Jain, Moksh, Raparthy, Sharath Chandra, Hernández-García, Alex, Rector-Brooks, Jarrid, Bengio, Yoshua, Miret, Santiago, and Bengio, Emmanuel. Multi-objective gflownets. International Conference on Machine Learning (ICML), 2023b.
Karalias, Nikolaos and Loukas, Andreas. Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs. Neural Information Processing Systems (NeurIPS), 2020.
Kool, Wouter, van Hoof, Herke, and Welling, Max. Attention, learn to solve routing problems! International Conference on Learning Representations (ICLR), 2019.
Kruskal, Joseph B. On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1):48-50, 1956.
Kuhn, Harold W. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97, 1955.
Kwon, Yeong-Dae, Choo, Jinho, Kim, Byoungjip, Yoon, Iljoo, Min, Seungjai, and Gwon, Youngjune. POMO: Policy optimization with multiple optima for reinforcement learning. Neural Information Processing Systems (NeurIPS), 2020.
Lahlou, Salem, Deleu, Tristan, Lemos, Pablo, Zhang, Dinghuai, Volokhova, Alexandra, Hernández-García, Alex, Ezzine, Léna Néhale, Bengio, Yoshua, and Malkin, Nikolay. A theory of continuous generative flow networks. International Conference on Machine Learning (ICML), 2023.
Lamm, Sebastian, Sanders, Peter, Schulz, Christian, Strash, Darren, and Werneck, Renato F. Finding near-optimal independent sets at scale. J. Heuristics, 23(4):207-229, 2017. doi: 10.1007/s10732-017-9337-x. URL https://doi.org/10.1007/s10732-017-9337-x.
Li, Wenqian, Li, Yinchuan, Li, Zhigang, Hao, Jianye, and Pang, Yan. DAG Matters! GFlowNets enhanced explainer for graph neural networks. International Conference on Learning Representations (ICLR), 2023.
Li, Zhuwen, Chen, Qifeng, and Koltun, Vladlen. Combinatorial optimization with graph convolutional networks and guided tree search. Neural Information Processing Systems (NeurIPS), 2018.
Madan, Kanika, Rector-Brooks, Jarrid, Korablyov, Maksym, Bengio, Emmanuel, Jain, Moksh, Nica, Andrei Cristian, Bosc, Tom, Bengio, Yoshua, and Malkin, Nikolay. Learning GFlowNets from partial episodes for improved convergence and stability. International Conference on Machine Learning (ICML), 2023.
Malkin, Nikolay, Jain, Moksh, Bengio, Emmanuel, Sun, Chen, and Bengio, Yoshua. Trajectory balance: Improved credit assignment in GFlowNets. Neural Information Processing Systems (NeurIPS), 2022.
Malkin, Nikolay, Lahlou, Salem, Deleu, Tristan, Ji, Xu, Hu, Edward J., Everett, Katie Elizabeth, Zhang, Dinghuai, and Bengio, Yoshua. GFlowNets and variational inference. International Conference on Learning Representations (ICLR), 2023.
Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A, Veness, Joel, Bellemare, Marc G, Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K, Ostrovski, Georg, et al. Human-level control through deep reinforcement learning. nature, 518(7540):529-533, 2015.
Nair, Vinod, Bartunov, Sergey, Gimeno, Felix, von Glehn, Ingrid, Lichocki, Pawel, Lobov, Ivan, O'Donoghue, Brendan, Sonnerat, Nicolas, Tjandraatmadja, Christian, Wang, Pengming, Addanki, Ravichandra, Hapuarachchi, Tharindi, Keck, Thomas, Keeling, James, Kohli, Pushmeet, Ktena, Ira, Li, Yujia, Vinyals, Oriol, and Zwols, Yori. Solving mixed integer programs using neural networks. arXiv preprint 2012.13349, 2020.
Nishikawa-Toomey, Mizu, Deleu, Tristan, Subramanian, Jithendaraa, Bengio, Yoshua, and Charlin, Laurent. Bayesian learning of causal structure and mechanisms with gflows and variational bayes. ArXiv, abs/2211.02763, 2022. URL https://api_semanticscholar.org/CorpusID:253384724.