Degree
thesis:*- Grantor dc:publisher
- Kyoto University
- Year dc:date.issued
- 2022
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Okawa, Maya
Subjects
dc:subject × 5Rights
dc:rights- Statement dc:rights
-
- ・Deep mixture point processes: Spatio-temporal event prediction with rich contextual information. In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 373–383. ACM, 2019. https://doi.org/10.1145/3292500.3330937 ・Context-aware spatio-temporal event prediction via convolutional hawkes processes. Machine Learning Journal (Special Issue of ECML PKDD), 107(8-10):1283–1302, 2021. doi:10.1007/s10994-022-06136-5 ・Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 1276–1286. ACM, 2021.https://doi.org/10.1145/3447548.3467248
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/2433/275350
- OAI identifier oai:identifier
- oai:repository.kulib.kyoto-u.ac.jp:2433/275350