08:45-09:00: Welcome (Eugene C. Freuder and Barry O'Sullivan)
09:00-10:30: Modeling
- Learning constraint models from data (20 minutes)
- Dimosthenis C. Tsouros, Tias Guns and Kostas Stergiou - Constraint Acquisition and Machine Learning (20 minutes)
- Steven Prestwich - Requirements for Practical Constraint Acquisition (20 minutes)
- Helmut Simonis - Discussion (30 minutes)
10:30-11:00: Break
11:00-12:30: Solving
- Machine Learning and Constraint Solving: An Overview. (20 minutes)
- Alexander Felfernig - Unlocking the power of modern ML for use in CSP (minus predict-then-optimize) (20 minutes)
- Chris Cameron, Jason Hartford, Taylor Lundy and Kevin Leyton-Brown - Towards Machine-Generated Algorithms (20 minutes)
- Lars Kotthoff - Discussion (30 minutes)
12:30-14:00: Lunch
14:00-15:15: Integration
- Neuro-Symbolic AI Systems Combining Neural Networks and Marginals-Augmented Constraint Programming (10 minutes)
- Gilles Pesant - Learning a Constrained Optimizer: A Primal Method (10 minutes)
- Tao Liu and Anoop Cherian - Bridging Constraint-based Sequential Pattern Mining and Machine Learning(10 minutes)
- Xin Wang and Serdar Kadioglu - Embedding Constraint Reasoning in Machine Learning for Structured Prediction and Content Generation (20 minutes)
- Yexiang Xue, Maxwell Jacobson, Nan Jiang, Maosen Zhang and Willem-Jan van Hoeve - Discussion (25 minutes)
15:15-15:45: Break
15:45-16:30: Predict/Optimize
- Constraint solving for Prediction + Optimisation problems (10 minutes)
- Maxime Mulamba Ke Tchomba, Emilio Gamba and Tias Guns - Learning to Reason: Predict-then-Optimize vs Predict-and-Optimize (10 minutes)
- Marianne Defresne, Thomas Schiex and Sophie Barbe - Unlocking the power of modern ML for use in CSP (predict-then-optimize section) (10 minutes)
- Chris Cameron, Jason Hartford, Taylor Lundy and Kevin Leyton-Brown - Discussion (15 minutes)
16:30-17:15: Fairness and Explanation
- Improving Fairness Generalization Through a Sample-Robust Optimization Method (10 minutes)
- Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet and Mohamed Siala - Fairness in Clustering and Outlier Detection as Constraint Satisfaction (10 minutes)
- Ian Davidson and S. S. Ravi - Explainable Unsupervised Learning as Constraint Satisfaction (10 minutes)
- Ian Davidson and S.S. Ravi - Discussion (15 minutes)
17:15-17:45: Tools
- Seq2pat (10 minutes)
- MiniCPBP (10 minutes)
- CPMpy (10 minutes) -
