Discovery Science: 29th International Conference, DS 2026, Mainz, Germany, October 59, Proceedings: 17130

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Bol The DS 2026 constitutes the proceedings of the 29th International Conference on Discovery Science, DS 2026, which took place in Mainz, Germany, during October 5-9 2026. The 52 full papers presented in this volume were carefully reviewed and selected from 113 submissions. Machine Learning: Representation Learning; The DS 2026 constitutes the proceedings of the 29th International Conference on Discovery Science, DS 2026, which took place in Mainz, Germany, during October 5-9 2026. The 52 full papers presented in this volume were carefully reviewed and selected from 113 submissions. They were organized in topical sections as follows: Explainable AI; Hypothesis Generation and Causal Discovery; Language Models and Automated Scientific Discovery; Retrieval-Augmented Generation, Agentic AI; Physics-Informed Machine Learning; Computational Equation Discovery and Symbolic Regression; Benchmarking Methodologies; Continual; Transfer and Meta-Learning; Machine Learning: Representation Learning; Clustering and Model Design and Machine Learning Applications.

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The DS 2026 constitutes the proceedings of the 29th International Conference on Discovery Science, DS 2026, which took place in Mainz, Germany, during October 5-9 2026. The 52 full papers presented in this volume were carefully reviewed and selected from 113 submissions. Machine Learning: Representation Learning; The DS 2026 constitutes the proceedings of the 29th International Conference on Discovery Science, DS 2026, which took place in Mainz, Germany, during October 5-9 2026. The 52 full papers presented in this volume were carefully reviewed and selected from 113 submissions. They were organized in topical sections as follows: Explainable AI; Hypothesis Generation and Causal Discovery; Language Models and Automated Scientific Discovery; Retrieval-Augmented Generation, Agentic AI; Physics-Informed Machine Learning; Computational Equation Discovery and Symbolic Regression; Benchmarking Methodologies; Continual; Transfer and Meta-Learning; Machine Learning: Representation Learning; Clustering and Model Design and Machine Learning Applications.


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