b'Shaojie Tang'

research

∙ 09/20/2023

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

Personalized federated learning (PFL) reduces the impact of non-independ...

0 Xinghao Wu, et al. ∙

research

∙ 09/11/2023

Data Summarization beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization

The objective of a two-stage submodular maximization problem is to reduc...

0 Shaojie Tang, et al. ∙

research

∙ 08/16/2023

Non-monotone Sequential Submodular Maximization

In this paper, we study a fundamental problem in submodular optimization...

0 Shaojie Tang, et al. ∙

research

∙ 07/26/2023

Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space

Personalized federated learning (PFL) is a popular framework that allows...

0 Guogang Zhu, et al. ∙

research

∙ 06/05/2023

Unlocking the Potential of Federated Learning for Deeper Models

Federated learning (FL) is a new paradigm for distributed machine learni...

0 Haolin Wang, et al. ∙

research

∙ 04/13/2023

Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints

Machine learning algorithms play an important role in a variety of impor...

0 Shaojie Tang, et al. ∙

research

∙ 04/10/2023

Achieving Long-term Fairness in Submodular Maximization through Randomization

Submodular function optimization has numerous applications in machine le...

0 Shaojie Tang, et al. ∙

research

∙ 03/18/2023

DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models

Many large vision models have been deployed on the cloud for real-time s...

0 Yucheng Ding, et al. ∙

research

∙ 02/21/2023

Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes

In recent years, large amounts of electronic health records (EHRs) conce...

0 Di Wang, et al. ∙

research

∙ 02/03/2023

Group Fairness in Non-monotone Submodular Maximization

Maximizing a submodular function has a wide range of applications in mac...

0 Jing Yuan, et al. ∙

research

∙ 01/23/2023

A New Approximation Algorithm for Minimum-Weight (1,m)–Connected Dominating Set

Consider a graph with nonnegative node weight. A vertex subset is called...

0 Jiao Zhou, et al. ∙

research

∙ 11/11/2022

One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design

The mainstream workflow of image recognition applications is first train...

0 Yikai Yan, et al. ∙

research

∙ 10/25/2022

Worst-Case Adaptive Submodular Cover

In this paper, we study the adaptive submodular cover problem under the ...

0 Jing Yuan, et al. ∙

research

∙ 10/21/2022

On-Device Model Fine-Tuning with Label Correction in Recommender Systems

To meet the practical requirements of low latency, low cost, and good pr...

0 Yucheng Ding, et al. ∙

research

∙ 10/03/2022

Evolution is Still Good: Theoretical Analysis of Evolutionary Algorithms on General Cover Problems

Theoretical studies on evolutionary algorithms have developed vigorously...

0 Yaoyao Zhang, et al. ∙

research

∙ 08/17/2022

Streaming Adaptive Submodular Maximization

Many sequential decision making problems can be formulated as an adaptiv...

0 Shaojie Tang, et al. ∙

research

∙ 07/26/2022

Partial-Monotone Adaptive Submodular Maximization

Many sequential decision making problems, including pool-based active le...

0 Shaojie Tang, et al. ∙

research

∙ 07/07/2022

Group Fairness in Adaptive Submodular Maximization

In this paper, we study the classic submodular maximization problem subj...

0 Shaojie Tang, et al. ∙

research

∙ 06/07/2022

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

Background: The assessment of left ventricular (LV) function by myocardi...

9 Fubao Zhu, et al. ∙

research

∙ 05/30/2022

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

To break the bottlenecks of mainstream cloud-based machine learning (ML)...

0 Chengfei Lv, et al. ∙

research

∙ 01/24/2022

On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems

Data heterogeneity is an intrinsic property of recommender systems, maki...

0 Renjie Gu, et al. ∙

research

∙ 11/11/2021

Constrained Stochastic Submodular Maximization with State-Dependent Costs

In this paper, we study the constrained stochastic submodular maximizati...

0 Shaojie Tang, et al. ∙

research

∙ 11/01/2021

Partial-Adaptive Submodular Maximization

The goal of a typical adaptive sequential decision making problem is to ...

0 Shaojie Tang, et al. ∙

research

∙ 09/30/2021

Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising

The idea of social advertising (or social promotion) is to select a grou...

0 Shaojie Tang, et al. ∙

research

∙ 09/16/2021

Federated Submodel Averaging

We study practical data characteristics underlying federated learning, w...

0 Yucheng Ding, et al. ∙

research

∙ 08/24/2021

Data-Free Evaluation of User Contributions in Federated Learning

Federated learning (FL) trains a machine learning model on mobile device...

0 Hongtao Lv, et al. ∙

research

∙ 07/23/2021

Robust Adaptive Submodular Maximization

Most of existing studies on adaptive submodular optimization focus on th...

0 Shaojie Tang, et al. ∙

research

∙ 04/10/2021

Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to a Knapsack Constraint

In this paper, we study the non-monotone adaptive submodular maximizatio...

0 Shaojie Tang, et al. ∙

research

∙ 04/05/2021

Optimal Sampling Gaps for Adaptive Submodular Maximization

Running machine learning algorithms on large and rapidly growing volumes...

0 Shaojie Tang, et al. ∙

research

∙ 02/28/2021

Adaptive Regularized Submodular Maximization

In this paper, we study the problem of maximizing the difference between...

0 Shaojie Tang, et al. ∙

research

∙ 02/03/2021

A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images

Automatic CT segmentation of proximal femur is crucial for the diagnosis...

11 Yu Deng, et al. ∙

research

∙ 12/20/2020

Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model...

0 Yihao Xue, et al. ∙

research

∙ 12/11/2020

Adaptive Submodular Meta-Learning

Meta-Learning has gained increasing attention in the machine learning an...

0 Shaojie Tang, et al. ∙

research

∙ 08/11/2020

Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization in Linear Time

In this paper, we study the non-monotone adaptive submodular maximizatio...

0 Shaojie Tang, et al. ∙

research

∙ 07/08/2020

Linear-Time Algorithms for Adaptive Submodular Maximization

In this paper, we develop fast algorithms for two stochastic submodular ...

0 Shaojie Tang, et al. ∙

research

∙ 07/07/2020

Adaptive Cascade Submodular Maximization

In this paper, we propose and study the cascade submodular maximization ...

0 Shaojie Tang, et al. ∙

research

∙ 04/25/2020

A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis

Although deep learning models like CNNs have achieved a great success in...

0 Xiaozheng Xie, et al. ∙

research

∙ 02/18/2020

Distributed Optimization over Block-Cyclic Data

We consider practical data characteristics underlying federated learning...

0 Yucheng Ding, et al. ∙

research

∙ 02/18/2020

Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability

Federated learning is a new distributed machine learning framework, wher...

0 Yikai Yan, et al. ∙

research

∙ 02/13/2020

Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost

In this paper, we study the assortment optimization problem faced by man...

0 Shaojie Tang, et al. ∙

research

∙ 11/28/2019

Online Pricing with Reserve Price Constraint for Personal Data Markets

The society's insatiable appetites for personal data are driving the eme...

0 Chaoyue Niu, et al. ∙

research

∙ 11/28/2019

Product Sequencing and Pricing under Cascade Browse Model

In this paper, we study the joint product sequencing and pricing problem...

0 Shaojie Tang, et al. ∙

research

∙ 11/06/2019

Secure Federated Submodel Learning

Federated learning was proposed with an intriguing vision of achieving c...

0 Chaoyue Niu, et al. ∙

research

∙ 09/01/2019

Stochastic Submodular Probing with State-Dependent Costs

In this paper, we study a new stochastic submodular maximization problem...

0 Shaojie Tang, et al. ∙

research

∙ 05/23/2019

Price of Dependence: Stochastic Submodular Maximization with Dependent Items

In this paper, we study the stochastic submodular maximization problem w...

0 Shaojie Tang, et al. ∙

research

∙ 05/14/2019

Adaptive Robust Optimization with Nearly Submodular Structure

Constrained submodular maximization has been extensively studied in the ...

0 Shaojie Tang, et al. ∙

research

∙ 01/23/2019

Submodular Maximization under Fading Model: Building Online Quizzes for Better Customer Segmentation

E-Commerce personalization aims to provide individualized offers, produc...

0 Shaojie Tang, et al. ∙

research

∙ 11/14/2018

Fine-Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing

In mobile crowdsensing, finding the best match between tasks and users i...

0 Shuo Yang, et al. ∙

research

∙ 07/07/2018

Stochastic Coupon Probing in Social Networks

In this paper, we study stochastic coupon probing problem in social netw...

0 Shaojie Tang, et al. ∙

research

∙ 04/16/2018

Towards Robust Monitoring of Stealthy Diffusion

In this work, we introduce and study the (α, β)-Monitoring game on netwo...

0 Shaojie Tang, et al. ∙

Shaojie Tang

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