activity
20182020
most citedA Unified Framework for Marketing Budget Allocation

2 citations · 6 across the 3 of their papers we have counts for

collaborators

6 papers

cs.RO20202 cited

Multi-Agent Coverage in Urban Environments

Shivang Patel, Senthil Hariharan, Pranav Dhulipala +4

We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments. We consider scenarios in which a team of flying agents uses downward facing cam…

cs.IR20202 cited

Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective

Yifei Zhao, Yu-Hang Zhou, Mingdong Ou +2

To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursui…

cs.LG2020

RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection

Qingsong Wen, Kai He, Liang Sun +3

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database mana…

cs.DS20192 cited

A Unified Framework for Marketing Budget Allocation

Kui Zhao, Junhao Hua, Ling Yan +3

While marketing budget allocation has been studied for decades in traditional business, nowadays online business brings much more challenges due to the dynamic environment and comp…

cs.LG2018

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Qingsong Wen, Jingkun Gao, Xiaomin Song +3

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerou…

eess.SP2018

A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring

Chong Zhang, Geok Soon Hong, Jun-Hong Zhou +5

In this paper, a multi-state diagnosis and prognosis (MDP) framework is proposed for tool condition monitoring via a deep belief network based multi-state approach (DBNMS). For fau…