All regular talks are 15 minutes + 5 minutes Q&A.
09:00 - 09:10: Opening
09:10-10:30 Session 1
Formulations and Implementations, by Ali Irfan Mahmutogulları, Jayanta Mandi, Tias Guns
Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent, by Benjamin Leblanc, Louis-Jacob Lebel
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework, by Chengpeng Hu, Hendrik Baier, Yingqian Zhang
DROVER: Diffusion Routing with Verbal Reasoning, by Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
10:30-11:00 Coffee Break
11:00 - 11:45 Keynote 1: Understanding the Algorithmic Bias of Graph Neural Networks, by Christopher Morris (Aachen University, Germany)
11:45-12:25 Session 2:
Supervised Learning of Model Aware Variable Orderings in CP, by Georg Ringwelski, Julian Kirsch, Benedict Lelanz
Deep Reinforcement Learning Approach to Solving Clustered Vehicle Routing Problems, by Yue Yu, Yaoxin Wu
12:25 - 14:00 Lunch
14:00 - 14:45 Keynote 2: Automated Algorithm Design with Large Language Model, by Fei Liu (ETH Zurich, Switzerland)
14:45 - 15:25 Session 3:
Manifold Sampling via Entropy Maximization, by Cornelius V. Braun, Tilman Burghoff, Marc Toussaint
Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations, by Henry Xiao, Tong Li, Devika Prasad, Luke Gerschwitz, Anjin Liu, Coco Wu, Anna Leontjeva
15:25 - 16:00 Coffee Break
16:00 - 17:20 Session 4:
Scalable Decision-Focused Learning through Cost-Sensitive Regression, by Noah Schutte, Senne Berden, Tias Guns, Neil Yorke-Smith, Krzysztof Postek
Optimizing Emergency Blood Distribution: A Novel Multiplex Network Modeling Framework, by Suman Nandi, Giridhar Maji, Animesh Dutta
Learning-Guided State Partitioning for Decision Diagram Relaxations in Two-Stage Robust Optimization, by Michael Römer, Merve Bodur
Forge: Foundational Optimization Representations from Graph Embeddings, by Zohair Shafi, Serdar Kadioglu