[36] Haggai Maron, Ethan Fetaya, Nimrod Segol, and Yaron Lipman. On the universality of invariant networks. In International conference on machine learning, pages 4363-4371. PMLR, 2019.
[37] Haggai Maron, Or Litany, Gal Chechik, and Ethan Fetaya. On learning sets of symmetric elements. In International conference on machine learning, pages 6734-6744. PMLR, 2020.
[38] Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, and Hyunjik Kim. Learning instance-specific augmentations by capturing local invariances. 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 24720-24736. PMLR, 23-29 Jul 2023. URL https://proceedings.mlr.press/v202/miao23a.html.
[39] Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, and Siamak Ravanbakhsh. EqR: Equivariant representations for data-efficient reinforcement learning. 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 15908-15926. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/mondal22a.html.
[40] Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, and Arnold W.M. Smeulders. LieGG: Studying learned lie group generators. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=9sKZ60VtRmi.
[41] Chuer Pan, Brian Okorn, Harry Zhang, Ben Eisner, and David Held. Tax-posed: Task-specific cross-posed estimation for robot manipulation. In Conference on Robot Learning, pages 1783–1792. PMLR, 2023.
[42] Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan Willem van de Meent, and Robin Walters. Learning symmetric representations for equivariant world model. In International Conference on Machine Learning, 2022. URL https://arxiv.org/abs/2204.11371.
[43] Robin Quessard, Thomas Barrett, and William Clements. Learning disentangled representations and group structure of dynamical environments. Advances in Neural Information Processing Systems, 33:19727-19737, 2020.
[44] Cédric Rommel, Thomas Moreau, Joseph Paillard, and Alexandre Gramfort. Cadda: Class-wise automatic differentiable data augmentation for eeg signals. In International Conference on Learning Representations, 2021.
[45] Hyunwoo Ryu, Hong in Lee, Jeong-Hoon Lee, and Jondeun Choi. Equivariant descriptor fields: SE(3)-equivariant energy-based models for end-to-end visual robotic manipulation learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=dnjZSPGmY50.
[46] Akiyoshi Sannai, Masaaki Imaizumi, and Makoto Kawano. Improved generalization bounds of group invariant/equivariant deep networks via quotient feature spaces. In Uncertainty in Artificial Intelligence, pages 771-780. PMLR, 2021.
[47] Uwe Schmidt and Stefan Roth. Learning rotation-aware features: From invariant priors to equivariant descriptors. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2050–2057. IEEE, 2012.
[48] Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann. Neural descriptor fields: Se (3)-equivariant object representations for manipulation. In 2022 International Conference on Robotics and Automation (ICRA), pages 6394-6400. IEEE, 2022.
[49] Anthony Simeonov, Yilun Du, Yen-Chen Lin, Alberto Rodriguez Garcia, Leslie Pack Kaelbling, Tomás Lozano-Pérez, and Pulkit Agrawal. Se (3)-equivariant relational rearrangement with neural descriptor fields. In Conference on Robot Learning, pages 835-846. PMLR, 2023.