{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:41:45Z","timestamp":1763106105692,"version":"3.45.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,15]]},"DOI":"10.1145\/3768292.3770391","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:24:26Z","timestamp":1763105066000},"page":"906-914","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust time series generation via Schr\u00f6dinger Bridge: a comprehensive evaluation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2582-4328","authenticated-orcid":false,"given":"Alexandre","family":"Alouadi","sequence":"first","affiliation":[{"name":"Ecole Polytechnique, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9045-0141","authenticated-orcid":false,"given":"Baptiste","family":"Barreau","sequence":"additional","affiliation":[{"name":"BNP PARIBAS, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0631-6100","authenticated-orcid":false,"given":"Laurent","family":"Carlier","sequence":"additional","affiliation":[{"name":"BNP PARIBAS, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9758-3550","authenticated-orcid":false,"given":"Huy\u00ean","family":"Pham","sequence":"additional","affiliation":[{"name":"Ecole Polytechnique, Paris, France"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_3_1_2_2","volume-title":"International Conference on Machine Learning (ICML)","author":"Behrmann Jens","year":"2019","unstructured":"Jens Behrmann, Will Grathwohl, Ricky T.\u00a0Q. Chen, David Duvenaud, and J\u00f6rn-Henrik Jacobsen. 2019. Invertible Residual Networks. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_3_1_3_2","volume-title":"Advances in Neural Information Processing Systems","author":"Bortoli Valentin\u00a0De","year":"2021","unstructured":"Valentin\u00a0De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet. 2021. Diffusion Schr\u00f6dinger bridge with applications to score-based generative modeling. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_3_1_4_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Brock Andrew","year":"2019","unstructured":"Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Luis Candanedo\u00a0Ibarra Veronique Feldheim and Dominique Deramaix. 2017. Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings 140 (2017).","DOI":"10.1016\/j.enbuild.2017.01.083"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Y. Chen T. Georgiou and M. Pavon. 2021. Stochastic control liaisons: Richard Sinkhorn meets Gaspard Monge on a Schr\u00f6dinger bridge. SIAM review 63 2 (2021).","DOI":"10.1137\/20M1339982"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Saverio De\u00a0Vito Ettore Massera M Piga L Martinotto and G Francia. 2008. On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sensors and Actuators B Chemical 129 (2008) 750\u2013757.","DOI":"10.1016\/j.snb.2007.09.060"},{"key":"e_1_3_3_1_8_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Dinh Laurent","year":"2015","unstructured":"Laurent Dinh, David Krueger, and Yoshua Bengio. 2015. NICE: Non-linear Independent Components Estimation. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_9_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Goodfellow Ian\u00a0J.","year":"2014","unstructured":"Ian\u00a0J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Networks. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Mohamed Hamdouche Pierre Henry-Labordere and Huy\u00ean Pham. 2023. Generative modeling for time series via Schr\u00f6dinger bridge.","DOI":"10.2139\/ssrn.4412434"},{"key":"e_1_3_3_1_11_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_12_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Karras Tero","year":"2018","unstructured":"Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_13_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Kingma Diederik\u00a0P.","year":"2018","unstructured":"Diederik\u00a0P. Kingma and Prafulla Dhariwal. 2018. Glow: Generative Flow with Invertible 1x1 Convolutions. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_14_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kingma Diederik\u00a0P.","year":"2014","unstructured":"Diederik\u00a0P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Christian L\u00e9onard. 2014. A survey of the Schr\u00f6dinger problem and some of its connections with optimal transport. Discrete continuous and dynamical systems (2014).","DOI":"10.3934\/dcds.2014.34.1533"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Mark Leznik Arne Lochner Stefan Wesner and J\u00f6rg Domaschka. 2022. [SoK] The Great GAN Bake Off An Extensive Systematic Evaluation of Generative Adversarial Network Architectures for Time Series Synthesis. Journal of Systems Research 2 1 (2022).","DOI":"10.5070\/SR32159045"},{"key":"e_1_3_3_1_17_2","unstructured":"Haksoo Lim Minjung Kim Sewon Park Jaehoon Lee and Noseong Park. 2024. TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models."},{"key":"e_1_3_3_1_18_2","volume-title":"Advancement of Artificial Intelligence (AAAI)","author":"Lim Haksoo","year":"2023","unstructured":"Haksoo Lim, Minjung Kim, Sewon Park, and Noseong Park. 2023. Regular Time-series Generation using SGM. In Advancement of Artificial Intelligence (AAAI)."},{"key":"e_1_3_3_1_19_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Makhzani Alireza","year":"2016","unstructured":"Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2016. Adversarial Autoencoders. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_20_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Naiman Ilan","year":"2024","unstructured":"Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, and Omri Azencot. 2024. Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_21_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Papamakarios George","year":"2017","unstructured":"George Papamakarios, Theo Pavlakou, and Iain Murray. 2017. Masked Autoregressive Flow for Density Estimation. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_22_2","volume-title":"International Conference on Machine Learning (ICML)","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 (ICML)."},{"key":"e_1_3_3_1_23_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Razavi Ali","year":"2019","unstructured":"Ali Razavi, Aaron van\u00a0den Oord, and Oriol Vinyals. 2019. Generating Diverse High-Fidelity Images with VQ-VAE-2. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_3_1_24_2","volume-title":"Advances in Neural Information Processing Systems","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative Modeling by Estimating Gradients of the Data Distribution. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_3_1_25_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Song Yang","year":"2021","unstructured":"Yang Song, Jascha Sohl-Dickstein, Diederik\u00a0P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Wenpin Tang and Hanyang Zhao. 2024. Score-based Diffusion Models via Stochastic Differential Equations \u2013 a Technical Tutorial.","DOI":"10.1214\/25-SS152"},{"key":"e_1_3_3_1_27_2","unstructured":"Jiajie Tao Hao Ni and Chong Liu. 2024. High Rank Path Development: an approach of learning the filtration of stochastic processes."},{"key":"e_1_3_3_1_28_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Tolstikhin Ilya","year":"2018","unstructured":"Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. 2018. Wasserstein Auto-Encoders. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_29_2","unstructured":"Berwin Turlach. 1999. Bandwidth Selection in Kernel Density Estimation: A Review. Technical Report (02 1999)."},{"key":"e_1_3_3_1_30_2","volume-title":"International Conference on Machine Learning (ICML)","author":"Wang Gefei","year":"2021","unstructured":"Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, and Can Yang. 2021. Deep Generative Learning via Schr\u00f6dinger Bridge. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Magnus Wiese Robert Knobloch Ralf Korn and Peter Kretschmer. 2020. Quant GANs: deep generation of financial time series. Quantitative Finance 20 9 (2020) 1419\u20131440.","DOI":"10.1080\/14697688.2020.1730426"},{"key":"e_1_3_3_1_32_2","volume-title":"Advances in Neural Information Processing Systems","author":"Xu Tianlin","year":"2020","unstructured":"Tianlin Xu, Li\u00a0Kevin Wenliang, Michael Munn, and Beatrice Acciaio. 2020. COT-GAN: Generating Sequential Data via Causal Optimal Transport. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_3_1_33_2","volume-title":"Advances in Neural Information Processing Systems","author":"Yoon Jinsung","year":"2019","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela van\u00a0der Schaar. 2019. Time-series Generative Adversarial Networks. In Advances in Neural Information Processing Systems."}],"event":{"name":"ICAIF '25: 6th ACM International Conference on AI in Finance","location":"Singapore Singapore","acronym":"ICAIF '25"},"container-title":["Proceedings of the 6th ACM International Conference on AI in Finance"],"original-title":[],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:33:58Z","timestamp":1763105638000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768292.3770391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":32,"alternative-id":["10.1145\/3768292.3770391","10.1145\/3768292"],"URL":"https:\/\/doi.org\/10.1145\/3768292.3770391","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2025-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}