List of references

Forward Modeling

-Trabucco, Brandon, et al. “Conservative objective models for effective offline model-based optimization.” International Conference on Machine Learning. PMLR, 2021. https://arxiv.org/abs/2107.06882

• Yu, Sihyun, et al. “Roma: Robust model adaptation for offline model-based optimization.” Advances in Neural Information Processing Systems 34 (2021): 4619-4631. https://arxiv.org/abs/2110.14188

• Yao, Michael, et al. “Generative adversarial model-based optimization via source critic regularization.” Advances in neural information processing systems 37 (2024): 44009-44039. https://arxiv.org/abs/2402.06532 

• Dao, Manh Cuong, et al. “Boosting offline optimizers with surrogate sensitivity.” arXiv preprint arXiv:2503.04181 (2025). https://arxiv.org/abs/2503.04181 

• Dao, Cuong, et al. “Incorporating surrogate gradient norm to improve offline optimization techniques.” Advances in Neural Information Processing Systems 37 (2024): 8014-8046. https://arxiv.org/abs/2503.04242 

• Hoang, Minh, et al. “Learning surrogates for offline black-box optimization via gradient matching.” arXiv preprint arXiv:2503.01883 (2025).https://arxiv.org/abs/2503.01883 

• Chemingui, Yassine, et al. “Offline model-based optimization via policy-guided gradient search.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 10. 2024. https://ojs.aaai.org/index.php/AAAI/article/view/29001

Inverse Modeling 

• Brookes, David H., and Jennifer Listgarten. “Design by adaptive sampling.” arXiv preprint arXiv:1810.03714 (2018).https://arxiv.org/abs/1810.03714

• Brookes, David, Hahnbeom Park, and Jennifer Listgarten. “Conditioning by adaptive sampling for robust design.” International conference on machine learning. PMLR, 2019.https://arxiv.org/abs/1901.10060

• Mashkaria, Satvik Mehul, Siddarth Krishnamoorthy, and Aditya Grover. “Generative pretraining for black-box optimization.” International Conference on Machine Learning. PMLR, 2023.https://arxiv.org/abs/2206.10786 

• Krishnamoorthy, Siddarth, Satvik Mehul Mashkaria, and Aditya Grover. “Diffusion models for black-box optimization.” International Conference on Machine Learning. PMLR, 2023. https://arxiv.org/abs/2306.07180 

• Dao, Manh Cuong, et al. “ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge.” arXiv preprint arXiv:2509.16300 (2025). https://arxiv.org/abs/2509.16300

• Annadani, Yashas, et al. “Preference-Guided Diffusion for Multi-Objective Offline Optimization.” arXiv preprint arXiv:2503.17299 (2025). https://arxiv.org/abs/2503.17299

Small Data Setting

• Nguyen, Tung, Sudhanshu Agrawal, and Aditya Grover. “Expt: Synthetic pretraining for few-shot experimental design.” Advances in Neural Information Processing Systems 36 (2023): 45856-45869. https://arxiv.org/abs/2310.19961

Theoretical Analysis

• Grudzien Kuba, Jakub, et al. “Functional Graphical Models: Structure Enables Offline Data-Driven Optimization.” arXiv e-prints (2024): arXiv-2401. https://proceedings.mlr.press/v238/grudzien24a/grudzien24a.pdf 

• Li, Zihao, et al. “Diffusion model for data-driven black-box optimization.” arXiv preprint arXiv:2403.13219 (2024). https://arxiv.org/abs/2403.13219

Survey

• Kim, Minsu, et al. “Offline Model-Based Optimization: Comprehensive Review.” arXiv preprint arXiv:2503.17286 (2025). https://arxiv.org/abs/2503.17286

Benchmarks and Evaluation

• Trabucco, Brandon, et al. “Design-bench: Benchmarks for data-driven offline model-based optimization.” International Conference on Machine Learning. PMLR, 2022. https://arxiv.org/abs/2202.08450