170 citations · 395 across the 10 of their papers we have counts for
10 papers
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
Qingyi Wang, Shenhao Wang, Yunhan Zheng +4
Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. Ho…
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…
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
Jingzhao Zhang, Hongzhou Lin, Subhro Das +2
We study oracle complexity of gradient based methods for stochastic approximation problems. Though in many settings optimal algorithms and tight lower bounds are known for such pro…
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…
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact accelerated prox…
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy +2
We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these method…
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms. The proposed scheme, called QNing can be notably applied to inc…
A Universal Catalyst for First-Order Optimization
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorit…