% Key papers for State-space inference for stochastic systems (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2019_a_37097,
  title = {{A Dynamical Systems Perspective on Nesterov Acceleration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3920},
}

@inproceedings{icml2019_learning_37497,
  title = {{Learning interpretable continuous-time models of latent stochastic dynamical systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4089},
}

@inproceedings{icml2019_switching_37779,
  title = {{Switching Linear Dynamics for Variational Bayes Filtering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3860},
}

@inproceedings{aaai2018_group_106149,
  title = {{Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11344},
}

@inproceedings{neurips2016_poisson_55938,
  title = {{Poisson-Gamma dynamical systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7424},
}

@inproceedings{cvpr2016_sparse_11878,
  title = {{Sparse Coding and Dictionary Learning With Linear Dynamical Systems}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{icml2016_learning_35891,
  title = {{Learning to Filter with Predictive State Inference Machines}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/sun16.html},
}

@inproceedings{neurips2019_poisson_58807,
  title = {{Poisson-Randomized Gamma Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13309},
}

@inproceedings{neurips2023_score_68355,
  title = {{Score-based Data Assimilation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71453},
}

@inproceedings{neurips2018_metric_57376,
  title = {{Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11292},
}

@inproceedings{neurips2019_kalman_58441,
  title = {{Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14259},
}

@inproceedings{icml2020_collapsed_38020,
  title = {{Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6669},
}

@inproceedings{neurips2020_sparse_60854,
  title = {{Sparse Symplectically Integrated Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18192},
}

@inproceedings{icml2019_efficient_37323,
  title = {{Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4101},
}

@inproceedings{neurips2016_kernel_55802,
  title = {{Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7004},
}

@inproceedings{neurips2017_scalable_56655,
  title = {{Scalable Variational Inference for Dynamical Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9257},
}

@inproceedings{icml2019_overcoming_37635,
  title = {{Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4134},
}

@inproceedings{aaai2021_bayesian_189,
  title = {{Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08083-bayesian-dynamic-mode-decomposition-with-variational-matrix-factorization/},
}

@inproceedings{iclr2022_constrained_31555,
  title = {{Constrained Physical-Statistics Models for Dynamical System Identification and Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6791},
}

@inproceedings{iclr2024_odeformer_34537,
  title = {{ODEFormer: Symbolic Regression of Dynamical Systems with Transformers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18537},
}

@inproceedings{neurips2024_benchmarking_73579,
  title = {{Benchmarking Structural Inference Methods for Interacting Dynamical Systems with Synthetic Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97536},
}

@inproceedings{iclr2024_learning_35481,
  title = {{Learning invariant representations of time-homogeneous stochastic dynamical systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17592},
}

@inproceedings{icml2024_latent_45477,
  title = {{Latent Space Symmetry Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32974},
}

@inproceedings{neurips2024_probabilistic_72608,
  title = {{Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94770},
}

@inproceedings{neurips2024_physics_73472,
  title = {{Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94000},
}

@inproceedings{iclr2025_balanced_83394,
  title = {{Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28422},
}

@inproceedings{iclr2026_self_86014,
  title = {{Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ku3kLJle7Q},
}

@inproceedings{icml2024_on_43882,
  title = {{On the Identifiability of Switching Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34570},
}

@inproceedings{icml2024_prediction_44660,
  title = {{Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33792},
}

@inproceedings{icml2024_pac_44747,
  title = {{PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33705},
}

@inproceedings{icml2024_a_44859,
  title = {{A sampling theory perspective on activations for implicit neural representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33593},
}

@inproceedings{icml2024_nesting_45390,
  title = {{Nesting Particle Filters for Experimental Design in Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33061},
}

@inproceedings{icml2025_reskoopnet_47396,
  title = {{ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45196},
}

@inproceedings{icml2025_tensor_47864,
  title = {{Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44739},
}

@inproceedings{icml2025_guided_48122,
  title = {{Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44486},
}

@inproceedings{icml2026_online_49329,
  title = {{Online Bayesian Experimental Design for Partially Observed Dynamical Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=1Rh9assFy5},
}

@inproceedings{icml2026_maximum_49400,
  title = {{Maximum-Likelihood Learning of Latent Dynamics Without Reconstruction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=2CliXIoJNZ},
}

@inproceedings{icml2026_universal_50746,
  title = {{Universal Learning of Nonlinear Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=FJZiatEiCG},
}

@inproceedings{icml2026_two_53030,
  title = {{Two-Layer Linear Auto-Regressive Models Estimate Latent States}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=bMSnvqVWaB},
}