Qiang Yang

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The Hong Kong University of Science and Technology

New Bright Professor of Engineering, Chair Professor and Head of Department of Computer Science and Engineering at Hong Kong University of Science and Technology

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...
12/10/2019 ∙ by Peter Kairouz, et al. ∙ 31 ∙ share

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A Communication Efficient Vertical Federated Learning Framework

One critical challenge for applying today's Artificial Intelligence (AI)...
12/24/2019 ∙ by Yang Liu, et al. ∙ 23 ∙ share

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A Survey towards Federated Semi-supervised Learning

The success of Artificial Intelligence (AI) should be largely attributed...
02/26/2020 ∙ by Yilun Jin, et al. ∙ 23 ∙ share

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Easy Transfer Learning By Exploiting Intra-domain Structures

Transfer learning aims at transferring knowledge from a well-labeled dom...
04/02/2019 ∙ by Jindong Wang, et al. ∙ 18 ∙ share

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Federated Reinforcement Learning

In reinforcement learning, building policies of high-quality is challeng...
01/24/2019 ∙ by Hankz Hankui Zhuo, et al. ∙ 16 ∙ share

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Federated Transfer Reinforcement Learning for Autonomous Driving

Reinforcement learning (RL) is widely used in autonomous driving tasks a...
10/14/2019 ∙ by Xinle Liang, et al. ∙ 16 ∙ share

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Building Ethics into Artificial Intelligence

As artificial intelligence (AI) systems become increasingly ubiquitous, ...
12/07/2018 ∙ by Han Yu, et al. ∙ 14 ∙ share

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Federated Machine Learning: Concept and Applications

Today's AI still faces two major challenges. One is that in most industr...
02/13/2019 ∙ by Qiang Yang, et al. ∙ 14 ∙ share

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RPN: A Residual Pooling Network for Efficient Federated Learning

Federated learning is a new machine learning framework which enables dif...
01/23/2020 ∙ by Anbu Huang, et al. ∙ 14 ∙ share

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

Data privacy and security becomes a major concern in building machine le...
12/01/2019 ∙ by Kai Yang, et al. ∙ 13 ∙ share

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FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Visual object detection is a computer vision-based artificial intelligen...
01/17/2020 ∙ by Yang Liu, et al. ∙ 13 ∙ share

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AutoML @ NeurIPS 2018 challenge: Design and Results

We organized a competition on Autonomous Lifelong Machine Learning with ...
03/12/2019 ∙ by Hugo Jair Escalante, et al. ∙ 12 ∙ share

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Learning to Transfer Examples for Partial Domain Adaptation

Domain adaptation is critical for learning in new and unseen environment...
03/28/2019 ∙ by Zhangjie Cao, et al. ∙ 12 ∙ share

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Bike Flow Prediction with Multi-Graph Convolutional Networks

One fundamental issue in managing bike sharing systems is the bike flow ...
07/28/2018 ∙ by Di Chai, et al. ∙ 8 ∙ share

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Privacy-preserving Transfer Learning for Knowledge Sharing

In many practical machine-learning applications, it is critical to allow...
11/23/2018 ∙ by Xiawei Guo, et al. ∙ 8 ∙ share

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Transfer Learning with Dynamic Distribution Adaptation

Transfer learning aims to learn robust classifiers for the target domain...
09/17/2019 ∙ by Jindong Wang, et al. ∙ 7 ∙ share

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SecureBoost: A Lossless Federated Learning Framework

The protection of user privacy is an important concern in machine learni...
01/25/2019 ∙ by Kewei Cheng, et al. ∙ 6 ∙ share

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

Collaborative filtering (CF) is the key technique for recommender system...
01/22/2019 ∙ by Guangneng Hu, et al. ∙ 6 ∙ share

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Smart City Development with Urban Transfer Learning

The rapid development of big data techniques has offered great opportuni...
08/05/2018 ∙ by Leye Wang, et al. ∙ 2 ∙ share

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

Graph convolution network based approaches have been recently used to mo...
05/27/2019 ∙ by Xu Geng, et al. ∙ 2 ∙ share

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A Survey on Multi-Task Learning

Multi-Task Learning (MTL) is a learning paradigm in machine learning and...
07/25/2017 ∙ by Yu Zhang, et al. ∙ 0 ∙ share

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Learning to Transfer

Transfer learning borrows knowledge from a source domain to facilitate l...
08/18/2017 ∙ by Ying Wei, et al. ∙ 0 ∙ share

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Ridesourcing Car Detection by Transfer Learning

Ridesourcing platforms like Uber and Didi are getting more and more popu...
05/23/2017 ∙ by Leye Wang, et al. ∙ 0 ∙ share

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

Traditionally, multi-layer neural networks use dot product between the o...
02/20/2017 ∙ by Chunjie Luo, et al. ∙ 0 ∙ share

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Online Hashing

Although hash function learning algorithms have achieved great success i...
04/06/2017 ∙ by Long-Kai Huang, et al. ∙ 0 ∙ share

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Transitive Hashing Network for Heterogeneous Multimedia Retrieval

Hashing has been widely applied to large-scale multimedia retrieval due ...
08/15/2016 ∙ by Zhangjie Cao, et al. ∙ 0 ∙ share

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Inferring Taxi Status Using GPS Trajectories

In this paper, we infer the statuses of a taxi, consisting of occupied, ...
05/20/2012 ∙ by Yin Zhu, et al. ∙ 0 ∙ share

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Selective Transfer Learning for Cross Domain Recommendation

Collaborative filtering (CF) aims to predict users' ratings on items acc...
10/26/2012 ∙ by Zhongqi Lu, et al. ∙ 0 ∙ share

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Collaborative Receptive Field Learning

The challenge of object categorization in images is largely due to arbit...
02/02/2014 ∙ by Shu Kong, et al. ∙ 0 ∙ share

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

We now know that mid-level features can greatly enhance the performance ...
01/22/2014 ∙ by Shu Kong, et al. ∙ 0 ∙ share

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Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems

Training a personalized dialogue system requires a lot of data, and the ...
11/11/2017 ∙ by Kaixiang Mo, et al. ∙ 0 ∙ share

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

Task-oriented dialogue systems can efficiently serve a large number of c...
11/10/2017 ∙ by Weiyan Wang, et al. ∙ 0 ∙ share

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

In knowledge grounded conversation, domain knowledge plays an important ...
09/13/2017 ∙ by Wenya Zhu, et al. ∙ 0 ∙ share

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Personalizing a Dialogue System with Transfer Reinforcement Learning

It is difficult to train a personalized task-oriented dialogue system be...
10/10/2016 ∙ by Kaixiang Mo, et al. ∙ 0 ∙ share

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Crowd Flow Prediction by Deep Spatio-Temporal Transfer Learning

Crowd flow prediction is a fundamental urban computing problem. Recently...
02/01/2018 ∙ by Leye Wang, et al. ∙ 0 ∙ share

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Millionaire: A Hint-guided Approach for Crowdsourcing

Modern machine learning is migrating to the era of complex models, which...
02/26/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Big Data Dwarfs: Towards Fully Understanding Big Data Analytics Workloads

Though the big data benchmark suites like BigDataBench and CloudSuite ha...
02/01/2018 ∙ by Wanling Gao, et al. ∙ 0 ∙ share

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Parameter Transfer Unit for Deep Neural Networks

Parameters in deep neural networks which are trained on large-scale data...
04/23/2018 ∙ by Yinghua Zhang, et al. ∙ 0 ∙ share

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

Dialogue policy transfer enables us to build dialogue policies in a targ...
04/20/2018 ∙ by Kaixiang Mo, et al. ∙ 0 ∙ share

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Learning to Multitask

Multitask learning has shown promising performance in many applications ...
05/19/2018 ∙ by Yu Zhang, et al. ∙ 0 ∙ share

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Secure Federated Transfer Learning

Machine learning relies on the availability of a vast amount of data for...
12/08/2018 ∙ by Yang Liu, et al. ∙ 0 ∙ share

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

Aspect-level sentiment classification (ASC) aims at identifying sentimen...
11/16/2018 ∙ by Zheng Li, et al. ∙ 0 ∙ share

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A Dwarf-based Scalable Big Data Benchmarking Methodology

Different from the traditional benchmarking methodology that creates a n...
11/09/2017 ∙ by Wanling Gao, et al. ∙ 0 ∙ share

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Data Dwarfs: A Lens Towards Fully Understanding Big Data and AI Workloads

The complexity and diversity of big data and AI workloads make understan...
02/01/2018 ∙ by Wanling Gao, et al. ∙ 0 ∙ share

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

An effective content recommendation in modern social media platforms sho...
05/28/2019 ∙ by Wenyi Xiao, et al. ∙ 0 ∙ share

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Secure Federated Matrix Factorization

To protect user privacy and meet law regulations, federated (machine) le...
06/12/2019 ∙ by Di Chai, et al. ∙ 0 ∙ share

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HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography

Electroencephalography (EEG) classification techniques have been widely ...
09/11/2019 ∙ by Dashan Gao, et al. ∙ 0 ∙ share

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Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

Joint extraction of aspects and sentiments can be effectively formulated...
10/31/2019 ∙ by Zheng Li, et al. ∙ 0 ∙ share

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced ...
09/26/2019 ∙ by Wei Yang Bryan Lim, et al. ∙ 0 ∙ share

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

Modern Automatic Speech Recognition (ASR) systems primarily rely on scor...
10/25/2019 ∙ by Yuanfeng Song, et al. ∙ 0 ∙ share

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Qiang Yang

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