72 citations · 542 across the 65 of their papers we have counts for
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cs.LG2018
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
Tayo Ajayi, David Mildebrath, Anastasios Kyrillidis +3
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with -norm loss. The purpose of this work…
cs.LG2018★ 4 cited
Simple and practical algorithms for -norm low-rank approximation
Anastasios Kyrillidis
We propose practical algorithms for entrywise -norm low-rank approximation, for or . The proposed framework, which is non-convex and gradient-based, is…
cs.LG2018★ 4 cited
Approximate Newton-based statistical inference using only stochastic gradients
Tianyang Li, Anastasios Kyrillidis, Liu Liu +1
We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion…