Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. - Hannah Lawrence; Jerry Li 001; Cameron Musco; Christopher Musco (ICASSP, 2020)

Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. - Lin Chen 003; Qian Yu 001; Hannah Lawrence; Amin Karbasi (NeurIPS, 2020)

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 (MSML, 2021)

Implicit Bias of Linear Equivariant Networks. - Hannah Lawrence; Bobak Toussi Kiani; Kristian G. Georgiev; Andrew K. Dienes (ICML, 2022)

GULP: a prediction-based metric between representations. - Enric Boix-Adserà; Hannah Lawrence; George Stepaniants; Philippe Rigollet (NeurIPS, 2022)

Distilling Model Failures as Directions in Latent Space. - Saachi Jain; Hannah Lawrence; Ankur Moitra; Aleksander Madry (ICLR, 2023)

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

Toeplitz Low-Rank Approximation with Sublinear Query Complexity. - Michael Kapralov; Hannah Lawrence; Mikhail Makarov; Cameron Musco; Kshiteej Sheth (SODA, 2023)

On the hardness of learning under symmetries. - Bobak T. Kiani; Thien Le; Hannah Lawrence; Stefanie Jegelka; Melanie Weber 001 (ICLR, 2024)

Learning Polynomial Problems with SL(2, R)-Equivariance. - Hannah Lawrence; Mitchell Tong Harris (ICLR, 2024)

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

Improving Equivariant Networks with Probabilistic Symmetry Breaking. - Hannah Lawrence; Vasco Portilheiro; Yan Zhang 067; Sékou-Oumar Kaba (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)