57 citations · 143 across the 11 of their papers we have counts for
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math.OC2019★ 21 cited
Complexity of Highly Parallel Non-Smooth Convex Optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee +2
A landmark result of non-smooth convex optimization is that gradient descent is an optimal algorithm whenever the number of computed gradients is smaller than the dimension . In…
math.OC2018★ 30 cited
Near-optimal method for highly smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee +2
We propose a near-optimal method for highly smooth convex optimization. More precisely, in the oracle model where one obtains the order Taylor expansion of a function at t…
math.OC2017
An homotopy method for regression provably beyond self-concordance and in input-sparsity time
Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee +1
We consider the problem of linear regression where the norm loss (i.e., the usual least squares loss) is replaced by the norm. We show how to solve such probl…