Lawrence, Hannah

16 publications

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

ICLRW 2025 Detecting Symmetry-Breaking in Molecular Data Distributions Hannah Lawrence, Elyssa Hofgard, Yuxuan Chen, Tess Smidt, Robin Walters

ICLR 2025 Improving Equivariant Networks with Probabilistic Symmetry Breaking Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba

ICML 2024 Equivariant Frames and the Impossibility of Continuous Canonicalization Nadav Dym, Hannah Lawrence, Jonathan W. Siegel

ICMLW 2024 Improving Equivariant Networks with Probabilistic Symmetry Breaking Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba

ICLR 2024 Learning Polynomial Problems with $SL(2, \mathbb{R})$-Equivariance Hannah Lawrence, Mitchell Tong Harris

ICLR 2024 On the Hardness of Learning Under Symmetries Bobak Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber

ICLR 2023 Distilling Model Failures as Directions in Latent Space Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry

ICMLW 2023 Learning Polynomial Problems with SL(2)-Equivariance Hannah Lawrence, Mitchell Tong Harris

ICMLW 2023 Positional Encodings as Group Representations: A Unified Framework Derek Lim, Hannah Lawrence, Ningyuan Teresa Huang, Erik Henning Thiede

NeurIPS 2023 Self-Supervised Learning with Lie Symmetries for Partial Differential Equations Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak Kiani

ICLRW 2023 Self-Supervised Learning with Lie Symmetries for Partial Differential Equations Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak Kiani

NeurIPSW 2022 Barron's Theorem for Equivariant Networks Hannah Lawrence

NeurIPS 2022 GULP: A Prediction-Based Metric Between Representations Enric Boix-Adsera, Hannah Lawrence, George Stepaniants, Philippe Rigollet

ICML 2022 Implicit Bias of Linear Equivariant Networks Hannah Lawrence, Kristian Georgiev, Andrew Dienes, Bobak T. Kiani

NeurIPS 2020 Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi