Hannah Lawrence

31 papers A* 10Misc 1Journal 17Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Any-Subgroup Equivariant Networks via Symmetry Breaking. CoRR Abhinav Goel, Derek Lim, Hannah Lawrence, Stefanie Jegelka, Ningyuan Huang
2026	J	`jnl`	Quantitative Approximation Rates for Group Equivariant Learning. CoRR Jonathan W. Siegel, Snir Hordan, Hannah Lawrence, Ali Syed, Nadav Dym
2025	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Found. Trends Mach. Learn. 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
2025	A*	`conf`	Improving Equivariant Networks with Probabilistic Symmetry Breaking. ICLR Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba
2025	J	`jnl`	Improving Equivariant Networks with Probabilistic Symmetry Breaking. CoRR Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba
2025	J	`jnl`	To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking. CoRR Hannah Lawrence, Elyssa F. Hofgard, Vasco Portilheiro, Yuxuan Chen, Tess E. Smidt, Robin Walters
2024	A*	`conf`	Equivariant Frames and the Impossibility of Continuous Canonicalization. ICML Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	J	`jnl`	Equivariant Frames and the Impossibility of Continuous Canonicalization. CoRR Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024	A*	`conf`	Learning Polynomial Problems with SL(2, R)-Equivariance. ICLR Hannah Lawrence, Mitchell Tong Harris
2024	A*	`conf`	On the hardness of learning under symmetries. ICLR Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber
2024	J	`jnl`	On the hardness of learning under symmetries. CoRR Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber
2024	—	`conf`	Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop. GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2024	—	`ed.`	Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 29 July 2024, Vienna, Austria GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2023	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR 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
2023	A*	`conf`	Distilling Model Failures as Directions in Latent Space. ICLR Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry
2023	J	`jnl`	Learning Polynomial Problems with SL(2, ℝ) Equivariance. CoRR Hannah Lawrence, Mitchell Tong Harris
2023	A*	`conf`	Self-Supervised Learning with Lie Symmetries for Partial Differential Equations. NeurIPS Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak T. Kiani
2023	J	`jnl`	Self-Supervised Learning with Lie Symmetries for Partial Differential Equations. CoRR Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak Toussi Kiani
2023	A*	`conf`	Toeplitz Low-Rank Approximation with Sublinear Query Complexity. SODA Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco, Kshiteej Sheth
2022	J	`jnl`	Distilling Model Failures as Directions in Latent Space. CoRR Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry
2022	A*	`conf`	GULP: a prediction-based metric between representations. NeurIPS Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet
2022	J	`jnl`	GULP: a prediction-based metric between representations. CoRR Enric Boix Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet
2022	A*	`conf`	Implicit Bias of Linear Equivariant Networks. ICML Hannah Lawrence, Bobak Toussi Kiani, Kristian G. Georgiev, Andrew K. Dienes
2022	J	`jnl`	Toeplitz Low-Rank Approximation with Sublinear Query Complexity. CoRR Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco, Kshiteej Sheth
2021	J	`jnl`	Implicit Bias of Linear Equivariant Networks. CoRR Hannah Lawrence, Kristian Georgiev, Andrew Dienes, Bobak Toussi Kiani
2021	—	`conf`	Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging. MSML Hannah Lawrence, David Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié
2020	Misc	`conf`	Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. ICASSP Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
2020	A*	`conf`	Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. NeurIPS Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi
2020	J	`jnl`	Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging. CoRR Hannah Lawrence, David A. Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié
2019	J	`jnl`	Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. CoRR Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
2019	J	`jnl`	Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. CoRR Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi