AAAI 2019: 9595-9600 - [p1]Heri Ramampiaro, Helge Langseth, Thomas Almenningen, Herman Schistad, Martin Havig, Hai Thanh Nguyen:
New Ideas in Ranking for Personalized Fashion Recommender Systems. Business and Consumer Analytics: New Ideas 2019: 933-961 - [i10]Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth:
Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report). CoRR abs/1901.08422 (2019) - [i9]Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth:
Forecasting Intra-Hour Imbalances in Electric Power Systems. CoRR abs/1902.00563 (2019) - [i8]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. CoRR abs/1908.03442 (2019) - 2018
- [j23]Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth:
Effective hate-speech detection in Twitter data using recurrent neural networks. Appl. Intell. 48(12): 4730-4742 (2018) - [j22]Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100: 115-134 (2018) - [j21]Basant Agarwal, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco:
A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. 54(6): 922-937 (2018) - [j20]Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. 62: 799-828 (2018) - [c30]Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian R. Lane, Xiaobing Liu:
Understanding and improving recurrent networks for human activity recognition by continuous attention. UbiComp 2018: 56-63 - [i7]Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth:
Detecting Offensive Language in Tweets Using Deep Learning. CoRR abs/1801.04433 (2018) - [i6]Georgios Pitsilis, Heri Ramampiaro, Helge Langseth:
Securing Tag-based recommender systems against profile injection attacks: A comparative study. CoRR abs/1808.10550 (2018) - [i5]Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian R. Lane, Xiaobing Liu:
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention. CoRR abs/1810.04038 (2018) - 2017
- [j19]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88: 435-451 (2017) - [j18]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. 117: 46-55 (2017) - [j17]Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6(2): 133-144 (2017) - [c29]Bjørn Magnus Mathisen, Agnar Aamodt, Helge Langseth:
Data Driven Case Base Construction for Prediction of Success of Marine Operations. ICCBR (Workshops) 2017: 104-113 - [c28]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. ICML 2017: 2334-2343 - [c27]Eliezer de Souza da Silva, Helge Langseth, Heri Ramampiaro:
Content-Based Social Recommendation with Poisson Matrix Factorization. ECML/PKDD (1) 2017: 530-546 - [c26]Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, Helge Langseth:
Inter-Session Modeling for Session-Based Recommendation. DLRS@RecSys 2017: 24-31 - [i4]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR abs/1704.01427 (2017) - [i3]Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, Helge Langseth:
Inter-Session Modeling for Session-Based Recommendation. CoRR abs/1706.07506 (2017) - [i2]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR abs/1707.02293 (2017) - [i1]Basant Agarwal, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco:
A Deep Network Model for Paraphrase Detection in Short Text Messages. CoRR abs/1712.02820 (2017) - 2016
- [c25]Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa:
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c24]Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - [c23]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models 2016: 321-332 - [c22]Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen:
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models 2016: 415-425 - 2015
- [j16]Helge Langseth, Thomas D. Nielsen:
Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. 74: 1-11 (2015) - [j15]Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth, Pinar Öztürk:
Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning. IEEE Trans. Smart Grid 6(4): 1904-1913 (2015) - [c21]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
Parallelisation of the PC Algorithm. CAEPIA 2015: 14-24 - [c20]Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c19]Inmaculada Pérez-Bernabé, Antonio Salmerón, Helge Langseth:
Learning Conditional Distributions Using Mixtures of Truncated Basis Functions. ECSQARU 2015: 397-406 - [c18]Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
MPE Inference in Conditional Linear Gaussian Networks. ECSQARU 2015: 407-416 - [c17]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c16]Øyvind H. Myklatun, Thorstein K. Thorrud, Hai Thanh Nguyen, Helge Langseth, Anders Kofod-Petersen:
Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data. RecSys Challenge 2015: 5:1-5:4 - [c15]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j14]Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón:
Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. 55(4): 940-956 (2014) - [j13]Shengtong Zhong, Helge Langseth, Thomas Dyhre Nielsen:
A classification-based approach to monitoring the safety of dynamic systems. Reliab. Eng. Syst. Saf. 121: 61-71 (2014) - [c14]Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, Heri Ramampiaro:
Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback. MIKE 2014: 51-61 - [c13]Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón:
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK 2014 - [c12]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen:
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models 2014: 302-317 - 2013
- [c11]Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth:
Effects of data cleansing on load prediction algorithms. CIASG 2013: 93-100 - [c10]Helge Langseth, David Marquez, Martin Neil:
Fast Approximate Inference in Hybrid Bayesian Networks Using Dynamic Discretisation. IWINAC (1) 2013: 225-234 - [c9]Helge Langseth:
Beating the bookie: A look at statistical models for prediction of football matches. SCAI 2013: 165-174 - 2012
- [j12]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Mixtures of truncated basis functions. Int. J. Approx. Reason. 53(2): 212-227 (2012) - [j11]Helge Langseth, Thomas Dyhre Nielsen:
A latent model for collaborative filtering. Int. J. Approx. Reason. 53(4): 447-466 (2012) - 2011
- [c8]Tore Bruland, Agnar Aamodt, Helge Langseth:
A hybrid CBR and BN architecture refined through data analysis. ISDA 2011: 906-913 - [c7]Tor Gunnar Houeland, Tore Bruland, Agnar Aamodt, Helge Langseth:
Extended Abstract: Combining CBR and BN using metareasoning. SCAI 2011: 189-190 - [c6]Terje N. Lillegraven, Arnt C. Wolden, Anders Kofod-Petersen, Helge Langseth:
Extended Abstract: A design for a tourist CF system. SCAI 2011: 193-194 - [e1]Anders Kofod-Petersen, Fredrik Heintz, Helge Langseth:
Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011, Trondheim, Norway, May 24th - 26th, 2011. Frontiers in Artificial Intelligence and Applications 227, IOS Press 2011, ISBN 978-1-60750-753-6 [contents] - 2010
- [j10]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. 51(5): 485-498 (2010) - [c5]Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth:
Towards a More Expressive Model for Dynamic Classification. FLAIRS 2010 - [c4]Tore Bruland, Agnar Aamodt, Helge Langseth:
Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support. Intelligent Information Processing 2010: 82-91
2000 – 2009
- 2009
- [j9]Helge Langseth, Thomas D. Nielsen:
Latent classification models for binary data. Pattern Recognit. 42(11): 2724-2736 (2009) - [j8]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. 94(10): 1499-1509 (2009) - [c3]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU 2009: 240-251 - 2007
- [j7]Helge Langseth, Luigi Portinale:
Bayesian networks in reliability. Reliab. Eng. Syst. Saf. 92(1): 92-108 (2007) - 2006
- [j6]Helge Langseth, Thomas D. Nielsen:
Classification using Hierarchical Naïve Bayes models. Mach. Learn. 63(2): 135-159 (2006) - 2005
- [j5]Helge Langseth, Thomas D. Nielsen:
Latent Classification Models. Mach. Learn. 59(3): 237-265 (2005) - 2003
- [j4]Helge Langseth, Thomas D. Nielsen:
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. J. Mach. Learn. Res. 4: 339-368 (2003) - [j3]Helge Langseth, Finn Verner Jensen:
Decision theoretic troubleshooting of coherent systems. Reliab. Eng. Syst. Saf. 80(1): 49-62 (2003) - 2002
- [b1]Helge Langseth:
Bayesian networks with applications in reliability analysis. Norwegian University of Science and Technology, Trondheim, Norway, 2002 - 2001
- [j2]Finn Verner Jensen, Uffe Kjærulff, Brian Kristiansen, Helge Langseth, Claus Skaanning, Jirí Vomlel, Marta Vomlelová:
The SACSO methodology for troubleshooting complex systems. Artif. Intell. Eng. Des. Anal. Manuf. 15(4): 321-333 (2001) - [j1]Helge Langseth, Olav Bangsø:
Parameter Learning in Object-Oriented Bayesian Networks. Ann. Math. Artif. Intell. 32(1-4): 221-243 (2001) - [c2]Olav Bangsø, Helge Langseth, Thomas D. Nielsen:
Structural Learning in Object Oriented Domains. FLAIRS 2001: 340-344 - [c1]Helge Langseth, Finn Verner Jensen:
Heuristics for Two Extensions of Basic Troubleshooting. SCAI 2001: 80-89
Coauthor Index
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