{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T06:34:18Z","timestamp":1779345258984,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Nature Science Foundation of China","award":["U22B2057 61971267 U1936217"],"award-info":[{"award-number":["U22B2057 61971267 U1936217"]}]},{"name":"BNRist","award":["BNRist"],"award-info":[{"award-number":["BNRist"]}]},{"name":"the National Key Research and Development Program of China","award":["2020YFA0711403"],"award-info":[{"award-number":["2020YFA0711403"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599511","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"3173-3184","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["Spatio-temporal Diffusion Point Processes"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1701-2588","authenticated-orcid":false,"given":"Yuan","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7985-6263","authenticated-orcid":false,"given":"Jingtao","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5673-9978","authenticated-orcid":false,"given":"Chenyang","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-5514","authenticated-orcid":false,"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"17981","article-title":"Structured denoising diffusion models in discrete state-spaces","volume":"34","author":"Austin Jacob","year":"2021","unstructured":"Jacob Austin , Daniel D Johnson , Jonathan Ho , Daniel Tarlow , and Rianne van den Berg . 2021 . Structured denoising diffusion models in discrete state-spaces . Advances in Neural Information Processing Systems , Vol. 34 (2021), 17981 -- 17993 . Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. 2021. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, Vol. 34 (2021), 17981--17993.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_2_1","volume-title":"Italy, September 13--18","author":"Baddeley Adrian","year":"2004","unstructured":"Adrian Baddeley , Imre B\u00e1r\u00e1ny , and Rolf Schneider . 2007. Spatial point processes and their applications. Stochastic Geometry: Lectures Given at the CIME Summer School Held in Martina Franca , Italy, September 13--18 , 2004 (2007), 1--75. Adrian Baddeley, Imre B\u00e1r\u00e1ny, and Rolf Schneider. 2007. Spatial point processes and their applications. Stochastic Geometry: Lectures Given at the CIME Summer School Held in Martina Franca, Italy, September 13--18, 2004 (2007), 1--75."},{"key":"e_1_3_2_2_3_1","unstructured":"Ricky TQ Chen Brandon Amos and Maximilian Nickel. 2021. Neural spatio-temporal point processes. (2021). Ricky TQ Chen Brandon Amos and Maximilian Nickel. 2021. Neural spatio-temporal point processes. (2021)."},{"key":"e_1_3_2_2_4_1","volume-title":"Neural ordinary differential equations. Advances in neural information processing systems","author":"Chen Ricky TQ","year":"2018","unstructured":"Ricky TQ Chen , Yulia Rubanova , Jesse Bettencourt , and David K Duvenaud . 2018. Neural ordinary differential equations. Advances in neural information processing systems , Vol. 31 ( 2018 ). Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural ordinary differential equations. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_5_1","unstructured":"Daryl J Daley David Vere-Jones etal 2003. An introduction to the theory of point processes: volume I: elementary theory and methods. Springer. Daryl J Daley David Vere-Jones et al. 2003. An introduction to the theory of point processes: volume I: elementary theory and methods. Springer."},{"key":"e_1_3_2_2_6_1","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol . 2021 . Diffusion models beat gans on image synthesis . Advances in Neural Information Processing Systems , Vol. 34 (2021), 8780 -- 8794 . Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, Vol. 34 (2021), 8780--8794.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_7_1","first-page":"1","article-title":"Spatio-temporal point processes: methods and applications","volume":"107","author":"Diggle Peter J","year":"2006","unstructured":"Peter J Diggle . 2006 . Spatio-temporal point processes: methods and applications . Monographs on Statistics and Applied Probability , Vol. 107 (2006), 1 . Peter J Diggle. 2006. Spatio-temporal point processes: methods and applications. Monographs on Statistics and Applied Probability, Vol. 107 (2006), 1.","journal-title":"Monographs on Statistics and Applied Probability"},{"key":"e_1_3_2_2_8_1","volume-title":"Wasserstein generative adversarial networks for modeling marked events. The Journal of Supercomputing","author":"Dizaji S Haleh S","year":"2022","unstructured":"S Haleh S Dizaji , Saeid Pashazadeh , and Javad Musevi Niya . 2022. Wasserstein generative adversarial networks for modeling marked events. The Journal of Supercomputing ( 2022 ), 1--23. S Haleh S Dizaji, Saeid Pashazadeh, and Javad Musevi Niya. 2022. Wasserstein generative adversarial networks for modeling marked events. The Journal of Supercomputing (2022), 1--23."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"e_1_3_2_2_10_1","volume-title":"Difformer: Empowering Diffusion Model on Embedding Space for Text Generation. arXiv preprint arXiv:2212.09412","author":"Gao Zhujin","year":"2022","unstructured":"Zhujin Gao , Junliang Guo , Xu Tan , Yongxin Zhu , Fang Zhang , Jiang Bian , and Linli Xu . 2022 . Difformer: Empowering Diffusion Model on Embedding Space for Text Generation. arXiv preprint arXiv:2212.09412 (2022). Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, and Linli Xu. 2022. Difformer: Empowering Diffusion Model on Embedding Space for Text Generation. arXiv preprint arXiv:2212.09412 (2022)."},{"key":"e_1_3_2_2_11_1","volume-title":"International Conference on Machine Learning. PMLR, 7616--7633","author":"Goel Karan","year":"2022","unstructured":"Karan Goel , Albert Gu , Chris Donahue , and Christopher R\u00e9 . 2022 . It's raw! audio generation with state-space models . In International Conference on Machine Learning. PMLR, 7616--7633 . Karan Goel, Albert Gu, Chris Donahue, and Christopher R\u00e9. 2022. It's raw! audio generation with state-space models. In International Conference on Machine Learning. PMLR, 7616--7633."},{"key":"e_1_3_2_2_12_1","volume-title":"Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933","author":"Gong Shansan","year":"2022","unstructured":"Shansan Gong , Mukai Li , Jiangtao Feng , Zhiyong Wu , and LingPeng Kong . 2022 . Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933 (2022). Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. 2022. Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933 (2022)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2016.10.002"},{"key":"e_1_3_2_2_14_1","volume-title":"Aspects of risk theory","author":"Grandell Jan","unstructured":"Jan Grandell . 2012. Aspects of risk theory . Springer Science & Business Media . Jan Grandell. 2012. Aspects of risk theory. Springer Science & Business Media."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01660"},{"key":"e_1_3_2_2_16_1","volume-title":"INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process.. In IJCAI. 2191--2197.","author":"Guo Ruocheng","year":"2018","unstructured":"Ruocheng Guo , Jundong Li , and Huan Liu . 2018 . INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process.. In IJCAI. 2191--2197. Ruocheng Guo, Jundong Li, and Huan Liu. 2018. INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process.. In IJCAI. 2191--2197."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1971.tb01530.x"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2017.1403131"},{"key":"e_1_3_2_2_19_1","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho , Ajay Jain , and Pieter Abbeel . 2020 . Denoising diffusion probabilistic models . Advances in Neural Information Processing Systems , Vol. 33 (2020), 6840 -- 6851 . Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6840--6851.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_20_1","volume-title":"Video diffusion models. arXiv preprint arXiv:2204.03458","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho , Tim Salimans , Alexey Gritsenko , William Chan , Mohammad Norouzi , and David J Fleet . 2022. Video diffusion models. arXiv preprint arXiv:2204.03458 ( 2022 ). Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. 2022. Video diffusion models. arXiv preprint arXiv:2204.03458 (2022)."},{"key":"e_1_3_2_2_21_1","volume-title":"A self-correcting point process. Stochastic processes and their applications","author":"Isham Valerie","year":"1979","unstructured":"Valerie Isham and Mark Westcott . 1979. A self-correcting point process. Stochastic processes and their applications , Vol. 8 , 3 ( 1979 ), 335--347. Valerie Isham and Mark Westcott. 1979. A self-correcting point process. Stochastic processes and their applications, Vol. 8, 3 (1979), 335--347."},{"key":"e_1_3_2_2_22_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Jia Junteng","year":"2019","unstructured":"Junteng Jia and Austin R Benson . 2019 . Neural jump stochastic differential equations . Advances in Neural Information Processing Systems , Vol. 32 (2019). Junteng Jia and Austin R Benson. 2019. Neural jump stochastic differential equations. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_23_1","volume-title":"Poisson processes","author":"Charles Kingman John Frank","unstructured":"John Frank Charles Kingman . 1992. Poisson processes . Vol. 3 . Clarendon Press . John Frank Charles Kingman. 1992. Poisson processes. Vol. 3. Clarendon Press."},{"key":"e_1_3_2_2_24_1","volume-title":"Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761","author":"Kong Zhifeng","year":"2020","unstructured":"Zhifeng Kong , Wei Ping , Jiaji Huang , Kexin Zhao , and Bryan Catanzaro . 2020 . Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020). Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2020. Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Fuxian Li Huan Yan Guangyin Jin Yue Liu Yong Li and Depeng Jin. 2022. Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction. In CIKM. 1084?1093. Fuxian Li Huan Yan Guangyin Jin Yue Liu Yong Li and Depeng Jin. 2022. Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction. In CIKM. 1084?1093.","DOI":"10.1145\/3511808.3557243"},{"key":"e_1_3_2_2_26_1","volume-title":"Learning temporal point processes via reinforcement learning. Advances in neural information processing systems","author":"Li Shuang","year":"2018","unstructured":"Shuang Li , Shuai Xiao , Shixiang Zhu , Nan Du , Yao Xie , and Le Song . 2018. Learning temporal point processes via reinforcement learning. Advances in neural information processing systems , Vol. 31 ( 2018 ). Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, and Le Song. 2018. Learning temporal point processes via reinforcement learning. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_27_1","volume-title":"Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217","author":"Li Xiang Lisa","year":"2022","unstructured":"Xiang Lisa Li , John Thickstun , Ishaan Gulrajani , Percy Liang , and Tatsunori B Hashimoto . 2022. Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217 ( 2022 ). Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto. 2022. Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217 (2022)."},{"key":"e_1_3_2_2_28_1","volume-title":"Exploring Generative Neural Temporal Point Process. Transactions on Machine Learning Research","author":"Lin Haitao","year":"2022","unstructured":"Haitao Lin , Lirong Wu , Guojiang Zhao , Pai Liu , and Stan Z Li. 2022. Exploring Generative Neural Temporal Point Process. Transactions on Machine Learning Research ( 2022 ). Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, and Stan Z Li. 2022. Exploring Generative Neural Temporal Point Process. Transactions on Machine Learning Research (2022)."},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.","author":"Long Qingyue","year":"2023","unstructured":"Qingyue Long , Huandong Wang , Tong Li , Lisi Huang , Kun Wang , Qiong Wu , Guangyu Li , Yanping Liang , Li Yu , and Yong Li . 2023 . Practical Synthetic Human Trajectories Generation Based on Variational Point Processes . In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Qingyue Long, Huandong Wang, Tong Li, Lisi Huang, Kun Wang, Qiong Wu, Guangyu Li, Yanping Liang, Li Yu, and Yong Li. 2023. Practical Synthetic Human Trajectories Generation Based on Variational Point Processes. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00328"},{"key":"e_1_3_2_2_32_1","volume-title":"The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems","author":"Mei Hongyuan","year":"2017","unstructured":"Hongyuan Mei and Jason M Eisner . 2017. The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems , Vol. 30 ( 2017 ). Hongyuan Mei and Jason M Eisner. 2017. The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_33_1","volume-title":"Noise-contrastive estimation for multivariate point processes. Advances in neural information processing systems","author":"Mei Hongyuan","year":"2020","unstructured":"Hongyuan Mei , Tom Wan , and Jason Eisner . 2020. Noise-contrastive estimation for multivariate point processes. Advances in neural information processing systems , Vol. 33 ( 2020 ), 5204--5214. Hongyuan Mei, Tom Wan, and Jason Eisner. 2020. Noise-contrastive estimation for multivariate point processes. Advances in neural information processing systems, Vol. 33 (2020), 5204--5214."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01684.x"},{"key":"e_1_3_2_2_35_1","volume-title":"Statistical inference and simulation for spatial point processes","author":"Moller Jesper","unstructured":"Jesper Moller and Rasmus Plenge Waagepetersen . 2003. Statistical inference and simulation for spatial point processes . CRC press . Jesper Moller and Rasmus Plenge Waagepetersen. 2003. Statistical inference and simulation for spatial point processes. CRC press."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1988.10478560"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330937"},{"key":"e_1_3_2_2_38_1","unstructured":"Takahiro Omi Kazuyuki Aihara etal 2019. Fully neural network based model for general temporal point processes. Advances in neural information processing systems Vol. 32 (2019). Takahiro Omi Kazuyuki Aihara et al. 2019. Fully neural network based model for general temporal point processes. Advances in neural information processing systems Vol. 32 (2019)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5348"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1080\/02664763.2020.1825646"},{"key":"e_1_3_2_2_41_1","volume-title":"Yee Mun Lee, Ruth Madigan, Tatsuru Daimon, Natasha Merat, and Gustav Markkula.","author":"Pekkanen Jami","year":"2021","unstructured":"Jami Pekkanen , Oscar Terence Giles , Yee Mun Lee, Ruth Madigan, Tatsuru Daimon, Natasha Merat, and Gustav Markkula. 2021 . Variable-drift diffusion models of pedestrian road-crossing decisions. Computational Brain & Behavior ( 2021), 1--21. Jami Pekkanen, Oscar Terence Giles, Yee Mun Lee, Ruth Madigan, Tatsuru Daimon, Natasha Merat, and Gustav Markkula. 2021. Variable-drift diffusion models of pedestrian road-crossing decisions. Computational Brain & Behavior (2021), 1--21."},{"key":"e_1_3_2_2_42_1","volume-title":"Structural reliability under combined random load sequences. Computers & structures","author":"Rackwitz R\u00fcdiger","year":"1978","unstructured":"R\u00fcdiger Rackwitz and Bernd Flessler . 1978. Structural reliability under combined random load sequences. Computers & structures , Vol. 9 , 5 ( 1978 ), 489--494. R\u00fcdiger Rackwitz and Bernd Flessler. 1978. Structural reliability under combined random load sequences. Computers & structures, Vol. 9, 5 (1978), 489--494."},{"key":"e_1_3_2_2_43_1","volume-title":"Lecture notes: Temporal point processes and the conditional intensity function. arXiv preprint arXiv:1806.00221","author":"Rasmussen Jakob Gulddahl","year":"2018","unstructured":"Jakob Gulddahl Rasmussen . 2018. Lecture notes: Temporal point processes and the conditional intensity function. arXiv preprint arXiv:1806.00221 ( 2018 ). Jakob Gulddahl Rasmussen. 2018. Lecture notes: Temporal point processes and the conditional intensity function. arXiv preprint arXiv:1806.00221 (2018)."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Machine Learning. PMLR, 8857--8868","author":"Rasul Kashif","year":"2021","unstructured":"Kashif Rasul , Calvin Seward , Ingmar Schuster , and Roland Vollgraf . 2021 . Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting . In International Conference on Machine Learning. PMLR, 8857--8868 . Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. 2021. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning. PMLR, 8857--8868."},{"key":"e_1_3_2_2_45_1","first-page":"299","article-title":"A review of self-exciting spatio-temporal point processes and their applications","volume":"33","author":"Reinhart Alex","year":"2018","unstructured":"Alex Reinhart . 2018 . A review of self-exciting spatio-temporal point processes and their applications . Statist. Sci. , Vol. 33 , 3 (2018), 299 -- 318 . Alex Reinhart. 2018. A review of self-exciting spatio-temporal point processes and their applications. Statist. Sci., Vol. 33, 3 (2018), 299--318.","journal-title":"Statist. Sci."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_2_47_1","unstructured":"Oleksandr Shchur Marin Bilovs and Stephan G\u00fcnnemann. 2019. Intensity-free learning of temporal point processes. (2019). Oleksandr Shchur Marin Bilovs and Stephan G\u00fcnnemann. 2019. Intensity-free learning of temporal point processes. (2019)."},{"key":"e_1_3_2_2_48_1","first-page":"12533","article-title":"D2c: Diffusion-decoding models for few-shot conditional generation","volume":"34","author":"Sinha Abhishek","year":"2021","unstructured":"Abhishek Sinha , Jiaming Song , Chenlin Meng , and Stefano Ermon . 2021 . D2c: Diffusion-decoding models for few-shot conditional generation . Advances in Neural Information Processing Systems , Vol. 34 (2021), 12533 -- 12548 . Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021. D2c: Diffusion-decoding models for few-shot conditional generation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 12533--12548.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_49_1","volume-title":"International Conference on Machine Learning. PMLR, 2256--2265","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein , Eric Weiss , Niru Maheswaranathan , and Surya Ganguli . 2015 . Deep unsupervised learning using nonequilibrium thermodynamics . In International Conference on Machine Learning. PMLR, 2256--2265 . Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning. PMLR, 2256--2265."},{"key":"e_1_3_2_2_50_1","volume-title":"Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song , Chenlin Meng , and Stefano Ermon . 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 ( 2020 ). Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)."},{"key":"e_1_3_2_2_51_1","first-page":"24804","article-title":"CSDI: Conditional score-based diffusion models for probabilistic time series imputation","volume":"34","author":"Tashiro Yusuke","year":"2021","unstructured":"Yusuke Tashiro , Jiaming Song , Yang Song , and Stefano Ermon . 2021 . CSDI: Conditional score-based diffusion models for probabilistic time series imputation . Advances in Neural Information Processing Systems , Vol. 34 (2021), 24804 -- 24816 . Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. 2021. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 24804--24816.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_52_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Upadhyay Utkarsh","year":"2018","unstructured":"Utkarsh Upadhyay , Abir De , and Manuel Gomez Rodriguez . 2018 . Deep reinforcement learning of marked temporal point processes . Advances in Neural Information Processing Systems , Vol. 31 (2018). Utkarsh Upadhyay, Abir De, and Manuel Gomez Rodriguez. 2018. Deep reinforcement learning of marked temporal point processes. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_53_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494993"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2017.2699169"},{"key":"e_1_3_2_2_56_1","volume-title":"ITM Web of Conferences","volume":"23","author":"Stanis\u0142aw Wke","year":"2018","unstructured":"Stanis\u0142aw Wke glarczyk. 2018 . Kernel density estimation and its application . In ITM Web of Conferences , Vol. 23 . EDP Sciences, 00037. Stanis\u0142aw Wke glarczyk. 2018. Kernel density estimation and its application. In ITM Web of Conferences, Vol. 23. EDP Sciences, 00037."},{"key":"e_1_3_2_2_57_1","volume-title":"Wasserstein learning of deep generative point process models. Advances in neural information processing systems","author":"Xiao Shuai","year":"2017","unstructured":"Shuai Xiao , Mehrdad Farajtabar , Xiaojing Ye , Junchi Yan , Le Song , and Hongyuan Zha . 2017. Wasserstein learning of deep generative point process models. Advances in neural information processing systems , Vol. 30 ( 2017 ). Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, and Hongyuan Zha. 2017. Wasserstein learning of deep generative point process models. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2016.2517638"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2327053"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272003"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3542671"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583276"},{"key":"e_1_3_2_2_63_1","volume-title":"International conference on machine learning. PMLR, 11183--11193","author":"Zhang Qiang","year":"2020","unstructured":"Qiang Zhang , Aldo Lipani , Omer Kirnap , and Emine Yilmaz . 2020 . Self-attentive Hawkes process . In International conference on machine learning. PMLR, 11183--11193 . Qiang Zhang, Aldo Lipani, Omer Kirnap, and Emine Yilmaz. 2020. Self-attentive Hawkes process. In International conference on machine learning. PMLR, 11183--11193."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00577"},{"key":"e_1_3_2_2_65_1","volume-title":"Neural Point Process for Learning Spatiotemporal Event Dynamics. In Learning for Dynamics and Control Conference. PMLR, 777--789","author":"Zhou Zihao","year":"2022","unstructured":"Zihao Zhou , Xingyi Yang , Ryan Rossi , Handong Zhao , and Rose Yu . 2022 . Neural Point Process for Learning Spatiotemporal Event Dynamics. In Learning for Dynamics and Control Conference. PMLR, 777--789 . Zihao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, and Rose Yu. 2022. Neural Point Process for Learning Spatiotemporal Event Dynamics. In Learning for Dynamics and Control Conference. PMLR, 777--789."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3068139"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2006.00559.x"},{"key":"e_1_3_2_2_68_1","volume-title":"International conference on machine learning. PMLR, 11692--11702","author":"Zuo Simiao","year":"2020","unstructured":"Simiao Zuo , Haoming Jiang , Zichong Li , Tuo Zhao , and Hongyuan Zha . 2020 . Transformer hawkes process . In International conference on machine learning. PMLR, 11692--11702 . Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, and Hongyuan Zha. 2020. Transformer hawkes process. In International conference on machine learning. PMLR, 11692--11702."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599511","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599511","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:52Z","timestamp":1750178272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":68,"alternative-id":["10.1145\/3580305.3599511","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599511","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}