Towards a Model Independent Method for Explaining Classification for Individual Instances. - Erik Strumbelj; Igor Kononenko 001 (DaWaK, 2008)

Learning Betting Tips from Users' Bet Selections. - Erik Strumbelj; Marko Robnik-Sikonja; Igor Kononenko 001 (MLDM, 2009)

Explaining instance classifications with interactions of subsets of feature values. - Erik Strumbelj; Igor Kononenko 001; Marko Robnik-Sikonja (Data Knowl. Eng., 2009)

An Efficient Explanation of Individual Classifications using Game Theory. - Erik Strumbelj; Igor Kononenko 001 (J. Mach. Learn. Res., 2010)

Explanation and reliability of prediction models: the case of breast cancer recurrence. - Erik Strumbelj; Zoran Bosnic; Igor Kononenko 001; Branko Zakotnik; Cvetka Grasic Kuhar (Knowl. Inf. Syst., 2010)

Evaluating Reliability of Single Classifications of Neural Networks. - Darko Pevec; Erik Strumbelj; Igor Kononenko 001 (ICANNGA (1), 2011)

Efficiently Explaining Decisions of Probabilistic RBF Classification Networks. - Marko Robnik-Sikonja; Aristidis Likas; Constantinos Constantinopoulos; Igor Kononenko 001; Erik Strumbelj (ICANNGA (1), 2011)

A General Method for Visualizing and Explaining Black-Box Regression Models. - Erik Strumbelj; Igor Kononenko 001 (ICANNGA (2), 2011)

Quality of classification explanations with PRBF. - Marko Robnik-Sikonja; Igor Kononenko 001; Erik Strumbelj (Neurocomputing, 2012)

Efficiently explaining the predictions of a probabilistic radial basis function classification network. - Marko Robnik-Sikonja; Erik Strumbelj; Igor Kononenko 001 (Intell. Data Anal., 2013)

Explanation and Reliability of Individual Predictions. - Igor Kononenko 001; Erik Strumbelj; Zoran Bosnic; Darko Pevec; Matjaz Kukar; Marko Robnik-Sikonja (Informatica (Slovenia), 2013)

Explaining prediction models and individual predictions with feature contributions. - Erik Strumbelj; Igor Kononenko 001 (Knowl. Inf. Syst., 2014)

Predictive power of fantasy sports data for soccer forecasting. - Erik Strumbelj; Marko Robnik-Sikonja (Int. J. Data Min. Model. Manag., 2015)

Modeling basketball play-by-play data. - Petar Vracar; Erik Strumbelj; Igor Kononenko 001 (Expert Syst. Appl., 2016)

A Bayesian approach to forecasting daily air-pollutant levels. - Jana Faganeli Pucer; Gregor Pirs; Erik Strumbelj (Knowl. Inf. Syst., 2018)

Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms. - Erik Strumbelj; Igor Kononenko 001 (Human and Machine Learning, 2018)

GPU-based Parallel Computation Support for Stan. - Rok Cesnovar; Steve Bronder; Davor Sluga; Jure Demsar; Tadej Ciglaric; Sean Talts; Erik Strumbelj (CoRR, 2019)

bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology. - Jure Demsar; Grega Repovs; Erik Strumbelj (CoRR, 2019)

Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures. - Gregor Pirs; Erik Strumbelj (J. Mach. Learn. Res., 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)

An OpenCL library for parallel random number generators. - Tadej Ciglaric; Rok Cesnovar; Erik Strumbelj (J. Supercomput., 2019)

Opportunistic Positioning Using Unsynchronized References: Crowd Systems - Smartphones: Signals of opportunity, cellular systems (4G, 5G, WLAN, ...). - Matija Rezar; Erik Strumbelj; Giacomo Pojani; Chris Marshall (ENC, 2020)

Automated OpenCL GPU kernel fusion for Stan Math. - Tadej Ciglaric; Rok Cesnovar; Erik Strumbelj (IWOCL, 2020)

Automatic attribute construction for basketball modelling. - Petar Vracar; Erik Strumbelj; Igor Kononenko 001 (Knowl. Inf. Syst., 2020)

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)

Bayesian Gaussian process factor analysis with copula for count data. - Gregor Pirs; Erik Strumbelj (Expert Syst. Appl., 2022)

Benchmarking the Fidelity and Utility of Synthetic Relational Data. - Valter Hudovernik; Martin Jurkovic; Erik Strumbelj (CoRR, 2024)

Empirical evaluation of normalizing flows in Markov Chain Monte Carlo. - David Nabergoj; Erik Strumbelj (CoRR, 2024)

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models. - Valter Hudovernik; Minkai Xu; Juntong Shi; Lovro Subelj; Stefano Ermon; Erik Strumbelj; Jure Leskovec (CoRR, 2025)