| 8:30 | 8:55 | Invited Sessions - Reinforcement Learning from Human Feedback | Reinforcement learning from human feedback and AI safety | Dmitrii Krasheninnikov | C.003 | Timo Kaufmann |
| 8:55 | 9:20 | Invited Sessions - Reinforcement Learning from Human Feedback | Learning from Human Feedback for Fine-tuning Text-to-Image Models | Kimin Lee | C.003 | Timo Kaufmann |
| 9:20 | 9:45 | Invited Sessions - Reinforcement Learning from Human Feedback | Reward Trees for Interpretable Reinforcement Learning from Human Feedback | Tom Bewley | C.003 | Timo Kaufmann |
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| 8:30 | 8:55 | Invited Sessions - Unsupervised learning for mixed data | Convex clustering of mixed numerical and categorical data | Carlo Cavicchia | C.002 | Alfonso Iodice D'Enza |
| 8:55 | 9:20 | Invited Sessions - Unsupervised learning for mixed data | Spectral clustering on association-based distances for mixed data | Francesco Palumbo | C.002 | Alfonso Iodice D'Enza |
| 9:20 | 9:45 | Invited Sessions - Unsupervised learning for mixed data | Dendrogram slicing through a permutation test approach for mixed data | Lucio Palazzo | C.002 | Alfonso Iodice D'Enza |
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| 9:45 | 10:05 | Contributed Sessions -Decision Trees & Random Forests
| Random Forest Calibration | Mohammad Hossein Shaker | C.002 | Jakob.Raymaekers |
| 10:05 | 10:25 | Contributed Sessions -Decision Trees & Random Forests
| Fast Linear Model Trees by PILOT | Ruicong Yao | C.002 | Jakob.Raymaekers |
| 10:25 | 10:45 | Contributed Sessions -Decision Trees & Random Forests
| Optimization of Decision Trees under Restrictions: The PrInDT package | Claus Weihs | C.002 | Jakob.Raymaekers |
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| 9:45 | 10:05 | Contributed Sessions -Timeseries and matching
| A New Look at Model Averaging of Differently Sized Time Series | Lukas Neubauer | C.101 | Christophe Croux |
| 10:05 | 10:25 | Contributed Sessions -Timeseries and matching
| Dynamic Connectedness in Returns and Volatility among Clean and Conventional Energy ETFs | Katarzyna Kuziak | C.101 | Christophe Croux |
| 10:25 | 10:45 | Contributed Sessions -Timeseries and matching
| Hausdorff Distance: A Powerful Tool for Matching Households and Individuals in Different Databases | Thais Pacheco Menezes | C.101 | Christophe Croux |
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| 9:45 | 10:05 | Contributed Sessions -Biomedical applications
| Evaluation of network-guided random forest for disease gene discovery | Jianchang Hu | C.103 | Marta Lopes |
| 10:05 | 10:25 | Contributed Sessions -Biomedical applications
| Evaluating the impact of inclusion of molecular information in glioma classification on network-based biomarker discovery | Roberta Coletti | C.103 | Marta Lopes |
| 10:25 | 10:45 | Contributed Sessions -Biomedical applications
| EEG-Based Attention Detection through Machine Learning | Steven Mortier | C.103 | Marta Lopes |
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| 9:45 | 10:05 | Contributed Sessions -Visualisation
| Improving the Ternary Diagrams for Compositions | Paul Eilers | C.102 | José Antonio Oramas Mogrovejo |
| 10:05 | 10:25 | Contributed Sessions -Visualisation
| New tour methods for visualizing high-dimensional data | Ursula Laa | C.102 | José Antonio Oramas Mogrovejo |
| 10:25 | 10:45 | Contributed Sessions -Visualisation
| Computing Sensitive Color Transitions for the Identification of Two-Dimensional Structures | Quirin Stier | C.102 | José Antonio Oramas Mogrovejo |
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| 10:45 | 11:15 | Coffee-Break | Coffee-Break | - | Hall | - |
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| 11:15 | 11:40 | Invited Sessions - Dimension reduction and cluster analysis | Clusterwise Joint Independent Component Analysis: discovering disease related subtypes using multi-modal neuroimaging data | Jeffrey Durieux | C.003 | Michel van de Velden |
| 11:40 | 12:05 | Invited Sessions - Dimension reduction and cluster analysis | Interpretable Kernels | Patrick J.F. Groenen | C.003 | Michel van de Velden |
| 12:05 | 12:30 | Invited Sessions - Dimension reduction and cluster analysis | Methods to generate contingency tables satisfying user-specified properties | Michel van de Velden | C.003 | Michel van de Velden |
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| 11:15 | 11:40 | Invited Sessions - Prediction and Causality in Biomedical Research | Modeling multiple type count data with dependent observation times: An application to skin cancer prevention study | Chia-Hui Huang | C.002 | Sheng-Mao Chang |
| 11:40 | 12:05 | Invited Sessions - Prediction and Causality in Biomedical Research | A Comparative Study of Deep Learning-Based Predictive Methods for Remaining Useful Life | Hsiang-Ling Hsu | C.002 | Sheng-Mao Chang |
| 12:05 | 12:30 | Invited Sessions - Prediction and Causality in Biomedical Research | Modeling Multiple-Criterion Diagnoses by Heterogeneous-Instance Logistic Regression | Sheng-Mao Chang | C.002 | Sheng-Mao Chang |
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| 11:15 | 11:40 | Invited Sessions - Complex Data Structures in Social Network Analysis | Unveiling inter-firms relationships in Industrial Districts | Marialuisa Restaino | C.103 | Giuseppe Giordano |
| 11:40 | 12:05 | Invited Sessions - Complex Data Structures in Social Network Analysis | A strategy for simplyfying multimode weighted network data structure | G. Ragozini | C.103 | Giuseppe Giordano |
| 12:05 | 12:30 | Invited Sessions - Complex Data Structures in Social Network Analysis | Network changes in the Italian Container terminal industry: a stochastic actor oriented model | S.A. Mohseni | C.103 | Giuseppe Giordano |
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| 12:30 | 14:00 | Lunch | Lunch | - | - | - |
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| 14:00 | 14:20 | Invited Sessions - Advances in Causal Machine Learning | Causality and healthcare process optimization | Sam Verboven | C.003 | Wouter Verbeke |
| 14:20 | 14:40 | Invited Sessions - Advances in Causal Machine Learning | NOFLITE: Learning to Predict Individual Treatment Effect Distributions | Toon Vanderschueren | C.003 | Wouter Verbeke |
| 14:40 | 15:00 | Invited Sessions - Advances in Causal Machine Learning | TSLiNGAM: DirectLiNGAM under heavy tails | Sarah Leyder | C.003 | Wouter Verbeke |
| 15:00 | 15:20 | Invited Sessions - Advances in Causal Machine Learning | Which variables would you impute in treatment effects? | Jeroen Berrevoets | C.003 | Wouter Verbeke |
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| 14:00 | 14:20 | Invited Sessions - Choice-Based Analytics | Crowd Management for the FIFA WC 2022 in Doha: survey-based traffic analysis and forecasting using machine learning methods | Simon Rienks | C.002 | Friederike Paetz |
| 14:20 | 14:40 | Invited Sessions - Choice-Based Analytics | Dynamic pricing for upselling based on customer choice behavior | Davina Hartmann | C.002 | Friederike Paetz |
| 14:40 | 15:00 | Invited Sessions - Choice-Based Analytics | A comparison of alternative-specific and main-effects conjoint choice models | Anastasia Mirow | C.002 | Friederike Paetz |
| 15:00 | 15:20 | Invited Sessions - Choice-Based Analytics | The choice for in-home AI devices: A cohort-specific perspective | Friederike Paetz | C.002 | Friederike Paetz |
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| 15:20 | 16:20 | Keynote Day 3 | Time: The next frontier in machine learning | Mihaela van der Schaar | C.003 | Tim Verdonck |
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| 16:20 | 16:30 | DAC award ceremony & Closing | DAC award ceremony & Closing | - | C.003 | - |
| 16:15 | 17:00 | Coffee Reception | Coffee Reception | - | - | - |
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