[36] Dan Roth and Wen-tau Yih. Global Inference for Entity and Relation Identification via a Linear Programming Formulation. MIT Press, Introduction to Statistical Relational Learning edition, 2007.
[37] Rajhans Samdani, Ming-Wei Chang, and Dan Roth. Unified expectation maximization. In ACL, pages 688–698, 2012.
[38] Prithviraj Sen, Bruno W. S. R. de Carvalho, Ryan Riegel, and Alexander Gray. Neuro-symbolic inductive logic programming with logical neural networks. AAAI, 36(8):8212-8219, 2022.
[39] Junghoon Seo and Joon Suk Huh. On the power of deep but naive partial label learning. In ICASSP, pages 3820-3824, 2021.
[40] Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, USA, 2014.
[41] Mate Soos, Divesh Aggarwal, Sourav Chakraborty, Kuldeep S. Meel, and Maciej Obremski. Engineering an efficient approximate dnf-counter. In IJCAI, pages 2031-2038, 2023.
[42] Jacob Steinhardt and Percy S Liang. Learning with relaxed supervision. In NeurIPS, volume 28, 2015.
[43] Efthymia Tsamoura, Timothy Hospedales, and Loizos Michael. Neural-symbolic integration: A compositional perspective. In AAAI, pages 5051-5060, 2021.
[44] Efthymia Tsamoura, Jaehun Lee, and Jacopo Urbani. Probabilistic reasoning at scale: Trigger graphs to the rescue. Proceedings of the ACM on Management of Data, 1(1), 2023.
[45] Shyam Upadhyay, Ming-Wei Chang, Kai-Wei Chang, and Wen-tau Yih. Learning from explicit and implicit supervision jointly for algebra word problems. In EMNLP, pages 297–306, 2016.
[46] Brendan van Rooyen and Robert C. Williamson. A theory of learning with corrupted labels. Journal of Machine Learning Research, 18(228):1-50, 2018.
[47] Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, and Michal Rolínek. Differentiation of blackbox combinatorial solvers. CoRR, abs/1912.02175, 2019.
[48] Haoyu Peter Wang, Nan Wu, Hang Yang, Cong Hao, and Pan Li. Unsupervised learning for combinatorial optimization with principled objective relaxation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, NeurIPS, volume 35, pages 31444–31458, 2022.
[49] Kaifu Wang, Hangfeng He, Tin D. Nguyen, Piyush Kumar, and Dan Roth. On regularization and inference with label constraints. 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 35740-35762. PMLR, 23-29 Jul 2023.
[50] Kaifu Wang, Qiang Ning, and Dan Roth. Learnability with indirect supervision signals. In NeurIPS, volume 33, pages 9145–9155, 2020.
[51] Kaifu Wang, Efi Tsamoura, and Dan Roth. On learning latent models with multi-instance weak supervision, 2023.
[52] Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, and Heng Tao Shen. Template-based math word problem solvers with recursive neural networks. In AAAI, pages 7144-7151, 2019.
[53] Po-Wei Wang, Priya L. Donti, Bryan Wilder, and J. Zico Kolter. Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. In ICML, 2019.
[54] Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, and Zhouchen Lin. Leveraged weighted loss for partial label learning. CoRR, abs/2106.05731, 2021.