Search Results for author: Han Yu

Found 51 papers, 17 papers with code

NICO++: Towards Better Benchmarking for Domain Generalization

1 code implementation17 Apr 2022 Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui

Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.

Domain Generalization Generalization Bounds +1

Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild

no code implementations22 Feb 2022 Han Yu, Akane Sano

We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models.

Data Augmentation

More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors

1 code implementation16 Feb 2022 Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano

For example, although combining bio-signals from multiple sensors (i. e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context.

Transfer Learning

Federated Graph Neural Networks: Overview, Techniques and Challenges

no code implementations15 Feb 2022 Rui Liu, Han Yu

We propose a unique 3-tiered taxonomy of the FedGNNs literature to provide a clear view into how GNNs work in the context of Federated Learning (FL).

Federated Learning

Towards Verifiable Federated Learning

no code implementations15 Feb 2022 Yanci Zhang, Han Yu

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models.

Federated Learning

Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training

no code implementations31 Jan 2022 Shenglai Zeng, Zonghang Li, Hongfang Yu, Yihong He, Zenglin Xu, Dusit Niyato, Han Yu

In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.

Federated Learning

Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection

no code implementations3 Jan 2022 Yuxin Zhang, Jindong Wang, Yiqiang Chen, Han Yu, Tao Qin

In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection.

Self-Supervised Learning Sleep Stage Detection +2

Automatic Product Copywriting for E-Commerce

no code implementations15 Dec 2021 Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu, Lingfei Wu

It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.

Product Recommendation Text Generation

A Survey of Fairness-Aware Federated Learning

no code implementations2 Nov 2021 Yuxin Shi, Han Yu, Cyril Leung

Recent advances in Federated Learning (FL) have brought large-scale machine learning opportunities for massive distributed clients with performance and data privacy guarantees.

Fairness Federated Learning

MarS-FL: Enabling Competitors to Collaborate in Federated Learning

no code implementations26 Oct 2021 Xiaohu Wu, Han Yu

A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness.

Decision Making Federated Learning

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

1 code implementation5 Sep 2021 Zelei Liu, YuanYuan Chen, Han Yu, Yang Liu, Lizhen Cui

In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings.

Federated Learning

Towards Out-Of-Distribution Generalization: A Survey

no code implementations31 Aug 2021 Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui

Classic machine learning methods are built on the $i. i. d.$ assumption that training and testing data are independent and identically distributed.

Out-of-Distribution Generalization Representation Learning

Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering

no code implementations3 Aug 2021 Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao

Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks.

Metric Learning

Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild

1 code implementation19 Jul 2021 Han Yu, Thomas Vaessen, Inez Myin-Germeys, Akane Sano

Compared to the baseline method using the samples with complete modalities, the performance of the MFN improved by 1. 6% in f1-scores.

Transfer Learning

Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network

1 code implementation22 Jun 2021 Han Yu, Asami Itoh, Ryota Sakamoto, Motomu Shimaoka, Akane Sano

According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status.

Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection

1 code implementation NAACL 2021 Xu Guo, Boyang Li, Han Yu, Chunyan Miao

The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.

Meta-Learning Sarcasm Detection +1

Towards Personalized Federated Learning

no code implementations1 Mar 2021 Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang

In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy.

Personalized Federated Learning

HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

1 code implementation4 Feb 2021 YuanYuan Chen, Boyang Li, Han Yu, Pengcheng Wu, Chunyan Miao

the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory.

Privacy and Robustness in Federated Learning: Attacks and Defenses

no code implementations7 Dec 2020 Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.

Federated Learning

Federated Learning for Personalized Humor Recognition

no code implementations3 Dec 2020 Xu Guo, Han Yu, Boyang Li, Hao Wang, Pengwei Xing, Siwei Feng, Zaiqing Nie, Chunyan Miao

In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).

Federated Learning Language Modelling

Federated Crowdsensing: Framework and Challenges

no code implementations6 Nov 2020 Leye Wang, Han Yu, Xiao Han

In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.

Federated Learning

A VCG-based Fair Incentive Mechanism for Federated Learning

no code implementations15 Aug 2020 Mingshu Cong, Han Yu, Xi Weng, Jiabao Qu, Yang Liu, Siu Ming Yiu

In order to build an ecosystem for FL to operate in a sustainable manner, it has to be economically attractive to data owners.

Computer Science and Game Theory

A Robust Spearman Correlation Coefficient Permutation Test

1 code implementation3 Aug 2020 Han Yu, Alan D. Hutson

In general, there is common misconception that the tests about $\rho_s=0$ are robust to deviations from bivariate normality.

Methodology Applications

SCNet: A Neural Network for Automated Side-Channel Attack

1 code implementation2 Aug 2020 Guanlin Li, Chang Liu, Han Yu, Yanhong Fan, Libang Zhang, Zongyue Wang, Meiqin Wang

Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system.

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

no code implementations15 Jun 2020 Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin

It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.

Topology-aware Differential Privacy for Decentralized Image Classification

no code implementations14 Jun 2020 Shangwei Guo, Tianwei Zhang, Guowen Xu, Han Yu, Tao Xiang, Yang Liu

In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems.

Classification Image Classification

AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online

no code implementations25 Mar 2020 Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qing Da, An-Xiang Zeng, Han Yu, Yang Yu, Zhi-Hua Zhou

The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.


Threats to Federated Learning: A Survey

no code implementations4 Mar 2020 Lingjuan Lyu, Han Yu, Qiang Yang

It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation.

Federated Learning

FedCoin: A Peer-to-Peer Payment System for Federated Learning

1 code implementation26 Feb 2020 Yuan Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, Han Yu

In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol.

Federated Learning

Byzantine-resilient Decentralized Stochastic Gradient Descent

no code implementations20 Feb 2020 Shangwei Guo, Tianwei Zhang, Han Yu, Xiaofei Xie, Lei Ma, Tao Xiang, Yang Liu

It guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes.

Edge-computing Image Classification

Multi-Participant Multi-Class Vertical Federated Learning

no code implementations30 Jan 2020 Siwei Feng, Han Yu

Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants.

Federated Learning Multi-class Classification +1

FOCUS: Dealing with Label Quality Disparity in Federated Learning

1 code implementation29 Jan 2020 Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, Zhiqi Shen

It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset.

Federated Learning

Reviewing and Improving the Gaussian Mechanism for Differential Privacy

no code implementations27 Nov 2019 Jun Zhao, Teng Wang, Tao Bai, Kwok-Yan Lam, Zhiying Xu, Shuyu Shi, Xuebin Ren, Xinyu Yang, Yang Liu, Han Yu

Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.

Transfer Learning with Dynamic Distribution Adaptation

1 code implementation17 Sep 2019 Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, Qiang Yang

Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.

Domain Adaptation Image Classification +2

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

no code implementations4 Jun 2019 Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, Shuyu Shi

To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision.

Autonomous Vehicles Decision Making

Towards Fair and Privacy-Preserving Federated Deep Models

1 code implementation4 Jun 2019 Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, Kee Siong Ng

This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.

Fairness Federated Learning +1

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

no code implementations16 May 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, Dong In Kim

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.

Federated Learning

Easy Transfer Learning By Exploiting Intra-domain Structures

1 code implementation2 Apr 2019 Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang

In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.

Domain Adaptation Model Selection +1

Ethically Aligned Opportunistic Scheduling for Productive Laziness

no code implementations2 Jan 2019 Han Yu, Chunyan Miao, Yongqing Zheng, Lizhen Cui, Simon Fauvel, Cyril Leung

In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper.

Building Ethics into Artificial Intelligence

no code implementations7 Dec 2018 Han Yu, Zhiqi Shen, Chunyan Miao, Cyril Leung, Victor R. Lesser, Qiang Yang

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination.

Decision Making

Autoencoder Based Sample Selection for Self-Taught Learning

no code implementations5 Aug 2018 Siwei Feng, Han Yu, Marco F. Duarte

In this paper, we propose a metric for the relevance between a source sample and the target samples.

Transfer Learning

Cross-position Activity Recognition with Stratified Transfer Learning

no code implementations26 Jun 2018 Yiqiang Chen, Jindong Wang, Meiyu Huang, Han Yu

STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer.

Activity Recognition Transfer Learning

Surveillance Video Parsing with Single Frame Supervision

no code implementations CVPR 2017 Si Liu, Changhu Wang, Ruihe Qian, Han Yu, Renda Bao

In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage.

Frame Optical Flow Estimation

A Survey on Artificial Intelligence and Data Mining for MOOCs

no code implementations26 Jan 2016 Simon Fauvel, Han Yu

In this survey paper, we first review the state-of-the-art artificial intelligence and data mining research applied to MOOCs, emphasising the use of AI and DM tools and techniques to improve student engagement, learning outcomes, and our understanding of the MOOC ecosystem.

Cannot find the paper you are looking for? You can Submit a new open access paper.