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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...
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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...
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08/16/2023
Non-monotone Sequential Submodular Maximization
In this paper, we study a fundamental problem in submodular optimization...
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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...
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06/05/2023
Unlocking the Potential of Federated Learning for Deeper Models
Federated learning (FL) is a new paradigm for distributed machine learni...
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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...
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04/10/2023
Achieving Long-term Fairness in Submodular Maximization through Randomization
Submodular function optimization has numerous applications in machine le...
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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...
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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...
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02/03/2023
Group Fairness in Non-monotone Submodular Maximization
Maximizing a submodular function has a wide range of applications in mac...
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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...
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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...
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10/25/2022
Worst-Case Adaptive Submodular Cover
In this paper, we study the adaptive submodular cover problem under the ...
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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...
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10/03/2022
Evolution is Still Good: Theoretical Analysis of Evolutionary Algorithms on General Cover Problems
Theoretical studies on evolutionary algorithms have developed vigorously...
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08/17/2022
Streaming Adaptive Submodular Maximization
Many sequential decision making problems can be formulated as an adaptiv...
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07/26/2022
Partial-Monotone Adaptive Submodular Maximization
Many sequential decision making problems, including pool-based active le...
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07/07/2022
Group Fairness in Adaptive Submodular Maximization
In this paper, we study the classic submodular maximization problem subj...
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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...
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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)...
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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...
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11/11/2021
Constrained Stochastic Submodular Maximization with State-Dependent Costs
In this paper, we study the constrained stochastic submodular maximizati...
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11/01/2021
Partial-Adaptive Submodular Maximization
The goal of a typical adaptive sequential decision making problem is to ...
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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...
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09/16/2021
Federated Submodel Averaging
We study practical data characteristics underlying federated learning, w...
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08/24/2021
Data-Free Evaluation of User Contributions in Federated Learning
Federated learning (FL) trains a machine learning model on mobile device...
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07/23/2021
Robust Adaptive Submodular Maximization
Most of existing studies on adaptive submodular optimization focus on th...
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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...
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04/05/2021
Optimal Sampling Gaps for Adaptive Submodular Maximization
Running machine learning algorithms on large and rapidly growing volumes...
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02/28/2021
Adaptive Regularized Submodular Maximization
In this paper, we study the problem of maximizing the difference between...
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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...
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12/20/2020
Toward Understanding the Influence of Individual Clients in Federated Learning
Federated learning allows mobile clients to jointly train a global model...
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12/11/2020
Adaptive Submodular Meta-Learning
Meta-Learning has gained increasing attention in the machine learning an...
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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...
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07/08/2020
Linear-Time Algorithms for Adaptive Submodular Maximization
In this paper, we develop fast algorithms for two stochastic submodular ...
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07/07/2020
Adaptive Cascade Submodular Maximization
In this paper, we propose and study the cascade submodular maximization ...
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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...
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02/18/2020
Distributed Optimization over Block-Cyclic Data
We consider practical data characteristics underlying federated learning...
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02/18/2020
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Federated learning is a new distributed machine learning framework, wher...
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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...
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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...
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11/28/2019
Product Sequencing and Pricing under Cascade Browse Model
In this paper, we study the joint product sequencing and pricing problem...
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11/06/2019
Secure Federated Submodel Learning
Federated learning was proposed with an intriguing vision of achieving c...
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09/01/2019
Stochastic Submodular Probing with State-Dependent Costs
In this paper, we study a new stochastic submodular maximization problem...
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05/23/2019
Price of Dependence: Stochastic Submodular Maximization with Dependent Items
In this paper, we study the stochastic submodular maximization problem w...
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05/14/2019
Adaptive Robust Optimization with Nearly Submodular Structure
Constrained submodular maximization has been extensively studied in the ...
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01/23/2019
Submodular Maximization under Fading Model: Building Online Quizzes for Better Customer Segmentation
E-Commerce personalization aims to provide individualized offers, produc...
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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...
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07/07/2018
Stochastic Coupon Probing in Social Networks
In this paper, we study stochastic coupon probing problem in social netw...
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04/16/2018