| Development and benchmarking of open force field v1. 0.0—the parsley small-molecule force field Y Qiu, DGA Smith, S Boothroyd, H Jang, DF Hahn, J Wagner, CC Bannan, ... Journal of chemical theory and computation 17 (10), 6262-6280, 2021 | 138 | 2021 |
| Approximating gradients for differentiable quality diversity in reinforcement learning B Tjanaka, MC Fontaine, J Togelius, S Nikolaidis Proceedings of the Genetic and Evolutionary Computation Conference, 2022 | 56 | 2022 |
| On the importance of environments in human-robot coordination MC Fontaine, YC Hsu, Y Zhang, B Tjanaka, S Nikolaidis arXiv preprint arXiv:2106.10853, 2021 | 54 | 2021 |
| Scalable hierarchical agglomerative clustering N Monath, KA Dubey, G Guruganesh, M Zaheer, A Ahmed, A McCallum, ... Proceedings of the 27th ACM SIGKDD Conference on knowledge discovery & data …, 2021 | 48 | 2021 |
| pyribs: A bare-bones python library for quality diversity optimization B Tjanaka, MC Fontaine, DH Lee, Y Zhang, TTM Vu, S Sommerer, ... | 41 | 2021 |
| Deep surrogate assisted generation of environments V Bhatt, B Tjanaka, M Fontaine, S Nikolaidis Advances in Neural Information Processing Systems 35, 37762-37777, 2022 | 35 | 2022 |
| Training diverse high-dimensional controllers by scaling covariance matrix adaptation map-annealing B Tjanaka, MC Fontaine, DH Lee, A Kalkar, S Nikolaidis IEEE Robotics and Automation Letters, 2023 | 10 | 2023 |
| Proximal policy gradient arborescence for quality diversity reinforcement learning S Batra, B Tjanaka, MC Fontaine, A Petrenko, S Nikolaidis, G Sukhatme arXiv preprint arXiv:2305.13795, 2023 | 9 | 2023 |
| Scalable bottom-up hierarchical clustering N Monath, A Dubey, G Guruganesh, M Zaheer, A Ahmed, A McCallum, ... arXiv preprint arXiv:2010.11821, 2020 | 7 | 2020 |
| openforcefield/openforcefields: Version 1.2. 0 “Parsley” update H Jang, J Maat, Y Qiu, DG Smith, S Boothroyd, J Wagner, CC Bannan, ... Zenodo.[Google Scholar], 2020 | 6 | 2020 |
| openforcefield/openforcefields: Version 1.2. 0" Parsley H Jang, J Maat, Y Qiu, DG Smith, S Boothroyd, J Wagner, CC Bannan, ... Update, June, 2020 | 5 | 2020 |
| Surrogate assisted generation of human-robot interaction scenarios V Bhatt, H Nemlekar, MC Fontaine, B Tjanaka, H Zhang, YC Hsu, ... arXiv preprint arXiv:2304.13787, 2023 | 4 | 2023 |
| openforce-field/openforcefields: Version 1.0. 0 “Parsley” Y Qiu, DGA Smith, S Boothroyd, J Wagner, CC Bannan, T Gokey, H Jang, ... Zenodo, 2019 | 4 | 2019 |
| Density descent for diversity optimization DH Lee, A Palaparthi, MC Fontaine, B Tjanaka, S Nikolaidis Proceedings of the Genetic and Evolutionary Computation Conference, 674-682, 2024 | 2 | 2024 |
| Scaling covariance matrix adaptation map-annealing to high-dimensional controllers B Tjanaka, MC Fontaine, A Kalkar, S Nikolaidis Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | 2 | 2022 |
| Differentiable quality diversity for reinforcement learning by approximating gradients B Tjanaka, MC Fontaine, J Togelius, S Nikolaidis ICLR Workshop on Agent Learning in Open-Endedness, 2022 | 2 | 2022 |
| Quality Diversity for Robot Learning: Limitations and Future Directions S Batra, B Tjanaka, S Nikolaidis, G Sukhatme Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2024 | | 2024 |
| Bottom-Up Hierarchical Inference for DP-Means AMEH Ahmed, A McCallum, A Dubey, B Tjanaka, G Mergen, ... | | 2020 |
| Fitting improper torsion parameters for atomistic force fields J Maat, C Bannan, V Lim, D Mobley, B Tjanaka, LP Wang, C Bayly ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 258, 2019 | | 2019 |
| Covariance Matrix Adaptation MAP-Annealing: Theory and Experiments S Zhao, B Tjanaka, MC Fontaine, S Nikolaidis ACM Transactions on Evolutionary Learning, 0 | | |