12 citations · 14 across the 2 of their papers we have counts for
4 papers
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Yossi Arjevani, Joan Bruna, Bugra Can +3
We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of ap…
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions
Yossi Arjevani, Amit Daniely, Stefanie Jegelka +1
Recent advances in randomized incremental methods for minimizing -smooth -strongly convex finite sums have culminated in tight complexity of $\tilde{O}((n+\sqrt{n L/μ})\log(1…
Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions
Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka +2
We provide the first non-asymptotic analysis for finding stationary points of nonsmooth, nonconvex functions. In particular, we study the class of Hadamard semi-differentiable func…
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin, Stefanie Jegelka
We demonstrate that a very deep ResNet with stacked modules with one neuron per hidden layer and ReLU activation functions can uniformly approximate any Lebesgue integrable functio…