Hannah Lawrence

According to our database, Hannah Lawrence authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Equivariant Frames and the Impossibility of Continuous Canonicalization.

Nadav Dym, Hannah Lawrence, Jonathan W. Siegel

CoRR, 2024

On the hardness of learning under symmetries.

Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber

CoRR, 2024

2023

Learning Polynomial Problems with SL(2, ℝ) Equivariance.

Hannah Lawrence, Mitchell Tong Harris

CoRR, 2023

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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, 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

CoRR, 2023

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak Toussi Kiani

CoRR, 2023

Toeplitz Low-Rank Approximation with Sublinear Query Complexity.

Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco, Kshiteej Sheth

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak T. Kiani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distilling Model Failures as Directions in Latent Space.

Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

GULP: a prediction-based metric between representations.

Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Implicit Bias of Linear Equivariant Networks.

Hannah Lawrence, Bobak Toussi Kiani, Kristian G. Georgiev, Andrew K. Dienes

Proceedings of the International Conference on Machine Learning, 2022

2021

Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging.

Hannah Lawrence, David Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition.

Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers.

Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020