[5] Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa. Max-value entropy search for multi-objective Bayesian optimization. Advances in Neural Information Processing Systems, 32, 2019.
[6] Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. Flow network based generative models for non-iterative diverse candidate generation. Advances in Neural Information Processing Systems, 34:27381-27394, 2021.
[7] Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio. Gflownet foundations. Journal of Machine Learning Research, 24(210):1-55, 2023.
[8] G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90–98, 2012.
[9] Julian Blank and Kalyanmoy Deb. Pymoo: Multi-objective optimization in python. IEEE Access, 8:89497-89509, 2020.
[10] Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
[11] Eric Bradford, Artur M Schweidtmann, and Alexei Lapkin. Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. Journal of global optimization, 71(2):407-438, 2018.
[12] Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. Advances in Neural Information Processing Systems, 33:9851-9864, 2020.
[13] Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Parallel Bayesian optimization of multiple noisy objectives with expected hypervolume improvement. Advances in Neural Information Processing Systems, 34:2187-2200, 2021.
[14] Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE transactions on evolutionary computation, 18(4):577-601, 2013.
[15] Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio. Bayesian Structure Learning with Generative Flow Networks. arXiv preprint, 2022.
[16] Aryan Deshwal and Jana Doppa. Combining latent space and structured kernels for Bayesian optimization over combinatorial spaces. Advances in Neural Information Processing Systems, 34:8185-8200, 2021.
[17] Alexey Dosovitskiy and Josip Djolonga. You only train once: Loss-conditional training of deep networks. In International conference on learning representations, 2019.
[18] Matthias Ehrgott. Multicriteria optimization, volume 491. Springer Science & Business Media, 2005.
[19] Peter Ertl and Ansgar Schuffenhauer. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1(1):1-11, 2009.
[20] Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W Coley, and Jimeng Sun. Differentiable scaffolding tree for molecule optimization. In International Conference on Learning Representations, 2022.
[21] Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pages 1050-1059. PMLR, 2016.
[22] Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley. Sample efficiency matters: a benchmark for practical molecular optimization. Advances in Neural Information Processing Systems, 35:21342-21357, 2022.