| 2025 | DIS | Explaining Deep Neural Networks with Example and Pixel Attribution. | Genghua Dong, Henrik Bostrm, Roman Bresson, Amr Alkhatib |
| 2025 | ICASSP | Explaining Representations in Correlation-based Deep Multiview Representation Learning. | Maurice Kuschel, Amr Alkhatib, Tanuj Hasija, Henrik Bostrm |
| 2025 | ICML | Prediction via Shapley Value Regression. | Amr Alkhatib, Roman Bresson, Henrik Bostrm, Michalis Vazirgiannis |
| 2025 | IDA | Obtaining Example-Based Explanations from Deep Neural Networks. | Genghua Dong, Henrik Bostrm, Michalis Vazirgiannis, Roman Bresson |
| 2024 | AAAI | A Simple and Yet Fairly Effective Defense for Graph Neural Networks. | Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Bostrm |
| 2024 | DIS | Interpretable Graph Neural Networks for Heterogeneous Tabular Data. | Amr Alkhatib, Henrik Bostrm |
| 2024 | DIS | Faithfulness of Local Explanations for Tree-Based Ensemble Models. | Amir Hossein Akhavan Rahnama, Pierre Geurts, Henrik Bostrm |
| 2024 | ECAI | Interpretable Graph Neural Networks for Tabular Data. | Amr Alkhatib, Sofiane Ennadir, Henrik Bostrm, Michalis Vazirgiannis |
| 2024 | ICLR | Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks. | Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Bostrm |
| 2024 | IDA | Example-Based Explanations of Random Forest Predictions. | Henrik Bostrm |
| 2021 | MDAI | Well-Calibrated and Sharp Interpretable Multi-Class Models. | Ulf Johansson, Tuwe Lfstrm, Henrik Bostrm |
| 2020 | KI | Explaining Multivariate Time Series Forecasts: An Application to Predicting the Swedish GDP. | Henrik Bostrm, Peter Hglund, Sven-Olof Junker, Ann-Sofie berg, Martin Sparr |
| 2019 | AIME | Gated Hidden Markov Models for Early Prediction of Outcome of Internet-Based Cognitive Behavioral Therapy. | Negar Safinianaini, Henrik Bostrm, Viktor Kaldo |
| 2019 | DSAA | Customized Interpretable Conformal Regressors. | Ulf Johansson, Cecilia Snstrd, Tuwe Lfstrm, Henrik Bostrm |
| 2019 | SIGIR | Block-distributed Gradient Boosted Trees. | Theodore Vasiloudis, Hyunsu Cho, Henrik Bostrm |
| 2019 | SDM | Calibrating Probability Estimation Trees using Venn-Abers Predictors. | Ulf Johansson, Tuwe Lfstrm, Henrik Bostrm |
| 2018 | PAKDD | Classification with Reject Option Using Conformal Prediction. | Henrik Linusson, Ulf Johansson, Henrik Bostrm, Tuve Lfstrm |
| 2017 | ICMLA | Conformal Prediction Using Random Survival Forests. | Henrik Bostrm, Lars Asker, Ram B. Gurung, Isak Karlsson, Tony Lindgren, Panagiotis Papapetrou |
| 2017 | IJCNN | Model-agnostic nonconformity functions for conformal classification. | Ulf Johansson, Henrik Linusson, Tuve Lfstrm, Henrik Bostrm |
| 2016 | CBMS | Identifying Factors for the Effectiveness of Treatment of Heart Failure: A Registry Study. | Lars Asker, Henrik Bostrm, Panagiotis Papapetrou, Hans E. Persson |
| 2016 | DIS | Early Random Shapelet Forest. | Isak Karlsson, Panagiotis Papapetrou, Henrik Bostrm |
| 2016 | FlAIRS | Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins. | Ram B. Gurung, Tony Lindgren, Henrik Bostrm |
| 2016 | PAKDD | Reliable Confidence Predictions Using Conformal Prediction. | Henrik Linusson, Ulf Johansson, Henrik Bostrm, Tuve Lfstrm |
| 2015 | AMIA | Handling Temporality of Clinical Events for Drug Safety Surveillance. | Jing Zhao, Aron Henriksson, Maria Kvist, Lars Asker, Henrik Bostrm |
| 2015 | DSAA | Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection. | Aron Henriksson, Jing Zhao, Henrik Bostrm, Hercules Dalianis |
| 2015 | DSAA | Cascading adverse drug event detection in electronic health records. | Jing Zhao, Aron Henriksson, Henrik Bostrm |
| 2013 | ADMA | Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias. | Orlando P. Zacarias, Henrik Bostrm |
| 2013 | AIME | Predicting Adverse Drug Events by Analyzing Electronic Patient Records. | Isak Karlsson, Jing Zhao, Lars Asker, Henrik Bostrm |
| 2013 | CEC | Evolved decision trees as conformal predictors. | Ulf Johansson, Rikard Knig, Tuve Lfstrm, Henrik Bostrm |
| 2013 | ICDM | Conformal Prediction Using Decision Trees. | Ulf Johansson, Henrik Bostrm, Tuve Lfstrm |
| 2013 | IJCNN | Random brains. | Ulf Johansson, Tuve Lfstrm, Henrik Bostrm |
| 2013 | IJCNN | Effective utilization of data in inductive conformal prediction using ensembles of neural networks. | Tuve Lfstrm, Ulf Johansson, Henrik Bostrm |
| 2012 | FUSION | Choice of dimensionality reduction methods for feature and classifier fusion with nearest neighbor classifiers. | Sampath Deegalla, Henrik Bostrm, Keerthi Walgama |
| 2012 | ICMLA | Can Frequent Itemset Mining Be Efficiently and Effectively Used for Learning from Graph Data? | Thashmee Karunaratne, Henrik Bostrm |
| 2010 | ICMLA | Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - A Case Study with Medicinal Chemistry Datasets. | Thashmee Karunaratne, Henrik Bostrm, Ulf Norinder |
| 2010 | IJCNN | Comparing methods for generating diverse ensembles of artificial neural networks. | Tuve Lfstrm, Ulf Johansson, Henrik Bostrm |
| 2010 | SMC | Pin-pointing concept descriptions. | Cecilia Snstrd, Ulf Johansson, Henrik Bostrm, Ulf Norinder |
| 2009 | CIDM | Ensemble member selection using multi-objective optimization. | Tuve Lfstrm, Ulf Johansson, Henrik Bostrm |
| 2009 | FUSION | Fusion of dimensionality reduction methods: A case study in microarray classification. | Sampath Deegalla, Henrik Bostrm |
| 2009 | ICMLA | Improving Fusion of Dimensionality Reduction Methods for Nearest Neighbor Classification. | Sampath Deegalla, Henrik Bostrm |
| 2009 | ICMLA | Graph Propositionalization for Random Forests. | Thashmee Karunaratne, Henrik Bostrm |
| 2009 | KDD | Using uncertain chemical and thermal data to predict product quality in a casting process. | Catarina Dudas, Henrik Bostrm |
| 2008 | FlAIRS | Extending Nearest Neighbor Classification with Spheres of Confidence. | Ulf Johansson, Henrik Bostrm, Rikard Knig |
| 2008 | FUSION | On evidential combination rules for ensemble classifiers. | Henrik Bostrm, Ronnie Johansson, Alexander Karlsson |
| 2008 | ICMLA | Calibrating Random Forests. | Henrik Bostrm |
| 2008 | ICMLA | On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers. | Tuve Lfstrm, Ulf Johansson, Henrik Bostrm |
| 2008 | ICMLA | Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes. | Cecilia Snstrd, Ulf Johansson, Ulf Norinder, Henrik Bostrm |
| 2008 | IJCNN | The problem with ranking ensembles based on training or validation performance. | Ulf Johansson, Tuve Lfstrm, Henrik Bostrm |
| 2007 | FUSION | Feature vs. classifier fusion for predictive data mining a case study in pesticide classification. | Henrik Bostrm |
| 2007 | ICMLA | Estimating class probabilities in random forests. | Henrik Bostrm |
| 2007 | IDEAL | Classification of Microarrays with kNN: Comparison of Dimensionality Reduction Methods. | Sampath Deegalla, Henrik Bostrm |
| 2007 | SDM | Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets. | Henrik Bostrm |
| 2006 | ICMLA | Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification. | Sampath Deegalla, Henrik Bostrm |
| 2003 | IDA | Resolving Rule Conflicts with Double Induction. | Tony Lindgren, Henrik Bostrm |
| 2002 | ALT | Classification with Intersecting Rules. | Tony Lindgren, Henrik Bostrm |
| 2001 | CICLING | Automatic Keyword Extraction Using Domain Knowledge. | Anette Hulth, Jussi Karlgren, Anna Jonsson, Henrik Bostrm, Lars Asker |
| 2001 | ILP | Classifying Uncovered Examples by Rule Stretching. | Martin Eineborg, Henrik Bostrm |
| 2001 | KDD | Learning to recognize brain specific proteins based on low-level features from on-line prediction servers. | Mikael Huss, Henrik Bostrm, Lars Asker, Joakim Cster |
| 2000 | ILP | Learning First Order Logic Time Series Classifiers. | Juan Jos Rodrguez, Carlos J. Alonso, Henrik Bostrm |
| 1999 | ILP | Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction. | Henrik Bostrm, Lars Asker |
| 1996 | ECAI | Integrating Algorithmic Debugging and Unfolding Transformation in an Interactive Learner. | Zoltn Alexin, Tibor Gyimthy, Henrik Bostrm |
| 1996 | ICML | Theory-Guideed Induction of Logic Programs by Inference of Regular Languages. | Henrik Bostrm |
| 1995 | IJCAI | Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs. | Henrik Bostrm |
| 1990 | ICML | Generalizing the Order of Goals as an Approach to Generalizing Number. | Henrik Bostrm |