Search Results for author:
Found 5 papers, 4 papers with code
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
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.
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
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.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
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.
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations.
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.
