25 citations · 55 across the 22 of their papers we have counts for
27 papers
Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent
Jamie Haddock, Anna Ma, Elizaveta Rebrova
We study loss-based filtering for finite-sum optimization with a subset of corrupted component functions whose gradients may be highly unreliable. Motivated by minimum-loss-based S…
Attention Mechanisms Through the Lens of Numerical Methods: Approximation Methods and Alternative Formulations
Michel Fabrice Serret, Alice Cortinovis, Yijun Dong +10
The attention mechanism is the computational core of modern Transformer architectures, but its quadratic complexity in the input sequence length is the bottleneck for large-scale i…
Stochastic Gradient Descent for Incomplete Tensor Linear Systems
Anna Ma, Deanna Needell, Alexander Xue
Solving large tensor linear systems poses significant challenges due to the high volume of data stored, and it only becomes more challenging when some of the data is missing. Recen…
On Subsampled Quantile Randomized Kaczmarz
Jamie Haddock, Anna Ma, Elizaveta Rebrova
When solving noisy linear systems Ax = b + c, the theoretical and empirical performance of stochastic iterative methods, such as the Randomized Kaczmarz algorithm, depends on the n…
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
El Houcine Bergou, Soumia Boucherouite, Aritra Dutta +2
Large-scale linear systems, , frequently arise in practice and demand effective iterative solvers. Often, these systems are noisy due to operational errors or faulty data-col…
Stochastic Natural Thresholding Algorithms
Rachel Grotheer, Shuang Li, Anna Ma +2
Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of…