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Knowledge Discovery, Machine Learning, and Social Mining

Knowledge Discovery, Machine Learning, and Social Mining. Klausurtagung SFB-"Früherkennung von Stress und Stresstoleranz", Bonn, July 23, 2010. Stefan Wrobel. Christian Bauckhage. Thomas Gärtner. Kristian Kersting. @University of Bonn. Basic Research Groups: ~ 25 people

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Knowledge Discovery, Machine Learning, and Social Mining

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  1. Knowledge Discovery, Machine Learning, and Social Mining Klausurtagung SFB-"Früherkennung von Stress und Stresstoleranz", Bonn, July 23, 2010 Stefan Wrobel Christian Bauckhage Thomas Gärtner Kristian Kersting

  2. @University of Bonn • Basic Research Groups: ~ 25 people • Focus on Machine Learning, Data Mining (, and AI) • Editorial Boards: MLJ, JMLR, JAIR, … • PC Chairs: ICML, SRL, MLG, StarAI, … • Regularly PC membersofmajor AI, ML, and DM conferences • ICML, IJCAI, ECML-PKDD, AAAI, RSS, … • International Tutorials: ICML, ECML-PKDD, AAAI, ICAPS, … • Awards: ECML, ICDM, MLG, ECAI Dissertation Award, ATTRACT Fellowship, … • International Collabs: MIT, Cornell, Toronto, Google, … • Institute Directorof Fraunhofer IAIS

  3. @Fraunhofer - IAIS • Explores and develops innovative systems to analyze data and to make information available • 260 people: scientists, project engineers, technical and administrative staff • Concentrates the competences and scientific qualities of all engineering disciplines especially informatics, and mathematics, natural sciences, business economics, geo and social sciences • Profound industry expertise • Joint research groups and cooperations with the University of Bonn

  4. „Stress und Stresstoleranz“ 4 Years: Methods/models at individual levels Visual Analytics Social Network Analysis Frequent Itemsets Subgroup Discovery Graphical Models Kernel Methods Experimental Design Gaussian Processes Web-Scale Matrix Factorization

  5. Example: Hyperspectral Images Matrices withmillions/ billionsofentries

  6. 8-12 Years Mission: Stress is complex and uncertain Natural domain modeling: objects, properties, relations Compact, natural models Properties of entities can depend on properties of related entities Generalization over a variety of situations Let‘s deal with uncertainty and structure (objects and relations)jointly … SAT Planning Probability Statistics Search Learning CV Logic Graphs Trees Robotics Ongoing “revolution” within AI, ML, and DM

  7. Information Extraction Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

  8. Information Extraction Paper Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

  9. Author Segmentation Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

  10. Author Entity Resolution Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

  11. Author Entity Resolution Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. w1 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameAuthor(a1,a2) => SameBib(bc1,bc2) w2 : Title(bc1,t1) ^ Title(bc2,t2) ^ SameTitle(t1,t2) => SameBib(bc1,bc2) w3 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameVenue(v1,v2) => SameBib(bc1,bc2) w4 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameBib(bc1,bc2) => SameAuthor(a1,a2) w5 : itle(bc1,t1) ^ Title(bc2,t2) ^ SameBib(bc1,bc2) => SameTitle(t1,t2) w6 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameBib(bc1,bc2) => SameVenue(v1,v2)

  12. Author “Stress und Stresstoleranz“ Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains”(AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. w1 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameAuthor(a1,a2) => SameBib(bc1,bc2) w2 : Title(bc1,t1) ^ Title(bc2,t2) ^ SameTitle(t1,t2) => SameBib(bc1,bc2) w3 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameVenue(v1,v2) => SameBib(bc1,bc2) w4 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameBib(bc1,bc2) => SameAuthor(a1,a2) w5 : itle(bc1,t1) ^ Title(bc2,t2) ^ SameBib(bc1,bc2) => SameTitle(t1,t2) w6 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameBib(bc1,bc2) => SameVenue(v1,v2) • 8-12 Years Vision: Knowledge-rich, holisticstatisticalmodels/methodsacross different levels

  13. Conclusions • Basic Research + Industry Structure (Objects & Relations, Graphs, Tress) +Probabilities / Kernels + (Massive) Machine Learning and Data Mining • 4 Years Goal: Models at different levels • 8-12 Years Vision: Joint modelsacrosslevels • Liftedinference, Dynamic Models, Continuousvalues, Massive Data Analysis Tools Thanks for your attention

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