1 citations · 1 across the 3 of their papers we have counts for
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On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery
Shambhavi Suryanarayanan, Elizaveta Rebrova
In this paper, we take a step towards developing efficient hard thresholding methods for low-rank tensor recovery from memory-efficient linear measurements with tensorial structure…
Towards Universal Convergence of Backward Error in Linear System Solvers
MichaÅ DereziÅski, Yuji Nakatsukasa, Elizaveta Rebrova
The quest for an algorithm that solves an linear system in time complexity, or when solving up to relative error, is a long-sta…
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…
Quantile Randomized Kaczmarz Algorithm with Whitelist Trust Mechanism
Sofiia Shvaiko, Longxiu Huang, Elizaveta Rebrova
Randomized Kaczmarz (RK) is a simple and fast solver for consistent overdetermined systems, but it is known to be fragile under noise. We study overdetermined linear sy…
Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations
Jackie Lok, Elizaveta Rebrova
The randomized coordinate descent (RCD) method is a classical algorithm with simple, lightweight iterations that is widely used for various optimization problems, including the sol…
Beyond Expectation: Concentration Inequalities for Randomized Iterative Methods
Toby Anderson, Max Collins, Jamie Haddock +2
Stochastic iterative methods are useful in a variety of large-scale numerical linear algebraic, machine learning, and statistical problems, in part due to their low-memory footprin…