% Key papers for Local and high-dimensional Bayesian optimization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2019_batchbald_57936,
  title = {{BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13801},
}

@inproceedings{neurips2020_botorch_59476,
  title = {{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17263},
}

@inproceedings{icml2017_max_36280,
  title = {{Max-value Entropy Search for Efficient Bayesian Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/630},
}

@inproceedings{neurips2019_scalable_58949,
  title = {{Scalable Global Optimization via Local Bayesian Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14534},
}

@inproceedings{neurips2017_bayesian_56178,
  title = {{Bayesian Optimization with Gradients}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9302},
}

@inproceedings{icml2017_parallel_36337,
  title = {{Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/767},
}

@inproceedings{icml2018_bock_36534,
  title = {{BOCK : Bayesian Optimization with Cylindrical Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2230},
}

@inproceedings{icml2018_bayesian_36540,
  title = {{Bayesian Optimization of Combinatorial Structures}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2444},
}

@inproceedings{icml2019_a_37098,
  title = {{A Framework for Bayesian Optimization in Embedded Subspaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4216},
}

@inproceedings{aaai2016_gaussian_105038,
  title = {{Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10210},
}

@inproceedings{neurips2016_bayesian_55599,
  title = {{Bayesian optimization for automated model selection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7198},
}

@inproceedings{neurips2020_re_60670,
  title = {{Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18297},
}

@inproceedings{neurips2020_boss_59421,
  title = {{BOSS: Bayesian Optimization over String Spaces}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17258},
}

@inproceedings{aaai2018_decentralized_102521,
  title = {{Decentralized High-Dimensional Bayesian Optimization With Factor Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11788},
}

@inproceedings{icml2016_basc_35749,
  title = {{BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/carr16.html},
}

@inproceedings{icml2020_multi_38476,
  title = {{Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6780},
}

@inproceedings{icml2018_optimization_36905,
  title = {{Optimization, fast and slow: optimally switching between local and Bayesian optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2224},
}

@inproceedings{icml2022_accelerating_40200,
  title = {{Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18027},
}

@inproceedings{neurips2019_bayesian_57943,
  title = {{Bayesian Optimization with Unknown Search Space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14154},
}

@inproceedings{aaai2020_gradient_103340,
  title = {{Gradient-Based Optimization for Bayesian Preference Elicitation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6592},
}

@inproceedings{aaai2024_constrained_5477,
  title = {{Constrained Bayesian Optimization under Partial Observations: Balanced Improvements and Provable Convergence}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29488},
}

@inproceedings{icml2024_vanilla_44300,
  title = {{Vanilla Bayesian Optimization Performs Great in High Dimensions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34152},
}

@inproceedings{aaai2025_decomposed_8660,
  title = {{Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33234},
}

@inproceedings{iclr2025_bayesian_81348,
  title = {{Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30425},
}

@inproceedings{neurips2025_bo4mob_77863,
  title = {{BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YKQXu4JAYn},
}

@inproceedings{neurips2024_active_73095,
  title = {{Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94334},
}

@inproceedings{neurips2025_exploring_79408,
  title = {{Exploring and Exploiting Model Uncertainty in Bayesian Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=p58mKXaeWC},
}

@inproceedings{iclr2025_optimizing_81586,
  title = {{Optimizing Posterior Samples for Bayesian Optimization via Rootfinding}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30191},
}

@inproceedings{iclr2026_a_86345,
  title = {{A Genetic Algorithm for Navigating Synthesizable Molecular Spaces}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=OvMtGGaFUT},
}

@inproceedings{aaai2024_kernelized_6377,
  title = {{Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28992},
}

@inproceedings{aaai2025_de_8642,
  title = {{De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35201},
}

@inproceedings{icml2024_quality_45032,
  title = {{Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33420},
}

@inproceedings{icml2025_understanding_45919,
  title = {{Understanding High-Dimensional Bayesian Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46626},
}

@inproceedings{icml2025_a_47002,
  title = {{A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45581},
}

@inproceedings{icml2026_target_49765,
  title = {{Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=5WwoJ2W0nL},
}

@inproceedings{icml2026_alas_51102,
  title = {{ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=IlcYgLsvUN},
}

@inproceedings{icml2026_regime_53043,
  title = {{Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=bSUMVAYoMq},
}

@inproceedings{icml2026_many_53778,
  title = {{Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iZN85pjpsS},
}

@inproceedings{icml2026_local_53802,
  title = {{Local Constrained Bayesian Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iq8WrTozWy},
}

@inproceedings{neurips2024_approximation_74189,
  title = {{Approximation-Aware Bayesian Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93359},
}