314 citations · 862 across the 65 of their papers we have counts for
67 papers
Non-monotone Sequential Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization. Specifically, we aim to select and rank a group of …
Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints
Shaojie Tang, Jing Yuan
Machine learning algorithms play an important role in a variety of important decision-making processes, including targeted advertisement displays, home loan approvals, and criminal…
Achieving Long-term Fairness in Submodular Maximization through Randomization
Shaojie Tang, Jing Yuan, Twumasi Mensah-Boateng
Submodular function optimization has numerous applications in machine learning and data analysis, including data summarization which aims to identify a concise and diverse set of d…
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…
Group Fairness in Non-monotone Submodular Maximization
Jing Yuan, Shaojie Tang
Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small se…
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
Di Wang, Yao Wang, Shao-Bo Lin
In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases have been collected to facilitate medical diagnosis. Modeling the dynamic properties…
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…
Worst-Case Adaptive Submodular Cover
Jing Yuan, Shaojie Tang
In this paper, we study the adaptive submodular cover problem under the worst-case setting. This problem generalizes many previously studied problems, namely, the pool-based active…
Streaming Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setti…
Partial-Monotone Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Many sequential decision making problems, including pool-based active learning and adaptive viral marketing, can be formulated as an adaptive submodular maximization problem. Most…
Group Equality in Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the…
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu +17
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system,…
On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems
Renjie Gu, Chaoyue Niu, Yikai Yan +5
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to eac…
Constrained Stochastic Submodular Maximization with State-Dependent Costs
Shaojie Tang
In this paper, we study the constrained stochastic submodular maximization problem with state-dependent costs. The input of our problem is a set of items whose states (i.e., the ma…
Partial-Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
The goal of a typical adaptive sequential decision making problem is to design an interactive policy that selects a group of items sequentially, based on some partial observations,…
Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising
Shaojie Tang, Jing Yuan
The idea of social advertising (or social promotion) is to select a group of influential individuals (a.k.a \emph{seeds}) to help promote some products or ideas through an online s…
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…
Improved Parallel Algorithm for Minimum Cost Submodular Cover Problem
Yingli Ran, Zhao Zhang, Shaojie Tang
In the minimum cost submodular cover problem (MinSMC), we are given a monotone nondecreasing submodular function , a linear cost function $c:…
Robust Adaptive Submodular Maximization
Shaojie Tang
The goal of a sequential decision making problem is to design an interactive policy that adaptively selects a group of items, each selection is based on the feedback from the past,…
Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to Knapsack and -System Constraints
Shaojie Tang
In this paper, we study the non-monotone adaptive submodular maximization problem subject to a knapsack and a -system constraints. The input of our problem is a set of items, wh…
Optimal Sampling Gaps for Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Running machine learning algorithms on large and rapidly growing volumes of data is often computationally expensive, one common trick to reduce the size of a data set, and thus red…
Adaptive Regularized Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study the problem of maximizing the difference between an adaptive submodular (revenue) function and an non-negative modular (cost) function under the adaptive se…