Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks. - Annan Yu; Chloé Becquey; Diana Halikias; Matthew Esmaili Mallory; Alex Townsend (CoRR, 2021)

Matrix recovery from matrix-vector products. - Diana Halikias; Alex Townsend (CoRR, 2022)

Elliptic PDE learning is provably data-efficient. - Nicolas Boullé; Diana Halikias; Alex Townsend (CoRR, 2023)

Operator learning without the adjoint. - Nicolas Boullé; Diana Halikias; Samuel E. Otto; Alex Townsend (CoRR, 2024)

Fixed-sparsity matrix approximation from matrix-vector products. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco (CoRR, 2024)

Structured matrix recovery from matrix-vector products. - Diana Halikias; Alex Townsend (Numer. Linear Algebra Appl., 2024)

Near-optimal hierarchical matrix approximation from matrix-vector products. - Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (SODA, 2025)

Quasi-optimal hierarchically semi-separable matrix approximation. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)