Agenda
Day 1: July 5, 2023 (WED)
| Start Time (ECT) | End Time (ECT) | Track | Session Title | Speaker(s) | Room | Chair |
|---|---|---|---|---|---|---|
| 8:00 | 8:45 | Registration | Registration | - | Hall | - |
| 8:45 | 9:00 | Opening Session | Opening Session | - | C.003 | - |
| 9:00 | 10:00 | Keynote Day 1 | Towards Reliable Machine Learning | Masashi Sugiyama | C.003 | Ying Chen |
| 10:00 | 10:30 | Coffee-Break | Coffee-Break | - | Hall | - |
| 10:30 | 10:55 | Invited Sessions - Statistical Learning and Data Science | Sparsity: From High-Dimensional Statistics To Deep Learning | Johannes Lederer | C.003 | Ines Wilms |
| 10:55 | 11:20 | Invited Sessions - Statistical Learning and Data Science | The Influence Function of Graphical Lasso Estimators | Jakob Raymaekers | C.003 | Ines Wilms |
| 11:20 | 11:45 | Invited Sessions - Statistical Learning and Data Science | Monitoring Machine Learning Forecasts for Platform Data Streams | Jeroen Rombouts | C.003 | Ines Wilms |
| 11:45 | 12:10 | Invited Sessions - Statistical Learning and Data Science | Practicable portfolio optimization for portfolios that contain nonfungible tokens | Sven Serneels | C.003 | Ines Wilms |
| 10:30 | 10:55 | Invited Sessions - Converging Machine Learning and Network Analysis | Analayzing a citation network with a billion arcs | Thorsten Koch | C.002 | Ying Chen and Simon Trimborn |
| 10:55 | 11:20 | Invited Sessions - Converging Machine Learning and Network Analysis | Explainable Machine Learning for Lending Default Classification with Dependent Features | Paolo Pagnottoni | C.002 | Ying Chen and Simon Trimborn |
| 11:20 | 11:45 | Invited Sessions - Converging Machine Learning and Network Analysis | Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels | Julia Schaumburg | C.002 | Ying Chen and Simon Trimborn |
| 11:45 | 12:10 | Invited Sessions - Converging Machine Learning and Network Analysis | Financial Forecasting Using Deep Learning and Text Data: A Cryptocurrency Return Prediction Case Study | Vincent Gurgul | C.002 | Ying Chen and Simon Trimborn |
| 12:10 | 12:30 | Posters | Pitches (2 min) + Outside Q&A | C.003 | Kevin Mets | |
| Posters | Zoë-Mae Adams | Visualising opinions on the COVID-19 pandemic | C.003 | Kevin Mets | ||
| Posters | Amanda Chu | Enhancing the predictive power of Google Trends data through network analysis: An infodemiology study of COVID-19 | C.003 | Kevin Mets | ||
| Posters | Yu-Hui Huang | Uncertainty based Error Correction Model for Semantic Segmentation | C.003 | Kevin Mets | ||
| Posters | Markowska Małgorzata | Dynamic Cluster Analysis of Enterprises Number in Poland’s Provinces | C.003 | Kevin Mets | ||
| Posters | Elżbieta Sobczak | Public health in the context of sustainable development as a basis for assessing the diversity of European Union countries | C.003 | Kevin Mets | ||
| Posters | Kei Tsubotani | Constructing of Quasi-instrumental Variables Based on Sufficient Dimension Reduction | C.003 | Kevin Mets | ||
| Posters | Yuji Tsubota | An alternative model-based approach to causal mediation analysis with ordinal outcomes | C.003 | Kevin Mets | ||
| Posters | Ine Weyts | Using counterfactual explanations to detect bias in misclassifications | C.003 | Kevin Mets | ||
| 12:30 | 14:00 | Lunch | Lunch | - | - | - |
| 14:00 | 14:20 | Contributed Sessions - Mixture models | Conditional Mixture Modeling | Volodymyr Melnykov | C.101 | Christian Hennig |
| 14:20 | 14:40 | Contributed Sessions - Mixture models | A Mixture Modelling Approach to Enhance the Multisensory Experience of Museum Visitors | Matteo Ventura | C.101 | Christian Hennig |
| 14:40 | 15:00 | Contributed Sessions - Mixture models | Exploring the Equivalence of two Mixture Models for Rating Data in the CUB class | Ambra Macis | C.101 | Christian Hennig |
| 15:00 | 15:20 | Contributed Sessions - Mixture models | On contaminated transformation mixture models | Yana Melnykov | C.101 | Christian Hennig |
| 15:20 | 15:40 | Contributed Sessions - Mixture models | Mixture-based clustering with covariates for ordinal responses | Marta Nai Ruscone | C.101 | Christian Hennig |
| 14:00 | 14:20 | Contributed Sessions - Dimensionality reduction and latent variable modeling | Relationships Among Nonrandom/Random Score Formulations of PCA and Factor Analysis | Kohei Adachi | C.102 | Sven Serneels |
| 14:20 | 14:40 | Contributed Sessions - Dimensionality reduction and latent variable modeling | Combining New Dimension Reduction Tools for High-Dimensional Regression | Roman Parzer | C.102 | Sven Serneels |
| 14:40 | 15:00 | Contributed Sessions - Dimensionality reduction and latent variable modeling | Multi-way compositions and its analysis based on the elemental information | Kamila Fačevicová | C.102 | Sven Serneels |
| 15:00 | 15:20 | Contributed Sessions - Dimensionality reduction and latent variable modeling | Simultaneous orthogonal rotation of parameter matrices in generalized structured component analysis | Naoto Yamashita | C.102 | Sven Serneels |
| 15:20 | 15:40 | Contributed Sessions - Dimensionality reduction and latent variable modeling | Factor analysis with variable selection via group L0 penalty | Naoya Shimada | C.102 | Sven Serneels |
| 14:00 | 14:20 | Contributed Sessions - Explainable AI and causal inference | Explainable AI for Rapid and Simple Differential Diagnosis of Inflammatory Conditions Using Combined Myeloid ActivationTest, Complete Blood Count, and CRP Analysis | Michael Thrun | C.103 | Stefan Lessmann |
| 14:20 | 14:40 | Contributed Sessions - Explainable AI and causal inference | The trade-offs of obscuring your digital footprints | Sofie Goethals | C.103 | Stefan Lessmann |
| 14:40 | 15:00 | Contributed Sessions - Explainable AI and causal inference | Multi-Modal Counterfactual Explanations for Image Classification | Camille Dams | C.103 | Stefan Lessmann |
| 15:00 | 15:20 | Contributed Sessions - Explainable AI and causal inference | Performance Evaluation of Doubly Robust Estimators of Quantile Treatment Effects on Model Misspecification | Takehiro Shoji | C.103 | Stefan Lessmann |
| 15:20 | 15:40 | Contributed Sessions - Explainable AI and causal inference | CBRNets: Regularizing neural networks to learn continuously-valued treatment effects from observational data | Christopher Bockel-Rickermann | C.103 | Stefan Lessmann |
| 14:00 | 14:20 | Contributed Sessions - Robust statistics | On the comparison of unsupervised anomaly detection algorithms | Stefanie Schwaar | C.002 | Peter Rousseeuw |
| 14:20 | 14:40 | Contributed Sessions - Robust statistics | MacroPARAFAC for a cellwise and rowwise robust PARAFAC analysis | Mia Hubert | C.002 | Peter Rousseeuw |
| 14:40 | 15:00 | Contributed Sessions - Robust statistics | Functional Outlier Detection based on the Minimum Regularized Covariance Trace Estimator | Jeremy Oguamalam | C.002 | Peter Rousseeuw |
| 15:00 | 15:20 | Contributed Sessions - Robust statistics | Robust and sparse logistic regression | Lise Tubex | C.002 | Peter Rousseeuw |
| 15:20 | 15:40 | Contributed Sessions - Robust statistics | L0 Regularized Cellwise Outlier Detection and Covariance Estimation | Marcus Mayrhofer | C.002 | Peter Rousseeuw |
| 15:40 | 16:10 | Coffee-Break | Coffee-Break | - | - | - |
| 16:10 | 16:35 | Invited Sessions - New Statistical Approaches in Economics and the Natural Sciences | Bayesian inference for functional extreme events defined via partially unobserved processes | Marco Oesting | C.003 | Johannes Lederer |
| 16:35 | 17:00 | Invited Sessions - New Statistical Approaches in Economics and the Natural Sciences | A geometric approach to convergence rates for graphical models based on a single observation of discrete and dependent network and attribute data | A. Michael Schweinberger | C.003 | Johannes Lederer |
| 17:00 | 17:25 | Invited Sessions - New Statistical Approaches in Economics and the Natural Sciences | Temporal Ordering and Manifold Recovery on Noisy Data | Wanjie Wang | C.003 | Johannes Lederer |
| 17:25 | 17:50 | Invited Sessions - New Statistical Approaches in Economics and the Natural Sciences | Influencer Detection Between Sectors Via Sparse Network Analysis | Simon Trimborn | C.003 | Johannes Lederer |
| 16:10 | 16:35 | Invited Sessions - Multidimensional Data Visualization | Visualising interpretability of random forest models | Peter Manefeldt | C.002 | Sugnet Lubbe and Niël le Roux |
| 16:35 | 17:00 | Invited Sessions - Multidimensional Data Visualization | The effect of data aggregation on ordinary least-squares estimation and model selection procedures | Pieter Schoonees | C.002 | Sugnet Lubbe and Niël le Roux |
| 17:00 | 17:25 | Invited Sessions - Multidimensional Data Visualization | Predictive biplots for individual differences scaling (INDSCAL) models | Niel Le Roux | C.002 | Sugnet Lubbe and Niël le Roux |
| 17:25 | 17:50 | Invited Sessions - Multidimensional Data Visualization | Open-Set Recognition with Second-Order Extreme Value Theory | Matthys Lucas Steyn | C.002 | Sugnet Lubbe and Niël le Roux |
| 19:00 | 20:00 | Reception | Reception | - | Horta Art Nouveau restaurant | - |
| 20:00 | 23:00 | Congress Dinner | Congress Dinner | - | Horta Art Nouveau restaurant | - |