Search Results for author: Qiang Yang

Found 149 papers, 44 papers with code

Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages

no code implementations EMNLP 2020 Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying WEI, Yu Zhang, Qiang Yang

Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks.

Cross-Lingual Transfer Graph Learning +1

Transferring SLU Models in Novel Domains

no code implementations ICLR 2019 Yaohua Tang, Kaixiang Mo, Qian Xu, Chao Zhang, Qiang Yang

When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.

Intent Recognition Meta-Learning +4

Unlearning during Learning: An Efficient Federated Machine Unlearning Method

no code implementations24 May 2024 Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, Qiang Yang

To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged.

Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data

no code implementations23 May 2024 Haoran Li, Xinyuan Zhao, Dadi Guo, Hanlin Gu, Ziqian Zeng, Yuxing Han, Yangqiu Song, Lixin Fan, Qiang Yang

In this paper, we introduce a Federated Domain-specific Knowledge Transfer (FDKT) framework, which enables domain-specific knowledge transfer from LLMs to SLMs while preserving clients' data privacy.

Federated Learning Transfer Learning

A Survey on Contribution Evaluation in Vertical Federated Learning

1 code implementation3 May 2024 Yue Cui, Chung-ju Huang, Yuzhu Zhang, Leye Wang, Lixin Fan, Xiaofang Zhou, Qiang Yang

Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing.

Vertical Federated Learning

Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation

no code implementations2 May 2024 Yu Zhu, Qiang Yang, Li Xu

Cell image segmentation is usually implemented using fully supervised deep learning methods, which heavily rely on extensive annotated training data.

Active Learning Cell Segmentation +3

PackVFL: Efficient HE Packing for Vertical Federated Learning

no code implementations1 May 2024 Liu Yang, Shuowei Cai, Di Chai, Junxue Zhang, Han Tian, Yilun Jin, Kun Guo, Kai Chen, Qiang Yang

To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms.

Vertical Federated Learning

Advances and Open Challenges in Federated Learning with Foundation Models

no code implementations23 Apr 2024 Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, Qiang Yang

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.

Computational Efficiency Federated Learning +1

FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom

no code implementations18 Apr 2024 Yuanqin He, Yan Kang, Lixin Fan, Qiang Yang

To address these issues, we propose a Federated Evaluation framework of Large Language Models, named FedEval-LLM, that provides reliable performance measurements of LLMs on downstream tasks without the reliance on labeled test sets and external tools, thus ensuring strong privacy-preserving capability.

Federated Learning Privacy Preserving

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning

no code implementations6 Apr 2024 Yan Kang, Ziyao Ren, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang

This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.

Bayesian Optimization Hyperparameter Optimization +2

Evaluating Membership Inference Attacks and Defenses in Federated Learning

1 code implementation9 Feb 2024 Gongxi Zhu, Donghao Li, Hanlin Gu, Yuxing Han, Yuan YAO, Lixin Fan, Qiang Yang

Firstly, combining model information from multiple communication rounds (Multi-temporal) enhances the overall effectiveness of MIAs compared to utilizing model information from a single epoch.

Federated Learning

Decentralized Federated Learning: A Survey on Security and Privacy

no code implementations25 Jan 2024 Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif, Boyu Wang, Qiang Yang

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages.

Federated Learning Privacy Preserving

A Survey on Cross-Domain Sequential Recommendation

1 code implementation10 Jan 2024 Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).

Auxiliary Learning Sequential Recommendation

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

no code implementations18 Dec 2023 Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie

Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.

Logical Reasoning

A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting

no code implementations8 Dec 2023 Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, Qiang Yang

Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights.

Partial Domain Adaptation Universal Domain Adaptation +1

Grounding Foundation Models through Federated Transfer Learning: A General Framework

no code implementations29 Nov 2023 Yan Kang, Tao Fan, Hanlin Gu, Xiaojin Zhang, Lixin Fan, Qiang Yang

Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy.

Federated Learning Privacy Preserving +1

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

1 code implementation12 Nov 2023 Wenke Huang, Mang Ye, Zekun Shi, Guancheng Wan, He Li, Bo Du, Qiang Yang

In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.

Fairness Federated Learning +1

A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models

no code implementations24 Oct 2023 Yuanfeng Song, Yuanqin He, Xuefang Zhao, Hanlin Gu, Di Jiang, Haijun Yang, Lixin Fan, Qiang Yang

The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm.

Multi-Task Learning Prompt Engineering

FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

1 code implementation16 Oct 2023 Tao Fan, Yan Kang, Guoqiang Ma, Weijing Chen, Wenbin Wei, Lixin Fan, Qiang Yang

FATE-LLM (1) facilitates federated learning for large language models (coined FedLLM); (2) promotes efficient training of FedLLM using parameter-efficient fine-tuning methods; (3) protects the intellectual property of LLMs; (4) preserves data privacy during training and inference through privacy-preserving mechanisms.

Federated Learning Privacy Preserving

A Survey of Heterogeneous Transfer Learning

1 code implementation12 Oct 2023 Runxue Bao, Yiming Sun, Yuhe Gao, Jindong Wang, Qiang Yang, Haifeng Chen, Zhi-Hong Mao, Ye Ye

These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.

Transfer Learning

ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning

1 code implementation8 Oct 2023 Wang Lu, Hao Yu, Jindong Wang, Damien Teney, Haohan Wang, Yiqiang Chen, Qiang Yang, Xing Xie, Xiangyang Ji

When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.

Personalized Federated Learning

A Survey for Federated Learning Evaluations: Goals and Measures

1 code implementation23 Aug 2023 Di Chai, Leye Wang, Liu Yang, Junxue Zhang, Kai Chen, Qiang Yang

Evaluation is a systematic approach to assessing how well a system achieves its intended purpose.

Federated Learning Privacy Preserving

Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

1 code implementation KDD 2023 Yilun Jin, Kai Chen, Qiang Yang

To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities.

Graph structure learning Knowledge Distillation +1

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning

no code implementations20 Jul 2023 Ziyao Ren, Yan Kang, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang

To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.

Privacy Preserving Vertical Federated Learning

Large Language Models Understand and Can be Enhanced by Emotional Stimuli

no code implementations14 Jul 2023 Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie

In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.

Emotional Intelligence Informativeness

A Survey on Evaluation of Large Language Models

1 code implementation6 Jul 2023 Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.


A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning

no code implementations28 May 2023 Xiaojin Zhang, Yan Kang, Lixin Fan, Kai Chen, Qiang Yang

Motivated by this requirement, we propose a framework that (1) formulates TFL as a problem of finding a protection mechanism to optimize the tradeoff between privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors.

Federated Learning Meta-Learning

Theoretically Principled Federated Learning for Balancing Privacy and Utility

no code implementations24 May 2023 Xiaojin Zhang, Wenjie Li, Kai Chen, Shutao Xia, Qiang Yang

We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility.

Federated Learning

A Topic-aware Summarization Framework with Different Modal Side Information

no code implementations19 May 2023 Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng, Qiang Yang, Qishen Zhang, Xin Gao, Xiangliang Zhang

To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.

Contrastive Learning

FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature

no code implementations10 May 2023 Wenyuan Yang, Gongxi Zhu, Yuguo Yin, Hanlin Gu, Lixin Fan, Qiang Yang, Xiaochun Cao

Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.

Federated Learning

FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof

no code implementations8 May 2023 Wenyuan Yang, Yuguo Yin, Gongxi Zhu, Hanlin Gu, Lixin Fan, Xiaochun Cao, Qiang Yang

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.

Federated Learning

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion

no code implementations7 May 2023 Xiaojin Zhang, Kai Chen, Qiang Yang

The nature of the widely-adopted protection mechanisms including \textit{Randomization Mechanism} and \textit{Compression Mechanism} is to protect privacy via distorting model parameter.

Federated Learning Privacy Preserving

Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning

no code implementations29 Apr 2023 Yan Kang, Hanlin Gu, Xingxing Tang, Yuanqin He, Yuzhu Zhang, Jinnan He, Yuxing Han, Lixin Fan, Kai Chen, Qiang Yang

Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.

Fairness Federated Learning

Learn to Cluster Faces with Better Subgraphs

no code implementations21 Apr 2023 Yuan Cao, Di Jiang, Guanqun Hou, Fan Deng, Xinjia Chen, Qiang Yang

Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models.

Clustering Face Clustering +1

A Game-theoretic Framework for Privacy-preserving Federated Learning

no code implementations11 Apr 2023 Xiaojin Zhang, Lixin Fan, Siwei Wang, Wenjie Li, Kai Chen, Qiang Yang

To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks.

Federated Learning Privacy Preserving

Probably Approximately Correct Federated Learning

no code implementations10 Apr 2023 Xiaojin Zhang, Anbu Huang, Lixin Fan, Kai Chen, Qiang Yang

However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved.

Federated Learning PAC learning

Federated Learning without Full Labels: A Survey

no code implementations25 Mar 2023 Yilun Jin, Yang Liu, Kai Chen, Qiang Yang

Therefore, the problem of federated learning without full labels is important in real-world FL applications.

Federated Learning Self-Supervised Learning +1

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation

no code implementations30 Jan 2023 Hanlin Gu, Jiahuan Luo, Yan Kang, Lixin Fan, Qiang Yang

Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance.

Privacy Preserving Vertical Federated Learning

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders

no code implementations24 Nov 2022 Hanlin Gu, Lixin Fan, Xingxing Tang, Qiang Yang

Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks.

Community Detection Federated Learning

Vertical Federated Learning: Concepts, Advances and Challenges

no code implementations23 Nov 2022 Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, Qiang Yang

Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy.

Fairness Privacy Preserving +1

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

no code implementations14 Nov 2022 Shuo Shao, Wenyuan Yang, Hanlin Gu, Zhan Qin, Lixin Fan, Qiang Yang, Kui Ren

To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants.

Continual Learning Federated Learning

A Survey on Heterogeneous Federated Learning

no code implementations10 Oct 2022 Dashan Gao, Xin Yao, Qiang Yang

Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL.

Federated Learning Transfer Learning

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

no code implementations8 Sep 2022 Yan Kang, Jiahuan Luo, Yuanqin He, Xiaojin Zhang, Lixin Fan, Qiang Yang

We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely-deployed VFL algorithms.

Privacy Preserving Vertical Federated Learning

Trading Off Privacy, Utility and Efficiency in Federated Learning

no code implementations1 Sep 2022 Xiaojin Zhang, Yan Kang, Kai Chen, Lixin Fan, Qiang Yang

In addition, it is a mandate for a federated learning system to achieve high \textit{efficiency} in order to enable large-scale model training and deployment.

Vertical Federated Learning

Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies

1 code implementation28 Aug 2022 Yinghua Zhang, Yangqiu Song, Kun Bai, Qiang Yang

To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example.


A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning

1 code implementation18 Aug 2022 Yuanqin He, Yan Kang, Xinyuan Zhao, Jiahuan Luo, Lixin Fan, Yuxing Han, Qiang Yang

In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i. e., dispersed features) of samples aligned among parties and local views (i. e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model.

Inference Attack Representation Learning +2

WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning

no code implementations21 Jun 2022 Xueyang Wu, Shengqi Tan, Qian Xu, Qiang Yang

The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.

BIG-bench Machine Learning Ensemble Learning +2

An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application

no code implementations21 Jun 2022 Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan, Qiang Yang

Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.

Face Recognition Federated Learning +1

FadMan: Federated Anomaly Detection across Multiple Attributed Networks

no code implementations27 May 2022 Nannan Wu, Ning Zhang, Wenjun Wang, Lixin Fan, Qiang Yang

The proposed algorithm FadMan is a vertical federated learning framework for public node aligned with many private nodes of different features, and is validated on two tasks correlated anomaly detection on multiple attributed networks and anomaly detection on an attributeless network using five real-world datasets.

Anomaly Detection Data Integration +1

Multi-core fiber enabled fading noise suppression in φ-OFDR based quantitative distributed vibration sensing

no code implementations3 May 2022 Yuxiang Feng, Weilin Xie, Yinxia Meng, Jiang Yang, Qiang Yang, Yan Ren, Tianwai Bo, Zhongwei Tan, Wei Wei, Yi Dong

Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry ({\phi}-OFDR) based distributed fiber-optic sensing.

Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

no code implementations21 Apr 2022 Tao Yang, Jinming Wang, Weijie Hao, Qiang Yang, Wenhai Wang

The sensor data detection model based on Gaussian and Bayesian algorithms can detect the anomalous sensor data in real-time and upload them to the cloud for further analysis, filtering the normal sensor data and reducing traffic load.

Anomaly Detection

No Free Lunch Theorem for Security and Utility in Federated Learning

no code implementations11 Mar 2022 Xiaojin Zhang, Hanlin Gu, Lixin Fan, Kai Chen, Qiang Yang

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms.

Federated Learning Privacy Preserving

LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization

no code implementations15 Dec 2021 Huiming Chen, Huandong Wang, Quanming Yao, Yong Li, Depeng Jin, Qiang Yang

Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.

Federated Learning

Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning

no code implementations10 Dec 2021 Yang Liu, Tianyuan Zou, Yan Kang, Wenhan Liu, Yuanqin He, Zhihao Yi, Qiang Yang

An immediate defense strategy is to protect sample-level messages communicated with Homomorphic Encryption (HE), and in this way only the batch-averaged local gradients are exposed to each party (termed black-boxed VFL).

Inference Attack Vertical Federated Learning

Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability

no code implementations22 Nov 2021 Yan Kang, Yang Liu, Yuezhou Wu, Guoqiang Ma, Qiang Yang

We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features.

Domain Adaptation Privacy Preserving +1

FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

2 code implementations16 Nov 2021 Yuezhou Wu, Yan Kang, Jiahuan Luo, Yuanqin He, Qiang Yang

Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data.

Federated Learning Privacy Preserving

Personalized Heterogeneous Federated Learning with Gradient Similarity

no code implementations29 Sep 2021 Jing Xie, Xiang Yin, Xiyi Zhang, Juan Chen, Quan Wen, Qiang Yang, Xuan Mo

In SPFL, the server uses the Softmax Normalized Gradient Similarity (SNGS) to weight the relationship between clients, and sends the personalized global model to each client.

Federated Learning

FedIPR: Ownership Verification for Federated Deep Neural Network Models

1 code implementation27 Sep 2021 Bowen Li, Lixin Fan, Hanlin Gu, Jie Li, Qiang Yang

To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i. e., FedIPR.

Federated Learning

Federated Deep Learning with Bayesian Privacy

no code implementations27 Sep 2021 Hanlin Gu, Lixin Fan, Bowen Li, Yan Kang, Yuan YAO, Qiang Yang

To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information.

Federated Learning Image Classification +1

Multi-Task Learning in Natural Language Processing: An Overview

no code implementations19 Sep 2021 Shijie Chen, Yu Zhang, Qiang Yang

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP).

Multi-Task Learning Scheduling

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

no code implementations9 Sep 2021 Zhifeng Jiang, Wei Wang, Bo Li, Qiang Yang

The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data.

Benchmarking Federated Learning +1

QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction

no code implementations19 Aug 2021 Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang

We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.

Attribute Attribute Value Extraction +3

Practical and Secure Federated Recommendation with Personalized Masks

no code implementations18 Aug 2021 Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang Yang

In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.

Federated Learning Recommendation Systems

Transferring Knowledge Distillation for Multilingual Social Event Detection

1 code implementation6 Aug 2021 Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang

Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.

Cross-Lingual Word Embeddings Event Detection +2

Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks

no code implementations CVPR 2021 Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang

Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.

Image Generation Image Super-Resolution +1

Frustratingly Easy Transferability Estimation

no code implementations17 Jun 2021 Long-Kai Huang, Ying WEI, Yu Rong, Qiang Yang, Junzhou Huang

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.

Mutual Information Estimation Transfer Learning

Ternary Hashing

no code implementations16 Mar 2021 Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang

This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.


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.

Benchmarking Personalized Federated Learning +1

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack

1 code implementation8 Feb 2021 Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang

Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.

Image Generation Image Super-Resolution +1

Self-supervised Cross-silo Federated Neural Architecture Search

no code implementations28 Jan 2021 Xinle Liang, Yang Liu, Jiahuan Luo, Yuanqin He, Tianjian Chen, Qiang Yang

Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties.

Neural Architecture Search Vertical Federated Learning

TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

no code implementations EACL 2021 GuangNeng Hu, Qiang Yang

To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus.

Cross-corpus News Recommendation +1

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 Privacy Preserving

Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness

no code implementations27 Nov 2020 Yilun Jin, Lixin Fan, Kam Woh Ng, Ce Ju, Qiang Yang

Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed.

FedEval: A Holistic Evaluation Framework for Federated Learning

no code implementations19 Nov 2020 Di Chai, Leye Wang, Liu Yang, Junxue Zhang, Kai Chen, Qiang Yang

In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.

Benchmarking Federated Learning +1

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

1 code implementation25 Aug 2020 Yinghua Zhang, Yangqiu Song, Jian Liang, Kun Bai, Qiang Yang

To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model.

Transfer Learning

Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection

1 code implementation25 Aug 2020 Jian Han Lim, Chee Seng Chan, Kam Woh Ng, Lixin Fan, Qiang Yang

By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital watermarking framework that was conventionally used to protect the ownership of multimedia and video content.

Image Captioning Image Classification

Data science and AI in FinTech: An overview

no code implementations10 Jul 2020 Longbing Cao, Qiang Yang, Philip S. Yu

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas.

BIG-bench Machine Learning Federated Learning +1

Privacy Threats Against Federated Matrix Factorization

no code implementations3 Jul 2020 Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng, Qiang Yang

Matrix Factorization has been very successful in practical recommendation applications and e-commerce.

Collaborative Filtering Federated Learning +2

Network On Network for Tabular Data Classification in Real-world Applications

no code implementations20 May 2020 Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang

As a result, the intra-field information and the non-linear interactions between those operations (e. g. neural network and factorization machines) are ignored.

General Classification

Fisher Deep Domain Adaptation

1 code implementation12 Mar 2020 Yinghua Zhang, Yu Zhang, Ying WEI, Kun Bai, Yangqiu Song, Qiang Yang

Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.

Domain Adaptation

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

Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

no code implementations26 Feb 2020 Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner.

BIG-bench Machine Learning Federated Learning

RPN: A Residual Pooling Network for Efficient Federated Learning

no code implementations23 Jan 2020 Anbu Huang, YuanYuan Chen, Yang Liu, Tianjian Chen, Qiang Yang

Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.

Federated Learning

A Communication Efficient Collaborative Learning Framework for Distributed Features

no code implementations24 Dec 2019 Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, Qiang Yang

We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

no code implementations1 Dec 2019 Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, Qiang Yang

Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.

regression Vertical Federated Learning

Prototypical Networks for Multi-Label Learning

no code implementations17 Nov 2019 Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang

We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.

Multi-Label Classification Multi-Label Learning

L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition

no code implementations25 Oct 2019 Yuanfeng Song, Di Jiang, Xuefang Zhao, Qian Xu, Raymond Chi-Wing Wong, Lixin Fan, Qiang Yang

Modern Automatic Speech Recognition (ASR) systems primarily rely on scores from an Acoustic Model (AM) and a Language Model (LM) to rescore the N-best lists.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Real-World Image Datasets for Federated Learning

2 code implementations14 Oct 2019 Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yun-Feng Huang, Yang Liu, Qiang Yang

Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.

BIG-bench Machine Learning Federated Learning +1

Federated Transfer Reinforcement Learning for Autonomous Driving

no code implementations14 Oct 2019 Xinle Liang, Yang Liu, Tianjian Chen, Ming Liu, Qiang Yang

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.

Autonomous Driving Collision Avoidance +3

CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction

no code implementations25 Sep 2019 Xu Geng, Lingyu Zhang, Shulin Li, Yuanbo Zhang, Lulu Zhang, Leye Wang, Qiang Yang, Hongtu Zhu, Jieping Ye

Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.

Decoder Graph Attention +3

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

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

1 code implementation11 Sep 2019 Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, Qiang Yang

To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.

EEG Emotion Recognition +3

Secure Federated Matrix Factorization

no code implementations12 Jun 2019 Di Chai, Leye Wang, Kai Chen, Qiang Yang

The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.

BIG-bench Machine Learning Federated Learning

Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction

1 code implementation28 May 2019 Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.

Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

no code implementations27 May 2019 Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, Jieping Ye

To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.

BIG-bench Machine Learning

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

no code implementations29 Apr 2019 Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Qiang Yang, Wenyuan Dai

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.

Distributed Computing

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.

Computational Efficiency Domain Adaptation +2

Learning to Transfer Examples for Partial Domain Adaptation

1 code implementation CVPR 2019 Zhangjie Cao, Kaichao You, Mingsheng Long, Jian-Min Wang, Qiang Yang

Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.

Partial Domain Adaptation Transfer Learning

AutoML @ NeurIPS 2018 challenge: Design and Results

no code implementations12 Mar 2019 Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.

AutoML BIG-bench Machine Learning

SecureBoost: A Lossless Federated Learning Framework

1 code implementation25 Jan 2019 Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, Qiang Yang

This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.

BIG-bench Machine Learning Entity Alignment +2

Federated Deep Reinforcement Learning

no code implementations24 Jan 2019 Hankz Hankui Zhuo, Wenfeng Feng, Yufeng Lin, Qian Xu, Qiang Yang

In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.

reinforcement-learning Reinforcement Learning (RL) +1

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

no code implementations22 Jan 2019 Guang-Neng Hu, Yu Zhang, Qiang Yang

Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods.

Collaborative Filtering Movie Recommendation +2

Secure Federated Transfer Learning

no code implementations8 Dec 2018 Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, Qiang Yang

A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation.

BIG-bench Machine Learning Privacy Preserving +1

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 Ethics

Differential Private Stack Generalization with an Application to Diabetes Prediction

no code implementations23 Nov 2018 Quanming Yao, Xiawei Guo, James T. Kwok, WeiWei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms.

Diabetes Prediction Ensemble Learning +3

Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification

1 code implementation AAAI 2019 2018 Zheng Li, Ying WEI, Yu Zhang, Xiang Zhang, Xin Li, Qiang Yang

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).

General Classification Sentence +2

Smart City Development with Urban Transfer Learning

no code implementations5 Aug 2018 Leye Wang, Bin Guo, Qiang Yang

To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.

Management Transfer Learning

Bike Flow Prediction with Multi-Graph Convolutional Networks

1 code implementation28 Jul 2018 Di Chai, Leye Wang, Qiang Yang

We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.


Transfer Learning via Learning to Transfer

no code implementations ICML 2018 Ying WEI, Yu Zhang, Junzhou Huang, Qiang Yang

In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.

Transfer Learning

Learning to Multitask

no code implementations NeurIPS 2018 Yu Zhang, Ying WEI, Qiang Yang

Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation.

Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

1 code implementation Thirty-Second AAAI Conference on Artificial Intelligence 2018 Zheng Li, Ying WEI, Yu Zhang, Qiang Yang

Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i. e., the domain- specific sentiment words, and pivots, i. e., the domain-shared sentiment words, simultaneously.

Classification Cross-Domain Text Classification +4

Parameter Transfer Unit for Deep Neural Networks

no code implementations23 Apr 2018 Yinghua Zhang, Yu Zhang, Qiang Yang

Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures.

Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment

no code implementations20 Apr 2018 Kaixiang Mo, Yu Zhang, Qiang Yang, Pascale Fung

Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

1 code implementation18 Apr 2018 Guang-Neng Hu, Yu Zhang, Qiang Yang

CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.

Recommendation Systems Transfer Learning

LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text

no code implementations17 Apr 2018 GuangNeng Hu, Yu Zhang, Qiang Yang

By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction.

Collaborative Filtering Recommendation Systems

Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

no code implementations1 Feb 2018 Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang

RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.

Transfer Learning

Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems

no code implementations11 Nov 2017 Kaixiang Mo, Yu Zhang, Qiang Yang, Pascale Fung

Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.

Decoder Sentence +2

Integrating User and Agent Models: A Deep Task-Oriented Dialogue System

no code implementations10 Nov 2017 Weiyan Wang, Yuxiang Wu, Yu Zhang, Zhongqi Lu, Kaixiang Mo, Qiang Yang

Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.

Task-Oriented Dialogue Systems

Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

no code implementations13 Sep 2017 Wenya Zhu, Kaixiang Mo, Yu Zhang, Zhangbin Zhu, Xuezheng Peng, Qiang Yang

Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities.

Generative Question Answering

Learning to Transfer

no code implementations18 Aug 2017 Ying Wei, Yu Zhang, Qiang Yang

We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function.

Transfer Learning

A Survey on Multi-Task Learning

1 code implementation25 Jul 2017 Yu Zhang, Qiang Yang

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.

Active Learning Clustering +3

Ridesourcing Car Detection by Transfer Learning

no code implementations23 May 2017 Leye Wang, Xu Geng, Jintao Ke, Chen Peng, Xiaojuan Ma, Daqing Zhang, Qiang Yang

Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.

Transfer Learning

Online Hashing

no code implementations6 Apr 2017 Long-Kai Huang, Qiang Yang, Wei-Shi Zheng

Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space.

Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks

1 code implementation20 Feb 2017 Chunjie Luo, Jianfeng Zhan, Lei Wang, Qiang Yang

We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN.

Personalizing a Dialogue System with Transfer Reinforcement Learning

no code implementations10 Oct 2016 Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang

One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.

reinforcement-learning Reinforcement Learning (RL) +1

Partially Observable Markov Decision Process for Recommender Systems

no code implementations28 Aug 2016 Zhongqi Lu, Qiang Yang

We report the "Recurrent Deterioration" (RD) phenomenon observed in online recommender systems.

Recommendation Systems

Transitive Hashing Network for Heterogeneous Multimedia Retrieval

no code implementations15 Aug 2016 Zhangjie Cao, Mingsheng Long, Qiang Yang

Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency.


Collaborative Receptive Field Learning

1 code implementation2 Feb 2014 Shu Kong, Zhuolin Jiang, Qiang Yang

However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.

General Classification Object Categorization

Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification

no code implementations22 Jan 2014 Shu Kong, Zhuolin Jiang, Qiang Yang

We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question.

Age Estimation Clustering +7

Action-Model Based Multi-agent Plan Recognition

no code implementations NeurIPS 2012 Hankz H. Zhuo, Qiang Yang, Subbarao Kambhampati

Previous MAPR approaches required a library of team activity sequences (team plans) be given as input.

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