Helge Langseth

102 papers A* 2A 3B 2C 5Misc 3Journal 58Unranked 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data. CoRR David Baumgartner, Helge Langseth, Heri Ramampiaro
2026	J	`jnl`	Exploring How Fair Model Representations Relate to Fair Recommendations. CoRR Bjørnar Vassøy, Benjamin Kille, Helge Langseth
2026	J	`jnl`	Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection. CoRR David Baumgartner, Helge Langseth, Kenth Engø-Monsen, Heri Ramampiaro
2025	J	`jnl`	A Framework for Causal Concept-based Model Explanations. CoRR Anna Rodum Bjøru, Jacob Lysnæs-Larsen, Oskar Jørgensen, Inga Strümke, Helge Langseth
2025	J	`jnl`	A framework for causal concept-based model explanations. Frontiers Artif. Intell. Anna Rodum Bjøru, Jacob Lysnæs-Larsen, Oskar Jørgensen, Inga Strümke, Helge Langseth
2025	J	`jnl`	Causal computations in Semi Markovian Structural Causal Models using divide and conquer. CoRR Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2025	J	`jnl`	Divide and conquer for causal computation. Int. J. Approx. Reason. Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2025	J	`jnl`	Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems. CoRR Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-Sørensen, Helge Langseth, Odd Erik Gundersen
2025	J	`jnl`	Interpretable Deep Reinforcement Learning Via Concept-Based Policy Distillation. Mach. Learn. Yanzhe Bekkemoen, Helge Langseth
2025	—	`conf`	Opt-in Transparent Fairness for Recommender Systems. ECIR (1) Bjørnar Vassøy, Benjamin Kille, Helge Langseth
2024	—	`conf`	A Divide and Conquer Approach for Solving Structural Causal Models. PGM Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2024	J	`jnl`	Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation. Artif. Intell. Rev. Bjørnar Vassøy, Helge Langseth
2024	J	`jnl`	Probing the Robustness of Time-series Forecasting Models with CounterfacTS. CoRR Håkon Hanisch Kjærnli, Lluis Mas-Ribas, Aida Ashrafi, Gleb Sizov, Helge Langseth, Odd Erik Gundersen
2023	C	`conf`	ASAP: Attention-Based State Space Abstraction for Policy Summarization. ACML Yanzhe Bekkemoen, Helge Langseth
2023	—	`conf`	Bayesian Exploration in Deep Reinforcement Learning. NAIS Ludvig Killingberg, Helge Langseth
2023	J	`jnl`	Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation. CoRR Bjørnar Vassøy, Helge Langseth
2023	J	`jnl`	Deep Contextual Grid Triplet Network for Context-Aware Recommendation. IEEE Access Sofia Aftab, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco
2023	J	`jnl`	Lecture Notes in Probabilistic Diffusion Models. CoRR Inga Strümke, Helge Langseth
2023	—	`conf`	On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness. ACM-REP Odd Erik Gundersen, Saeid Shamsaliei, Håkon Sletten Kjærnli, Helge Langseth
2023	A	`conf`	Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. RecSys Bjørnar Vassøy, Helge Langseth, Benjamin Kille
2023	J	`jnl`	Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. CoRR Bjørnar Vassøy, Helge Langseth, Benjamin Kille
2023	J	`jnl`	The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning. Trans. Mach. Learn. Res. Ludvig Killingberg, Helge Langseth
2023	B	`conf`	mTADS: Multivariate Time Series Anomaly Detection Benchmark Suites. IEEE Big Data David Baumgartner, Helge Langseth, Heri Ramampiaro, Kenth Engø-Monsen
2022	—	`conf`	A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen
2022	J	`jnl`	A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process. CoRR Emil Blixt Hansen, Helge Langseth, Nadeem Iftikhar, Simon Bøgh
2022	—	`conf`	Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches. ICAART (3) Shweta Tiwari, Gavin Bell, Helge Langseth, Heri Ramampiaro
2021	J	`jnl`	Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities. CoRR Yanzhe Bekkemoen, Helge Langseth
2021	J	`jnl`	Machine Learning in Financial Market Surveillance: A Survey. IEEE Access Shweta Tiwari, Heri Ramampiaro, Helge Langseth
2021	J	`jnl`	Probabilistic Models with Deep Neural Networks. Entropy Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	Learning similarity measures from data. CoRR Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach, Helge Langseth
2020	J	`jnl`	Learning similarity measures from data. Prog. Artif. Intell. Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach, Helge Langseth
2020	A	`conf`	Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. UAI Tárik S. Salem, Helge Langseth, Heri Ramampiaro
2020	J	`jnl`	Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. CoRR Tárik S. Salem, Helge Langseth, Heri Ramampiaro
2019	J	`jnl`	AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2019	A*	`conf`	Forecasting Intra-Hour Imbalances in Electric Power Systems. AAAI Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth
2019	J	`jnl`	Forecasting Intra-Hour Imbalances in Electric Power Systems. CoRR Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth
2019	—	`ch.`	New Ideas in Ranking for Personalized Fashion Recommender Systems. Business and Consumer Analytics: New Ideas Heri Ramampiaro, Helge Langseth, Thomas Almenningen, Herman Schistad, Martin Havig, Hai Thanh Nguyen
2019	J	`jnl`	Probabilistic Models with Deep Neural Networks. CoRR Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2019	J	`jnl`	Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report). CoRR Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
2018	J	`jnl`	A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2018	J	`jnl`	A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. Basant Agarwal, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco
2018	J	`jnl`	Detecting Offensive Language in Tweets Using Deep Learning. CoRR Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
2018	J	`jnl`	Effective hate-speech detection in Twitter data using recurrent neural networks. Appl. Intell. Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
2018	J	`jnl`	Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2018	J	`jnl`	Securing Tag-based recommender systems against profile injection attacks: A comparative study. CoRR Georgios Pitsilis, Heri Ramampiaro, Helge Langseth
2018	J	`jnl`	Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention. CoRR Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian R. Lane, Xiaobing Liu
2018	Misc	`conf`	Understanding and improving recurrent networks for human activity recognition by continuous attention. UbiComp Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian R. Lane, Xiaobing Liu
2017	J	`jnl`	A Deep Network Model for Paraphrase Detection in Short Text Messages. CoRR Basant Agarwal, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco
2017	J	`jnl`	A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
2017	J	`jnl`	AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	A*	`conf`	Bayesian Models of Data Streams with Hierarchical Power Priors. ICML Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	J	`jnl`	Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	—	`conf`	Content-Based Social Recommendation with Poisson Matrix Factorization. ECML/PKDD (1) Eliezer de Souza da Silva, Helge Langseth, Heri Ramampiaro
2017	—	`conf`	Data Driven Case Base Construction for Prediction of Success of Marine Operations. ICCBR (Workshops) Bjørn Magnus Mathisen, Agnar Aamodt, Helge Langseth
2017	—	`conf`	Inter-Session Modeling for Session-Based Recommendation. DLRS@RecSys Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, Helge Langseth
2017	J	`jnl`	Inter-Session Modeling for Session-Based Recommendation. CoRR Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, Helge Langseth
2017	J	`jnl`	MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	J	`jnl`	Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2016	—	`conf`	Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2016	A	`conf`	Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
2016	—	`conf`	Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen
2016	—	`conf`	d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2015	—	`conf`	Dynamic Bayesian modeling for risk prediction in credit operations. SCAI Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	C	`conf`	Learning Conditional Distributions Using Mixtures of Truncated Basis Functions. ECSQARU Inmaculada Pérez-Bernabé, Antonio Salmerón, Helge Langseth
2015	C	`conf`	MPE Inference in Conditional Linear Gaussian Networks. ECSQARU Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
2015	B	`conf`	Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	—	`conf`	Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
2015	—	`conf`	Parallelisation of the PC Algorithm. CAEPIA Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
2015	—	`conf`	Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data. RecSys Challenge Øyvind H. Myklatun, Thorstein K. Thorrud, Hai Thanh Nguyen, Helge Langseth, Anders Kofod-Petersen
2015	J	`jnl`	Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. Helge Langseth, Thomas D. Nielsen
2015	J	`jnl`	Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning. IEEE Trans. Smart Grid Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth, Pinar Öztürk
2014	—	`conf`	A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen
2014	J	`jnl`	A classification-based approach to monitoring the safety of dynamic systems. Reliab. Eng. Syst. Saf. Shengtong Zhong, Helge Langseth, Thomas Dyhre Nielsen
2014	J	`jnl`	Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón
2014	—	`conf`	Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback. MIKE Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, Heri Ramampiaro
2014	—	`conf`	Requirement Engineering for a Small Project with Pre-Specified Scope. NIK Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón
2013	—	`conf`	Beating the bookie: A look at statistical models for prediction of football matches. SCAI Helge Langseth
2013	—	`conf`	Effects of data cleansing on load prediction algorithms. CIASG Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth
2013	—	`conf`	Fast Approximate Inference in Hybrid Bayesian Networks Using Dynamic Discretisation. IWINAC (1) Helge Langseth, David Marquez, Martin Neil
2012	J	`jnl`	A latent model for collaborative filtering. Int. J. Approx. Reason. Helge Langseth, Thomas Dyhre Nielsen
2012	J	`jnl`	Mixtures of truncated basis functions. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2011	C	`conf`	A hybrid CBR and BN architecture refined through data analysis. ISDA Tore Bruland, Agnar Aamodt, Helge Langseth
2011	—	`ed.`	Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011, Trondheim, Norway, May 24th - 26th, 2011 SCAI Anders Kofod-Petersen, Fredrik Heintz, Helge Langseth
2011	—	`conf`	Extended Abstract: A design for a tourist CF system. SCAI Terje N. Lillegraven, Arnt C. Wolden, Anders Kofod-Petersen, Helge Langseth
2011	—	`conf`	Extended Abstract: Combining CBR and BN using metareasoning. SCAI Tor Gunnar Houeland, Tore Bruland, Agnar Aamodt, Helge Langseth
2010	—	`conf`	Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support. IIP Tore Bruland, Agnar Aamodt, Helge Langseth
2010	J	`jnl`	Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2010	Misc	`conf`	Towards a More Expressive Model for Dynamic Classification. FLAIRS Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth
2009	J	`jnl`	Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2009	J	`jnl`	Latent classification models for binary data. Pattern Recognit. Helge Langseth, Thomas D. Nielsen
2009	C	`conf`	Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2007	J	`jnl`	Bayesian networks in reliability. Reliab. Eng. Syst. Saf. Helge Langseth, Luigi Portinale
2006	J	`jnl`	Classification using Hierarchical Naïve Bayes models. Mach. Learn. Helge Langseth, Thomas D. Nielsen
2005	J	`jnl`	Latent Classification Models. Mach. Learn. Helge Langseth, Thomas D. Nielsen
2003	J	`jnl`	Decision theoretic troubleshooting of coherent systems. Reliab. Eng. Syst. Saf. Helge Langseth, Finn Verner Jensen
2003	J	`jnl`	Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. J. Mach. Learn. Res. Helge Langseth, Thomas D. Nielsen
2002	—	`—`	Bayesian networks with applications in reliability analysis. Helge Langseth
2001	—	`conf`	Heuristics for Two Extensions of Basic Troubleshooting. SCAI Helge Langseth, Finn Verner Jensen
2001	J	`jnl`	Parameter Learning in Object-Oriented Bayesian Networks. Ann. Math. Artif. Intell. Helge Langseth, Olav Bangsø
2001	Misc	`conf`	Structural Learning in Object Oriented Domains. FLAIRS Olav Bangsø, Helge Langseth, Thomas D. Nielsen
2001	J	`jnl`	The SACSO methodology for troubleshooting complex systems. Artif. Intell. Eng. Des. Anal. Manuf. Finn Verner Jensen, Uffe Kjærulff, Brian Kristiansen, Helge Langseth, Claus Skaanning, Jirí Vomlel, Marta Vomlelová