% Key papers for Semidefinite relaxations for verification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_towards_37056,
  title = {{Towards Fast Computation of Certified Robustness for ReLU Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2052},
}

@inproceedings{neurips2021_beta_62632,
  title = {{Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26793},
}

@inproceedings{iclr2021_fast_30620,
  title = {{Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3365},
}

@inproceedings{cvpr2019_robustness_14749,
  title = {{Robustness Verification of Classification Deep Neural Networks via Linear Programming}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2020_the_60937,
  title = {{The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18776},
}

@inproceedings{neurips2019_robustness_58924,
  title = {{Robustness Verification of Tree-based Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14191},
}

@inproceedings{neurips2022_general_63989,
  title = {{General Cutting Planes for Bound-Propagation-Based Neural Network Verification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53350},
}

@inproceedings{neurips2020_certified_59523,
  title = {{Certified Monotonic Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17266},
}

@inproceedings{iclr2022_complete_31630,
  title = {{Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6097},
}

@inproceedings{neurips2020_semialgebraic_60815,
  title = {{Semialgebraic Optimization for Lipschitz Constants of ReLU Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17096},
}

@inproceedings{neurips2020_enabling_59783,
  title = {{Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19019},
}

@inproceedings{aaai2022_tight_2845,
  title = {{Tight Neural Network Verification via Semidefinite Relaxations and Linear Reformulations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07272-tight-neural-network-verification-via-semidefinite-relaxations-and-linear-reformulations/},
}

@inproceedings{neurips2021_partition_62998,
  title = {{Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26960},
}

@inproceedings{icml2021_comboptnet_39142,
  title = {{CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9555},
}

@inproceedings{neurips2020_lipschitz_60259,
  title = {{Lipschitz-Certifiable Training with a Tight Outer Bound}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17356},
}

@inproceedings{iclr2021_scaling_30708,
  title = {{Scaling the Convex Barrier with Active Sets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3035},
}

@inproceedings{neurips2022_efficiently_64751,
  title = {{Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53888},
}

@inproceedings{neurips2024_scalable_71308,
  title = {{Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95939},
}

@inproceedings{aaai2024_taming_7493,
  title = {{Taming Binarized Neural Networks and Mixed-Integer Programs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28968},
}

@inproceedings{neurips2023_neural_67188,
  title = {{Neural Lyapunov Control for Discrete-Time Systems}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72523},
}

@inproceedings{aaai2025_dynamic_8840,
  title = {{Dynamic Algorithm Termination for Branch-and-Bound-based Neural Network Verification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34946},
}

@inproceedings{aaai2025_dynamic_8841,
  title = {{Dynamic Back-Substitution in Bound-Propagation-Based Neural Network Verification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34949},
}

@inproceedings{neurips2024_eclipse_70560,
  title = {{ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96554},
}

@inproceedings{icml2024_lyapunov_43410,
  title = {{Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35042},
}

@inproceedings{icml2024_a_45315,
  title = {{A fast algorithm to simulate nonlinear resistive networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33137},
}

@inproceedings{iclr2026_expressiveness_87745,
  title = {{Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=f07Kf4pD0f},
}

@inproceedings{aaai2026_on_108531,
  title = {{On the Probabilistic Learnability of Compact Neural Network Preimage Bounds}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40883},
}

@inproceedings{icml2024_practical_44424,
  title = {{Practical Performance Guarantees for Pipelined DNN Inference}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34028},
}

@inproceedings{icml2025_sdp_46150,
  title = {{SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46410},
}

@inproceedings{neurips2024_provable_71483,
  title = {{Provable Editing of Deep Neural Networks using Parametric Linear Relaxation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95777},
}

@inproceedings{neurips2025_clip_76297,
  title = {{Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=HuSSR12Yot},
}

@inproceedings{neurips2026_soc_92238,
  title = {{SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150828},
}

@inproceedings{aaai2026_scalable_106106,
  title = {{Scalable Mixed-Integer Optimization with Neural Constraints via Dual Decomposition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38454},
}

@inproceedings{aaai2026_t_106111,
  title = {{T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38459},
}

@inproceedings{aaai2026_hiq_106960,
  title = {{HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39312},
}

@inproceedings{aaai2026_tighter_107181,
  title = {{Tighter Truncated Rectangular Prism Approximation for RNN Robustness Verification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39533},
}

@inproceedings{iclr2024_on_34836,
  title = {{On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18238},
}

@inproceedings{icml2025_solving_48814,
  title = {{Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43808},
}