activity
20192022
most citedMisGAN: Learning from Incomplete Data with Generative Adversarial Networks

59 citations · 89 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2022★ 13 cited

FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models

Yunzhuo Liu, Bo Jiang, Tian Guo +4

Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, system…

cs.LG2021★ 1 cited

Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network

Yichen Zhu, Bo Jiang, Haiming Jin +5

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment moni…

cs.SI2020★ 16 cited

Neighborhood Matters: Influence Maximization in Social Networks with Limited Access

Chen Feng, Luoyi Fu, Bo Jiang +4

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user's privacy conc…

cs.SI2019

Information Source Detection with Limited Time Knowledge

Xuecheng Liu, Luoyi Fu, Bo Jiang +2

This paper investigates the problem of utilizing network topology and partial timestamps to detect the information source in a network. The problem incurs prohibitive cost under ca…

cs.LG2019★ 59 cited

MisGAN: Learning from Incomplete Data with Generative Adversarial Networks

Steven Cheng-Xian Li, Bo Jiang, Benjamin Marlin

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.…