103 citations · 365 across the 26 of their papers we have counts for
26 papers
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models
Yucheng Ding, Chaoyue Niu, Fan Wu +3
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage th…
Truthful Auctions for Automated Bidding in Online Advertising
Yidan Xing, Zhilin Zhang, Zhenzhe Zheng +4
Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate…
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
Yikai Yan, Chaoyue Niu, Fan Wu +4
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with h…
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
Yucheng Ding, Chaoyue Niu, Fan Wu +3
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mob…
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
Chen Gong, Zhenzhe Zheng, Yunfeng Shao +3
Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To…
Federated Submodel Optimization for Hot and Cold Data Features
Yucheng Ding, Chaoyue Niu, Fan Wu +4
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves…
Data-Free Evaluation of User Contributions in Federated Learning
Hongtao Lv, Zhenzhe Zheng, Tie Luo +5
Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device's private data and computing resources. A critical issues is to…
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang +10
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mecha…
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Liyi Guo, Junqi Jin, Haoqi Zhang +10
Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial a…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng +7
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform…
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
Liyi Guo, Rui Lu, Haoqi Zhang +9
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and sho…
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
Yuben Qu, Chao Dong, Jianchao Zheng +4
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the…
UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks
Chao Dong, Yun Shen, Yuben Qu +3
The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to i…
Distributed Optimization over Block-Cyclic Data
Yucheng Ding, Chaoyue Niu, Yikai Yan +5
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…
Online Pricing with Reserve Price Constraint for Personal Data Markets
Chaoyue Niu, Zhenzhe Zheng, Fan Wu +2
The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by…
Mechanism Design with Predicted Task Revenue for Bike Sharing Systems
Hongtao Lv, Chaoli Zhang, Zhenzhe Zheng +3
Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply i…
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey
Renjie Gu, Chaoyue Niu, Fan Wu +4
In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning…
Achieving Data Truthfulness and Privacy Preservation in Data Markets
Chaoyue Niu, Zhenzhe Zheng, Fan Wu +2
As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data f…
Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing
Shuo Yang, Zhenzhe Zheng, Shaojie Tang +2
In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assu…
ERA: Towards Privacy Preservation and Verifiability for Online Ad Exchanges
Chaoyue Niu, Minping Zhou, Zhenzhe Zheng +2
Ad exchanges are kind of the most popular online advertising marketplaces for trading ad spaces over the Internet. Ad exchanges run auctions to sell diverse ad spaces on the publis…
Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems
Tony T. Luo, Salil S. Kanhere, Jianwei Huang +2
Proper incentive mechanisms are critical for mobile crowdsensing systems to motivate people to actively and persistently participate. This article provides an exposition of design…