[34] Antoine Chaffin, Vincent Claveau, and Ewa Kijak. PPL-MCTS: Constrained textual generation through discriminator-guided MCTS decoding. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2953-2967, Seattle, United States, July 2022. Association for Computational Linguistics.
[35] Xinyun Chen, Chang Liu, and Dawn Song. Execution-guided neural program synthesis. In International Conference on Learning Representations, 2019.
[36] Parshin Shojaaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K Reddy. Execution-based code generation using deep reinforcement learning. arXiv preprint arXiv:2301.13816, 2023.
[37] Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, and Chuang Gan. Planning with large language models for code generation. In International Conference on Learning Representations, 2023.
[38] Guillaume Lample and François Charton. Deep learning for symbolic mathematics. In International Conference on Learning Representations, 2020.
[39] Roger Fletcher. Practical Methods of Optimization. John Wiley & Sons, New York, NY, USA, second edition, 1987.
[40] Lerente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In Johannes Furnkranz, Tobias Scheffer, and Myra Spiliopoulou, editors, Machine Learning: ECML 2006, pages 282-293, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.
[41] David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science, 362(6419):1140-1144, 2018.
[42] William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio de Franca, Marco Virgolin, Ying Jin, Michael Kommenda, and Jason Moore. Contemporary symbolic regression methods and their relative performance. In J. Vanschoeren and S. Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021.
[43] Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore. Pmlb: a large benchmark suite for machine learning evaluation and comparison. BioData Mining, 10(1):36, Dec 2017.
[44] Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark. Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 4860-4871. Curran Associates, Inc., 2020.
[45] William La Cava, Kourosh Danai, and Lee Spector. Inference of compact nonlinear dynamic models by epigenetic local search. Engineering Applications of Artificial Intelligence, 55:292-306, 2016.
[46] Stéphane D'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, and Francois Charton. Deep symbolic regression for recurrence prediction. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 4520-4536. PMLR, 17-23 Jul 2022.
[47] Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, and Marco Virgolin. Deep generative symbolic regression with Monte-Carlo-tree-search. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 15655-15668. PMLR, 23-29 Jul 2023.
[48] Jerome H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000.