% Key papers for Irregular series and imputation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_brits_56885,
  title = {{BRITS: Bidirectional Recurrent Imputation for Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11654},
}

@inproceedings{neurips2018_multivariate_57412,
  title = {{Multivariate Time Series Imputation with Generative Adversarial Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11174},
}

@inproceedings{aaai2021_generative_672,
  title = {{Generative Semi-supervised Learning for Multivariate Time Series Imputation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08983-generative-semi-supervised-learning-for-multivariate-time-series-imputation/},
}

@inproceedings{neurips2021_csdi_62386,
  title = {{CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26846},
}

@inproceedings{neurips2016_a_55552,
  title = {{A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7120},
}

@inproceedings{aaai2018_a_103377,
  title = {{A Neural Stochastic Volatility Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12124},
}

@inproceedings{iclr2019_interpolation_29119,
  title = {{Interpolation-Prediction Networks for Irregularly Sampled Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1058},
}

@inproceedings{aaai2024_grafiti_6142,
  title = {{GraFITi: Graphs for Forecasting Irregularly Sampled Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29560},
}

@inproceedings{iclr2022_graph_31157,
  title = {{Graph-Guided Network for Irregularly Sampled Multivariate Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6409},
}

@inproceedings{iclr2021_multi_29998,
  title = {{Multi-Time Attention Networks for Irregularly Sampled Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2703},
}

@inproceedings{icml2020_set_38755,
  title = {{Set Functions for Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6545},
}

@inproceedings{icml2020_learning_38400,
  title = {{Learning from Irregularly-Sampled Time Series: A Missing Data Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6300},
}

@inproceedings{neurips2022_gt_65584,
  title = {{GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54370},
}

@inproceedings{aaai2022_conditional_1860,
  title = {{Conditional Loss and Deep Euler Scheme for Time Series Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08098-conditional-loss-and-deep-euler-scheme-for-time-series-generation/},
}

@inproceedings{aaai2023_estimating_3592,
  title = {{Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25844},
}

@inproceedings{aaai2025_self_10722,
  title = {{Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33931},
}

@inproceedings{neurips2022_c2far_65840,
  title = {{C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53883},
}

@inproceedings{iclr2021_generative_30284,
  title = {{Generative Time-series Modeling with Fourier Flows}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2750},
}

@inproceedings{iclr2021_probabilistic_30329,
  title = {{Probabilistic Numeric Convolutional Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2556},
}

@inproceedings{icml2023_probabilistic_41676,
  title = {{Probabilistic Imputation for Time-series Classification with Missing Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23522},
}

@inproceedings{icml2024_time_44605,
  title = {{Time Weaver: A Conditional Time Series Generation Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33847},
}

@inproceedings{aaai2025_probabilistic_10364,
  title = {{Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35494},
}

@inproceedings{iclr2024_tactis_35634,
  title = {{TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17439},
}

@inproceedings{icml2024_bayotide_44759,
  title = {{BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33693},
}

@inproceedings{neurips2024_task_73789,
  title = {{Task-oriented Time Series Imputation Evaluation via Generalized Representers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93717},
}

@inproceedings{icml2024_probabilistic_44507,
  title = {{Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33945},
}

@inproceedings{icml2025_lscd_46752,
  title = {{LSCD: Lomb--Scargle Conditioned Diffusion for Time series Imputation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45821},
}

@inproceedings{icml2026_causal_55347,
  title = {{Causal Discovery for Irregularly Time Series with Consistency Guarantees}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=y5GiPedJPV},
}

@inproceedings{neurips2024_graph_74202,
  title = {{Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93348},
}

@inproceedings{neurips2025_adaptive_74782,
  title = {{Adaptive Quantization in Generative Flow Networks for Probabilistic Sequential Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=18GBPdnuXs},
}

@inproceedings{neurips2025_contimask_76502,
  title = {{Contimask: Explaining Irregular Time Series via Perturbations in Continuous Time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Jzr9VOiJYd},
}

@inproceedings{iclr2025_amortized_81038,
  title = {{Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30730},
}

@inproceedings{iclr2025_zero_81905,
  title = {{Zero-shot Imputation with Foundation Inference Models for Dynamical Systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29879},
}

@inproceedings{iclr2026_functional_85368,
  title = {{Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Dgphd9qizu},
}

@inproceedings{iclr2026_cronos_87123,
  title = {{CRONOS: Continuous time reconstruction for 4D medical longitudinal series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=XxqdbYD74l},
}

@inproceedings{neurips2026_sdflow_89988,
  title = {{SDFlow: Similarity-Driven Flow Matching for Time Series Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150124},
}

@inproceedings{neurips2026_procti_90310,
  title = {{ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149404},
}

@inproceedings{neurips2026_discretizing_91212,
  title = {{Discretizing Continuous Time Series for Imputation with Masked Diffusion Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149547},
}