2024
Fokkema, M. (2024). One shape may not fit all: Detecting differences in trajectories using GAM trees. International Meeting of the Psychometric Society, Prague. slides
Fokkema, M. (2024). Subgroup detection in generalized mixed-effects models (GLMMs) and generalized additive models (GAMs). Workshop at Speech Prosody 2024, Leiden. workshop materials
Fokkema, M. (2024). One shape may not fit all: Subgroup discovery in smoothing spline models. Università degli Studi di Napoli Federico II DISES (Dipartimention di Scienze Economiche e Statistiche). slides
Fokkema, M. (2024). Towards scientific inference with machine learning. Retreat of the Formal Methods in Lifespan Psychology project, Max Plank Instittue Berlin. slides
Fokkema, M. (2024). Association for Psychological Science. Panel on AI Buzz: What’s Not New? slides
2023
Fokkema, M. & Hilbert, A. (2023). Prediction rule ensembling with relaxed and adaptive lasso penalties. 16th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2023). slides
Fokkema, M. & Lieber, T. (2023). Detecting shape heterogeneity in penalized regression splines. Psychoco International Workshop on Psychometric Computing 2023, Universität Zürich. slides
Fokkema, M. (2023). Artificial Intelligence for Psychologists. Labyrinth Career Event, Institute of Psychology, Leiden University. slides
Fokkema, M. (2023). Causal Inference and Personalized Treatment Recommendations with Prediction Rule Ensembles. Causal Inference & AI Convergence meeting, TU Delft. slides
2022
Fokkema, M. (2022). Prediction rule ensembles: Balancing accuracy and interpretability. Talk at the Methods and Statistics programme group, Faculty of Social and Behavioural Sciences, University of Amsterdam. slides
Fokkema, M. (2022). Keynote at the National Algorithm Debate. https://www.vka.nl/evenement/het-nationale-algoritme-debat-2022/. slides
Fokkema, M. (2022). Prediction rules! Bridging black-box and interpretable ML. Small Group Meeting on AI and Machine-Learning Algorithms in Personnel Recruitment, Selection, and Assessment, Vrije Universiteit Amsterdam. slides Friday
Fokkema, M. (2022). Balancing accuracy and interpretability with prediction rule ensembles. Small Group Meeting on AI and Machine-Learning Algorithms in Personnel Recruitment, Selection, and Assessment, Vrije Universiteit Amsterdam. slides Thursday
Fokkema, M. (2022). Trees and rules - Bridging interpretable and explainable machine learning. Symposium on Transparent Machine Learning, Vereniging voor Statistiek en Operations Research (VvSOR). slides
Fokkema, M. (2022). Born-Again and Bayesian approaches for improving trees. Paper presentation at the Conference of the International Federation of Classification Societies (IFCS 2022), Porto, Portugal. slides
Fokkema, M. (2022). Seeing the forest for the trees. Machine Learning Day, Vereniging voor Statistiek en Operations Research (VVS-OR), section Social Sciences. slides
2021
Fokkema, M. (2021). Something old, something new, something borrowed (and a spaghetti western). Keynote presentation, Annual Meeting of the Dutch-Flemish network for selection psychology, University of Groningen. slides
Fokkema, M. (2021). Born-again trees for predicting treatment outcomes. Treatment Selection Idea Lab 2021, University of Trier. slides
Fokkema, M. (2021). A few simple rules for prediction. SAILS Lunch Time Seminar, Leiden University. slides
Fokkema, M. (2021). Subgroup detection in growth trajectories. University of Montreal, Laboratoire sur les Changements Sociaux et l'Identité, Canada. workshop materials
Fokkema, M. (2021). Beslisbomen: Wat werkt voor wie? Altrecht Academic Anxiety Centre, The Netherlands. slides
2020
Fokkema, M. (2020). GLMM Trees: Recursive partitioning for multilevel and longitudinal data. Invited lecture at the International Workshop of the IBS-DR Working Group "Nonparametric Methods". TU Dortmund University, Dortmund, Germany. slides
Fokkema, M. (2020). Prediction Rule Ensembles (PREs): An interpretable machine-learning method. Invited talk at the Child Development Group, University of Amsterdam, The Netherlands. slides
Fokkema, M. (2020). GLMM- and GAM-based recursive partitioning: Decision trees for mixed-effects and generalized additive models. Invited talk at the LUXs seminar, Leiden Center of Statistical Science, Leiden, The Netherlands. slides
2019
Fokkema, M. (2019). Prediction rule ensembles: Balancing accuracy and interpretability in statistical prediction. Spotlight presentation at the International Meeting of the Psychometric Society, Santiago de Chile, Chile. slides
Fokkema, M. (2019). Decision tree methods for psychological assessment. Workshop taught at the 15th European Conference on Psychological Assessment, Brussels, Belgium. workshop materials
Fokkema, M. & Jorink, M. (2019). Fitting decision trees to multilevel and longitudinal data. Paper presented at the 15th European Conference on Psychological Assessment, Brussels, Belgium. slides
Fokkema, M. (2019) Recursive partitioning of clustered and longitudinal data with GLMM trees. Paper presented at the joint International Conference on Computational and Financial Econometrics and the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom. slides
Fokkema, M. & Jorink, M. (2019). Recursive partitioning of longitudinal and growth‐curve models. Paper presented at the 16th Conference of the International Federation of Classification Societies, Thessaloniki, Greece. slides
Fokkema, M. & Jorink, M. (2019). Recursive Partitioning of Growth Curve Models with LMM Trees. Paper presented at Psychoco International Work International Workshop on Psychometric Computing, Charles University & Czech Academy of Sciences, Prague, Czech Republic. slides
Fokkema, M. (2019). Decision tree methods for multilevel and longitudinal data. STAT talk, Leiden University, the Netherlands. slides
2018
Fokkema, M. (2018). Statistical Methods for Clinical Decision Making: Trees and Rules. Data Science Seminar at the Faculty of Social Sciences, Data Science Research Programme, Leiden University.
Fokkema, M. & Wolpert, M. (2018). Balancing Precision With Practicality: Decision Trees. Presentation at the Treatment Selection Idea Lab 2018, University College London.
Fokkema, M. (2018). Tree-Based Multilevel Models. Pre-Conference Workshop at the Treatment Selection Idea Lab 2018, University College London.
Fokkema, M. (2018). Prediction Rule Ensembles: An Accurate and Interpretable Method for Prediction. Paper presented at the Spring Meeting of the Dutch-Flemish classification society (VOC), Utrecht University, the Netherlands.
Fokkema, M. (2018). Prediction Rule Ensembles. Invited lecture at the Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck.
Fokkema, M. (2018). Prediction Rule Ensembles for Multilevel Data. Paper presentated at Psychoco International Work International Workshop on Psychometric Computing, Universität Tübingen, Germany.
2017
Fokkema, M. (2017). Prediction Rule Ensembles, or a Japanese Gardening Approach to Random Forests. Invited lecture at the Evidence Based Practice Unit of University College London and the Anna Freud National Centre for Children and Families, London, United Kingdom.
Fokkema, M. (2017). Prediction Rule Ensembles: An Accurate and Interpretable Statistical Tool for Practical Decision Making. Paper presented at the 14th European Conference on Psychological Assessment, Lisbon, Portugal.
Fokkema, M. (2017). Introduction to Classification and Regression Trees, Random Forests and Model-Based Recursive Partitioning in R. Invited two-day workshop taught at the Zurich R Courses, UZH/ETH Zurich, Switzerland.
Fokkema, M. (2017). Recursive Partitioning Methods. Invited lecture at the Feedback Research Expert Meeting, May 3-4, Leiden University, The Netherlands.
Fokkema, M. (2017). Prediction Rule Ensembles, or a Japanese Gardening Approach to Random Forests. Invited lecture at ZüKoSt: Seminar on Applied Statistics, ETH Zurich, Switzerland.
Fokkema, M. (2017). pre: An R package for Deriving Prediction Rule Ensembles. Paper presented at the Psychoco International Workshop on Psychometric Computing, Vienna, Austria.
2016
Fokkema, M., Zeileis, A., Smits, N., Hothorn, T. & Kelderman, H. (2016). Generalized linear mixed-effects regression (glimmer) trees: model-based recursive partitioning of multilevel data. Paper presented at the 81st Annual Meeting of the Psychometric Society, Asheville, NC.
Fokkema, M., Zeileis, A., Smits, N., Hothorn, T. & Kelderman, H. (2016). glmertree: recursive paritioning based on generalized linear mixed-effects models. Paper presented at the Psychoco International Workshop on Psychometric Computing, Liège, Belgium.
Fokkema, M., Zeileis, A., Smits, N., Hothorn, T. & Kelderman, H. (2016). glmertrees: recursive paritioning of generalized linear mixed models. Invited paper presentated at the Fourth Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik (DAGstat 2016), Göttingen, Germany.
2015
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., and Kelderman, H. (2015). Detection of treatment-subgroup interactions in clustered datasets. Paper presented at the 80th Annual Meeting of the Psychometric Society (IMPS 2015), Beijing, China.
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., and Kelderman, H. (2015). Detection of treatment-subgroup interactions in clustered datasets. Invited paper presentation at the Conference of the International Federation of Classification Societies (IFCS 2015), Bologna, Italy.
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., and Kelderman, H. (2015). Combining model-based recursive partitioning and random-effects estimation. Paper presented at the 10th Multilevel Conference, Utrecht, The Netherlands.
2014 and before
Fokkema, M., Smits, N., and Kelderman, H. (2014). Connecting clinical and actuarial prediction with rule-based methods. Paper presented at the 29th IOPS Summer Conference, Tilburg, The Netherlands.
Fokkema, M., Smits, N., and Kelderman, H. (2014). Connecting clinical and actuarial prediction with rule-based methods. Paper presented at the 79th Annual Meeting of the Psychometric Society (IMPS 2014), Madison, WI.
Fokkema, M., Smits, N., and Kelderman, H. (2013). Combining decision trees and stochastic curtailment for assessment length reduction of test batteries used for classification. Paper presented at the Conference of the International Federation of Classification Societies 2013, Tilburg, The Netherlands.
Fokkema, M., Smits, N., Kelderman, H., and Cuijpers, P. (2011). Response shifts in mental health interventions: an illustration of longitudinal measurement invariance. Paper presented at the 21st IOPS Winter Conference, Leiden, The Netherlands.
Fokkema, M., Smits, N., Kelderman, H. and Cuijpers, P. (2010). Measurement invariance of the Beck Depression Inventory in assessing and comparing treatment effects. Paper presented at the IV European Congress of Methodology, Potsdam, Germany.