Erik Strumbelj

According to our database, Erik Strumbelj authored at least 27 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Benchmarking the Fidelity and Utility of Synthetic Relational Data.

Valter Hudovernik, Martin Jurkovic, Erik Strumbelj

CoRR, 2024

2022

Bayesian Gaussian process factor analysis with copula for count data.

Gregor Pirs, Erik Strumbelj

Expert Syst. Appl., 2022

2021

Predicting the Popularity of Games on Steam.

Andraz De Luisa, Jan Hartman, David Nabergoj, Samo Pahor, Marko Rus, Bozhidar Stevanoski, Jure Demsar, Erik Strumbelj

CoRR, 2021

2020

Automatic attribute construction for basketball modelling.

Petar Vracar, Erik Strumbelj, Igor Kononenko

Knowl. Inf. Syst., 2020

Automated OpenCL GPU kernel fusion for Stan Math.

Tadej Ciglaric, Rok Cesnovar, Erik Strumbelj

Proceedings of the IWOCL '20: International Workshop on OpenCL, 2020

Opportunistic Positioning Using Unsynchronized References: Crowd Systems - Smartphones: Signals of opportunity, cellular systems (4G, 5G, WLAN, ...).

Matija Rezar, Erik Strumbelj, Giacomo Pojani, Chris Marshall

Proceedings of the European Navigation Conference, 2020

2019

An OpenCL library for parallel random number generators.

Tadej Ciglaric, Rok Cesnovar, Erik Strumbelj

J. Supercomput., 2019

Evaluating existing manually constructed natural landscape classification with a machine learning-based approach.

Rok Ciglic, Erik Strumbelj, Rok Cesnovar, Mauro Hrvatin, Drago Perko

J. Spatial Inf. Sci., 2019

Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures.

Gregor Pirs, Erik Strumbelj

J. Mach. Learn. Res., 2019

bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology.

Jure Demsar, Grega Repovs, Erik Strumbelj

CoRR, 2019

GPU-based Parallel Computation Support for Stan.

Rok Cesnovar, Steve Bronder, Davor Sluga, Jure Demsar, Tadej Ciglaric, Sean Talts, Erik Strumbelj

CoRR, 2019

2018

Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms.

Erik Strumbelj, Igor Kononenko

Proceedings of the Human and Machine Learning, 2018

A Bayesian approach to forecasting daily air-pollutant levels.

Jana Faganeli Pucer, Gregor Pirs, Erik Strumbelj

Knowl. Inf. Syst., 2018

2016

Modeling basketball play-by-play data.

Petar Vracar, Erik Strumbelj, Igor Kononenko

Expert Syst. Appl., 2016

2015

Predictive power of fantasy sports data for soccer forecasting.

Erik Strumbelj, Marko Robnik-Sikonja

Int. J. Data Min. Model. Manag., 2015

2014

Explaining prediction models and individual predictions with feature contributions.

Erik Strumbelj, Igor Kononenko

Knowl. Inf. Syst., 2014

2013

Explanation and Reliability of Individual Predictions.

Igor Kononenko, Erik Strumbelj, Zoran Bosnic, Darko Pevec, Matjaz Kukar, Marko Robnik-Sikonja

Informatica (Slovenia), 2013

Efficiently explaining the predictions of a probabilistic radial basis function classification network.

Marko Robnik-Sikonja, Erik Strumbelj, Igor Kononenko

Intell. Data Anal., 2013

2012

Quality of classification explanations with PRBF.

Marko Robnik-Sikonja, Igor Kononenko, Erik Strumbelj

Neurocomputing, 2012

2011

A General Method for Visualizing and Explaining Black-Box Regression Models.

Erik Strumbelj, Igor Kononenko

Proceedings of the Adaptive and Natural Computing Algorithms, 2011

Efficiently Explaining Decisions of Probabilistic RBF Classification Networks.

Marko Robnik-Sikonja, Aristidis Likas, Constantinos Constantinopoulos, Igor Kononenko, Erik Strumbelj

Proceedings of the Adaptive and Natural Computing Algorithms, 2011

Evaluating Reliability of Single Classifications of Neural Networks.

Darko Pevec, Erik Strumbelj, Igor Kononenko

Proceedings of the Adaptive and Natural Computing Algorithms, 2011

2010

Explanation and reliability of prediction models: the case of breast cancer recurrence.

Erik Strumbelj, Zoran Bosnic, Igor Kononenko, Branko Zakotnik, Cvetka Grasic Kuhar

Knowl. Inf. Syst., 2010

An Efficient Explanation of Individual Classifications using Game Theory.

Erik Strumbelj, Igor Kononenko

J. Mach. Learn. Res., 2010

2009

Explaining instance classifications with interactions of subsets of feature values.

Erik Strumbelj, Igor Kononenko, Marko Robnik-Sikonja

Data Knowl. Eng., 2009

Learning Betting Tips from Users' Bet Selections.

Erik Strumbelj, Marko Robnik-Sikonja, Igor Kononenko

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2009

2008

Towards a Model Independent Method for Explaining Classification for Individual Instances.

Erik Strumbelj, Igor Kononenko

Proceedings of the Data Warehousing and Knowledge Discovery, 10th International Conference, 2008