Diana Halikias

According to our database, Diana Halikias authored at least 10 papers between 2021 and 2026.

Collaborative distances:

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

2026

Attention Mechanisms Through the Lens of Numerical Methods: Approximation Methods and Alternative Formulations.

Michel Fabrice Serret, Alice Cortinovis, Yijun Dong, Diana Halikias, Anna Ma, Fabio Matti, Deanna Needell, Katherine J. Pearce, Elizaveta Rebrova, Disha Shur, Rudi Smith, Hai-Xiao Wang, Laura Grigori

CoRR, April, 2026

Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop.

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

CoRR, February, 2026

Fixed-Sparsity Matrix Approximation from Matrix-Vector Products.

Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco

SIAM J. Matrix Anal. Appl., 2026

2025

Quasi-optimal hierarchically semi-separable matrix approximation.

Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson

CoRR, May, 2025

Near-optimal hierarchical matrix approximation from matrix-vector products.

Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, Christopher Musco, David Persson

Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms, 2025

2024

Structured matrix recovery from matrix-vector products.

Diana Halikias, Alex Townsend

Numer. Linear Algebra Appl., January, 2024

Operator learning without the adjoint.

Nicolas Boullé, Diana Halikias, Samuel E. Otto, Alex Townsend

J. Mach. Learn. Res., 2024

2023

Elliptic PDE learning is provably data-efficient.

Nicolas Boullé, Diana Halikias, Alex Townsend

CoRR, 2023

2022

Matrix recovery from matrix-vector products.

Diana Halikias, Alex Townsend

CoRR, 2022

2021

Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks.

Annan Yu, Chloé Becquey, Diana Halikias, Matthew Esmaili Mallory, Alex Townsend

CoRR, 2021