Diana Halikias

12 papers A* 1Journal 11
YearRankTypeTitle / Venue / Authors
2026 J jnl
Fixed-Sparsity Matrix Approximation from Matrix-Vector Products.
SIAM J. Matrix Anal. Appl.
Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco
2026 J jnl
Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop.
CoRR
Noah Amsel, Yves Baumann, Paul Beckman, Peter Bürgisser, Chris Camaño, Tyler Chen, Edmond Chow, Anil Damle, Michal Derezinski, Mark Embree, Ethan N. Epperly, Robert D. Falgout, Mark Fornace, Anne Greenbaum, Chen Greif, Diana Halikias, Zhen Huang, Elias Jarlebring, Yiannis Koutis, Daniel Kressner, Rasmus Kyng, Jörg Liesen, Jackie Lok, Raphael A. Meyer, Yuji Nakatsukasa, Kate Pearce, Richard Peng, David Persson, Elizaveta Rebrova, Ryan Schneider, Rikhav Shah, Edgar Solomonik, Nikhil Srivastava, Alex Townsend, Robert J. Webber, Jess Williams
2025 A* conf
Near-optimal hierarchical matrix approximation from matrix-vector products.
SODA
Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson
2025 J jnl
Quasi-optimal hierarchically semi-separable matrix approximation.
CoRR
Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson
2024 J jnl
Fixed-sparsity matrix approximation from matrix-vector products.
CoRR
Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco
2024 J jnl
Near-optimal hierarchical matrix approximation from matrix-vector products.
CoRR
Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson
2024 J jnl
Operator learning without the adjoint.
CoRR
Nicolas Boullé, Diana Halikias, Samuel E. Otto, Alex Townsend
2024 J jnl
Operator learning without the adjoint.
J. Mach. Learn. Res.
Nicolas Boullé, Diana Halikias, Samuel E. Otto, Alex Townsend
2024 J jnl
Structured matrix recovery from matrix-vector products.
Numer. Linear Algebra Appl.
Diana Halikias, Alex Townsend
2023 J jnl
Elliptic PDE learning is provably data-efficient.
CoRR
Nicolas Boullé, Diana Halikias, Alex Townsend
2022 J jnl
Matrix recovery from matrix-vector products.
CoRR
Diana Halikias, Alex Townsend
2021 J jnl
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks.
CoRR
Annan Yu, Chloé Becquey, Diana Halikias, Matthew Esmaili Mallory, Alex Townsend