no code implementations • 9 Oct 2023 • Cong Ma, Xingyu Xu, Tian Tong, Yuejie Chi
Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e. g., matrices and tensors) from incomplete, and possibly corrupted, linear measurements.
1 code implementation • 18 Jun 2022 • Harry Dong, Tian Tong, Cong Ma, Yuejie Chi
An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than matrices.
1 code implementation • 29 Apr 2021 • Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi
Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering.
2 code implementations • 26 Oct 2020 • Tian Tong, Cong Ma, Yuejie Chi
Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations.
2 code implementations • 18 May 2020 • Tian Tong, Cong Ma, Yuejie Chi
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.