Search Results for author: Shaojie Tang

Found 49 papers, 6 papers with code

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

1 code implementation ICCV 2023 Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang

The reasoning behind this approach is understandable, as localizing parameters that are easily influenced by non-IID data can prevent the potential negative effect of collaboration.

Personalized Federated Learning

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

no code implementations11 Sep 2023 Shaojie Tang

The objective of a two-stage submodular maximization problem is to reduce the ground set using provided training functions that are submodular, with the aim of ensuring that optimizing new objective functions over the reduced ground set yields results comparable to those obtained over the original ground set.

Data Summarization

Non-monotone Sequential Submodular Maximization

no code implementations16 Aug 2023 Shaojie Tang, Jing Yuan

In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization.

Recommendation Systems

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

no code implementations26 Jul 2023 Guogang Zhu, Xuefeng Liu, Shaojie Tang, Jianwei Niu, Xinghao Wu, Jiaxing Shen

FedPick achieves PFL in the low-dimensional feature space by selecting task-relevant features adaptively for each client from the features generated by the global encoder based on its local data distribution.

Personalized Federated Learning

Unlocking the Potential of Federated Learning for Deeper Models

no code implementations5 Jun 2023 Haolin Wang, Xuefeng Liu, Jianwei Niu, Shaojie Tang, Jiaxing Shen

Our further investigation shows that the decline is due to the continuous accumulation of dissimilarities among client models during the layer-by-layer back-propagation process, which we refer to as "divergence accumulation."

Federated Learning

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

no code implementations13 Apr 2023 Shaojie Tang, Jing Yuan

Our problem involves a global utility function and a set of group utility functions for each group, here a group refers to a group of individuals (e. g., people) sharing the same attributes (e. g., gender).

Decision Making Fairness

Achieving Long-term Fairness in Submodular Maximization through Randomization

no code implementations10 Apr 2023 Shaojie Tang, Jing Yuan, Twumasi Mensah-Boateng

Unlike previous studies in this area, we allow for randomized solutions, with the objective being to calculate a distribution over feasible sets such that the expected number of items selected from each group is subject to constraints in the form of upper and lower thresholds, ensuring that the representation of each group remains balanced in the long term.

Data Summarization Fairness

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

no code implementations18 Mar 2023 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen

In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples.

Knowledge Distillation

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

no code implementations21 Feb 2023 Di Wang, Yao Wang, Shaojie Tang, Shao-Bo Lin

The novelties of our research are as follows: 1) From a methodological perspective, we present a novel and scalable approach for generating DTRs by combining distributed learning with Q-learning.

Learning Theory Medical Diagnosis +2

Group Fairness in Non-monotone Submodular Maximization

no code implementations3 Feb 2023 Jing Yuan, Shaojie Tang

Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker.

Data Summarization Fairness

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

no code implementations11 Nov 2022 Yikai Yan, Chaoyue Niu, Fan Wu, Qinya Li, Shaojie Tang, Chengfei Lyu, Guihai Chen

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 heterogeneous images from a small subset of classes to be recognized.

Worst-Case Adaptive Submodular Cover

no code implementations25 Oct 2022 Jing Yuan, Shaojie Tang

We also study a worst-case maximum-coverage problem, a dual problem of the minimum-cost-cover problem, whose goal is to select a group of items to maximize its worst-case utility subject to a budget constraint.

Active Learning

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

no code implementations21 Oct 2022 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen

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 mobile devices.

Click-Through Rate Prediction Recommendation Systems

Streaming Adaptive Submodular Maximization

no code implementations17 Aug 2022 Shaojie Tang, Jing Yuan

Many sequential decision making problems can be formulated as an adaptive submodular maximization problem.

Decision Making

Partial-Monotone Adaptive Submodular Maximization

no code implementations26 Jul 2022 Shaojie Tang, Jing Yuan

We show that a sampling-based policy achieves an approximation ratio of $(m+1)/10$ if the utility function is $m$-adaptive monotone and adaptive submodular.

Active Learning Decision Making +1

Group Equality in Adaptive Submodular Maximization

1 code implementation7 Jul 2022 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.

Data Summarization Fairness

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

no code implementations30 May 2022 Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen

Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.

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

no code implementations24 Jan 2022 Renjie Gu, Chaoyue Niu, Yikai Yan, Fan Wu, Shaojie Tang, Rongfeng Jia, Chengfei Lyu, Guihai Chen

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 each individual user's local data distribution.

Data Augmentation Recommendation Systems

Constrained Stochastic Submodular Maximization with State-Dependent Costs

no code implementations11 Nov 2021 Shaojie Tang

Recall that each item has a state-dependent cost, and the inner constraint states that the total \emph{realized} cost of all selected items must not exceed a give budget.

Partial-Adaptive Submodular Maximization

no code implementations1 Nov 2021 Shaojie Tang, Jing Yuan

Although this approach can take full advantage of feedback from the past to make informed decisions, it may take a longer time to complete the selection process as compared with the non-adaptive solution where all selections are made in advance before any observations take place.

Active Learning Decision Making

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

no code implementations30 Sep 2021 Shaojie Tang, Jing Yuan

We formulate this problem as a seed selection problem whose objective function is non-monotone and it might take on negative values, making existing results on submodular optimization and influence maximization not applicable to our setting.

Federated Submodel Optimization for Hot and Cold Data Features

1 code implementation16 Sep 2021 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lv, Yanghe Feng, Guihai Chen

We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.

Federated Learning

Data-Free Evaluation of User Contributions in Federated Learning

no code implementations24 Aug 2021 Hongtao Lv, Zhenzhe Zheng, Tie Luo, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv

We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.

Federated Learning Product Recommendation

Robust Adaptive Submodular Maximization

no code implementations23 Jul 2021 Shaojie Tang

For the worst-case adaptive submodular maximization problem subject to a $p$-system constraint, we develop an adaptive worst-case greedy policy that achieves a $\frac{1}{p+1}$ approximation ratio against the optimal worst-case utility if the utility function is worst-case submodular.

Active Learning Decision Making +1

Optimal Sampling Gaps for Adaptive Submodular Maximization

no code implementations5 Apr 2021 Shaojie Tang, Jing Yuan

Although the benefit of running machine learning algorithms on the reduced data set is obvious, one major concern is that the performance of the solution obtained from samples might be much worse than that of the optimal solution when using the full data set.

Active Learning BIG-bench Machine Learning +1

Adaptive Regularized Submodular Maximization

no code implementations28 Feb 2021 Shaojie Tang, Jing Yuan

For the case when $g$ is adaptive monotone and adaptive submodular, we develop an effective policy $\pi^l$ such that $g_{avg}(\pi^l) - c_{avg}(\pi^l) \geq (1-\frac{1}{e}-\epsilon)g_{avg}(\pi^o) - c_{avg}(\pi^o)$, using only $O(n\epsilon^{-2}\log \epsilon^{-1})$ value oracle queries.


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

no code implementations3 Feb 2021 Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou

In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.

Interactive Segmentation

Toward Understanding the Influence of Individual Clients in Federated Learning

no code implementations20 Dec 2020 Yihao Xue, Chaoyue Niu, Zhenzhe Zheng, Shaojie Tang, Chengfei Lv, Fan Wu, Guihai Chen

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.

Federated Learning

Adaptive Submodular Meta-Learning

no code implementations11 Dec 2020 Shaojie Tang, Jing Yuan

Our objective is to adaptively select a group of items that achieve the best performance over a set of tasks, where each task is represented as an adaptive submodular function that maps sets of items and their states to a real number.


SelectScale: Mining More Patterns from Images via Selective and Soft Dropout

no code implementations30 Nov 2020 Zhengsu Chen, Jianwei Niu, Xuefeng Liu, Shaojie Tang

Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training.

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

no code implementations11 Aug 2020 Shaojie Tang

Our first contribution is to show that the adaptive random greedy algorithm achieves a $1/e$ approximation ratio under adaptive submodularity.

Linear-Time Algorithms for Adaptive Submodular Maximization

no code implementations8 Jul 2020 Shaojie Tang

We start with the well-studied adaptive submodular maximization problem subject to a cardinality constraint.

Adaptive Cascade Submodular Maximization

no code implementations7 Jul 2020 Shaojie Tang, Jing Yuan

The input of our problem is a set of items, each item is in a particular state (i. e., the marginal contribution of an item) which is drawn from a known probability distribution.

A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network

no code implementations20 Jun 2020 Xuan Hua, Jungang Han, Chen Zhao, Haipeng Tang, Zhuo He, Jinshan Tang, Qing-Hui Chen, Shaojie Tang, Weihua Zhou

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals.

Classification General Classification

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

no code implementations25 Apr 2020 Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.

Anomaly Detection Organ Segmentation +1

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

1 code implementation18 Feb 2020 Yikai Yan, Chaoyue Niu, Yucheng Ding, Zhenzhe Zheng, Fan Wu, Guihai Chen, Shaojie Tang, Zhihua Wu

In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process.

Benchmarking Federated Learning

Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost

no code implementations13 Feb 2020 Shaojie Tang, Jing Yuan

After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform.

Online Pricing with Reserve Price Constraint for Personal Data Markets

1 code implementation28 Nov 2019 Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Shaojie Tang, Guihai Chen

The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.

Secure Federated Submodel Learning

1 code implementation6 Nov 2019 Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, Guihai Chen

Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.

Federated Learning

Stochastic Submodular Probing with State-Dependent Costs

no code implementations1 Sep 2019 Shaojie Tang

The only way to know the realized state of an item is to probe that item.

Adaptive Robust Optimization with Nearly Submodular Structure

no code implementations14 May 2019 Shaojie Tang, Jing Yuan

Then we propose a approximate solution to this problem when all reward functions are submodular.

Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz

no code implementations23 Jan 2019 Shaojie Tang, Jing Yuan

Note that under the our model, the probability of a question being answered depends on the location of that question, as well as the set of other questions placed ahead of that question, this makes our problem fundamentally different from existing studies on submodular optimization.

Active Learning Question Selection

No Time to Observe: Adaptive Influence Maximization with Partial Feedback

no code implementations1 Sep 2016 Jing Yuan, Shaojie Tang

In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed.

Social and Information Networks

Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies

no code implementations20 Mar 2015 Shaojie Tang, Yaqin Zhou

In particular, we consider the setting of a decision maker over a networked bandits as follows: each time a combinatorial strategy, e. g., a group of arms, is chosen, and the decision maker receives a reward resulting from her strategy and also receives a side bonus resulting from that strategy for each arm's neighbor.

Multi-Armed Bandits

Towards Distribution-Free Multi-Armed Bandits with Combinatorial Strategies

no code implementations20 Jul 2013 Xiang-Yang Li, Shaojie Tang, Yaqin Zhou

At each decision epoch, we select a strategy, i. e., a subset of RVs, subject to arbitrary constraints on constituent RVs.

Multi-Armed Bandits

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