[23] Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, and Martin Vechev. Dl2: Training and querying neural networks with logic. In International Conference on Machine Learning, pages 1931–1941. PMLR, 2019.
[24] Kareem Ahmed, Eric Wang, Kai-Wei Chang, and Guy Van den Broeck. Neuro-symbolic entropy regularization. In UAI, 2022.
[25] Nick Hoernle, Rafael Michael Karampatsis, Vaishak Belle, and Kobi Gal. Multiplexnet: Towards fully satisfied logical constraints in neural networks. In AAAI, 2022.
[26] Michelangelo Diligenti, Marco Gori, Marco Maggini, and Leonardo Rigutini. Bridging logic and kernel machines. Machine learning, 86(1):57-88, 2012.
[27] Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Wang, and Lise Getoor. Neupsl: Neural probabilistic soft logic. arXiv preprint arXiv:2205.14268, 2022.
[28] Luc De Raedt and Angelika Kimmig. Probabilistic (logic) programming concepts. Machine Learning, 2015.
[29] Adnan Darwiche and Pierre Marquis. A knowledge compilation map. Journal of Artificial Intelligence Research, 17:229-264, 2002.
[30] Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, and Guy Van den Broeck. A compositional atlas of tractable circuit operations for probabilistic inference. Advances in Neural Information Processing Systems, 34, 2021.
[31] Robin Manhaeve, Giuseppe Marra, and Luc De Raedt. Approximate inference for neural probabilistic logic programming. In  $KR$ , 2021.
[32] Jiani Huang, Ziyang Li, Binghong Chen, Karan Samel, Mayur Naik, Le Song, and Xujie Si. Scallop: From probabilistic deductive databases to scalable differentiable reasoning. NeurIPS, 2021.
[33] Thomas Winters, Giuseppe Marra, Robin Manhaeve, and Luc De Raedt. DeepStochLog: Neural Stochastic Logic Programming. In AAAI, 2022.
[34] Emile van Krieken, Thiviyan Thanapalasingam, Jakub M Tomczak, Frank van Harmelen, and Annette ten Teije. A-nesi: A scalable approximate method for probabilistic neurosymbolic inference. arXiv preprint arXiv:2212.12393, 2022.
[35] Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. Toward causal representation learning. Proceedings of the IEEE, 2021.
[36] Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, and Stefan Bauer. Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness. In International Conference on Machine Learning, pages 6056-6065. PMLR, 2019.
[37] Julius Von Kugelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Scholkopf, Michel Besserve, and Francesco Locatello. Self-supervised learning with data augmentations provably isolates content from style. Advances in neural information processing systems, 34: 16451-16467, 2021.
[38] Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. Dataset shift in machine learning. Mit Press, 2008.
[39] Emanuele Marconato, Andrea Passerini, and Stefano Teso. Glancenets: Interpretable, leakproof concept-based models. NeurIPS, 2022.
[40] Geoffrey E Hinton and Richard Zemel. Autoencoders, minimum description length and helmholtz free energy. Advances in neural information processing systems, 6, 1993.
[41] Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning, 2014.