activity
20132022
most citedDSA: Decentralized Double Stochastic Averaging Gradient Algorithm

146 citations · 176 across the 17 of their papers we have counts for

collaborators
Showing 2018Show all

14 papers · 1 filter

cs.LG2018

Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series

Weiyu Huang, Yunlong Wang, Li Zhou +3

Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of…

cs.LG2018

Functional Nonlinear Sparse Models

Luiz F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro

Signal processing is rich in inherently continuous and often nonlinear applications, such as spectral estimation, optical imaging, and super-resolution microscopy, in which sparsit…

eess.SP2018

Connecting the Dots: Identifying Network Structure via Graph Signal Processing

Gonzalo Mateos, Santiago Segarra, Antonio G. Marques +1

Network topology inference is a prominent problem in Network Science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known, and then analy…

cs.LG2018

Median activation functions for graph neural networks

Luana Ruiz, Fernando Gama, Antonio G. Marques +1

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolu…

cs.LG2018

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

Majid Jahani, Xi He, Chenxin Ma +4

In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empiric…

math.OC2018

A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

We introduce the primal-dual quasi-Newton (PD-QN) method as an approximated second order method for solving decentralized optimization problems. The PD-QN method performs quasi-New…