1 citations · 1 across the 9 of their papers we have counts for
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Are Greedy Task Orderings Better Than Random in Continual Linear Regression?
Matan Tsipory, Ran Levinstein, Itay Evron +3
We analyze task orderings in continual learning for linear regression, assuming joint realizability of training data. We focus on orderings that greedily maximize dissimilarity bet…
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…
A federated Kaczmarz algorithm
Halyun Jeong, Deanna Needell, Chi-Hao Wu
In this paper, we propose a federated algorithm for solving large linear systems that is inspired by the classic randomized Kaczmarz algorithm. We provide convergence guarantees of…
Cauchy Random Features for Operator Learning in Sobolev Space
Chunyang Liao, Deanna Needell, Hayden Schaeffer
Operator learning is the approximation of operators between infinite dimensional Banach spaces using machine learning approaches. While most progress in this area has been driven b…
Randomized Kaczmarz Methods with Beyond-Krylov Convergence
Michał Dereziński, Deanna Needell, Elizaveta Rebrova +1
Randomized Kaczmarz methods form a family of linear system solvers which converge by repeatedly projecting their iterates onto randomly sampled equations. While effective in some c…