Search Results for author: Jie Xu

Found 52 papers, 10 papers with code

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

1 code implementation ICML 2020 Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik

Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal).

Context-Aware Online Client Selection for Hierarchical Federated Learning

no code implementations2 Dec 2021 Zhe Qu, Rui Duan, Lixing Chen, Jie Xu, Zhuo Lu, Yao Liu

In addition, client selection for HFL faces more challenges than conventional FL, e. g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO).

Federated Learning

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

no code implementations NeurIPS 2021 Jagdeep Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, Wojciech Matusik

In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots.

Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication

no code implementations26 Nov 2021 Xianxin Song, Ding Zhao, Haocheng Hua, Tony Xiao Han, Xun Yang, Jie Xu

This letter studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked.

A System for General In-Hand Object Re-Orientation

no code implementations4 Nov 2021 Tao Chen, Jie Xu, Pulkit Agrawal

The videos of the learned policies are available at: https://taochenshh. github. io/projects/in-hand-reorientation.

FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

no code implementations15 Oct 2021 Jieming Bian, Zhu Fu, Jie Xu

Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years.

Ensemble Learning Federated Learning

An End-to-End Differentiable Framework for Contact-Aware Robot Design

1 code implementation15 Jul 2021 Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal

Existing methods for co-optimization are limited and fail to explore a rich space of designs.

Contrastive Multi-Modal Clustering

no code implementations21 Jun 2021 Jie Xu, Huayi Tang, Yazhou Ren, Xiaofeng Zhu, Lifang He

(2) A feature contrastive module is proposed to learn common high-level semantic features from different modalities.

Contrastive Learning

Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

no code implementations ICCV 2021 Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He

The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection

1 code implementation18 Jun 2021 Ruiqing Ding, Yu Zhou, Jie Xu, Yan Xie, Qiqiang Liang, He Ren, YiXuan Wang, Yanlin Chen, Leye Wang, Man Huang

In SPSSOT, we first extract the same clinical indicators from the source domain (e. g., hospital with rich labeled data) and the target domain (e. g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance.

Domain Adaptation Transfer Learning

DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

no code implementations9 Jun 2021 Yifei Li, Tao Du, Kui Wu, Jie Xu, Wojciech Matusik

This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.

Transparent Model of Unabridged Data (TMUD)

no code implementations23 May 2021 Jie Xu, Min Ding

Recent advancements in computational power and algorithms have enabled unabridged data (e. g., raw images or audio) to be used as input in some models (e. g., deep learning).

Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

no code implementations14 Apr 2021 Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen

We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).

Federated Learning

Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering

1 code implementation28 Mar 2021 Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu

To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.

Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning

no code implementations2 Feb 2021 Letian Zhang, Lixing Chen, Jie Xu

The basic idea of this system is to partition a deep neural network (DNN) into a front-end part running on the mobile device and a back-end part running on the edge server, with the key challenge being how to locate the optimal partition point to minimize the end-to-end inference delay.

Decision Making Object Detection

Personalized Education in the AI Era: What to Expect Next?

no code implementations19 Jan 2021 Setareh Maghsudi, Andrew Lan, Jie Xu, Mihaela van der Schaar

The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal.

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

no code implementations10 Jan 2021 Jie Xu, Heqiang Wang, Lixing Chen

For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients.

Fairness Federated Learning

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

no code implementations4 Jan 2021 Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, Jie Xu

Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.

Graph Attention Representation Learning +1

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

no code implementations29 Dec 2020 Zhenglei He, Kim Phuc Tran, Sebastien Thomassey, Xianyi Zeng, Jie Xu, Chang Haiyi

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies.

Decision Making

Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning

no code implementations2 Dec 2020 Zhenglei He, Kim Phuc Tran, Sebastien Thomassey, Xianyi Zeng, Jie Xu, Changhai Yi

The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.

Multi-agent Reinforcement Learning

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

no code implementations19 Oct 2020 Qingqing Wu, Jie Xu, Yong Zeng, Derrick Wing Kwan Ng, Naofal Al-Dhahir, Robert Schober, A. Lee Swindlehurst

On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.

Neural Dialogue State Tracking with Temporally Expressive Networks

1 code implementation Findings of the Association for Computational Linguistics 2020 Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu

Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue.

Dialogue State Tracking

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

1 code implementation EMNLP 2020 Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu

In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.

Dialogue State Tracking Multi-domain Dialogue State Tracking

A(DP)$^2$SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy

no code implementations21 Aug 2020 Jie Xu, Wei zhang, Fei Wang

A popular distributed learning strategy is federated learning, where there is a central server storing the global model and a set of local computing nodes updating the model parameters with their corresponding data.

Federated Learning

Deep Embedded Multi-view Clustering with Collaborative Training

1 code implementation26 Jul 2020 Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu

Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.

Adversarial Machine Learning based Partial-model Attack in IoT

no code implementations25 Jun 2020 Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu

In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.

Decision Making

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

no code implementations8 Jun 2020 Xiongfei Zhai, Xihan Chen, Jie Xu, Derrick Wing Kwan Ng

It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas.

Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

no code implementations5 Jun 2020 Ganggang Ma, Jie Xu, Ya-Feng Liu, Mohammad R. Vedady Moghadam

Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT).

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

no code implementations9 Apr 2020 Jie Xu, Heqiang Wang

This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data.

Federated Learning Stochastic Optimization

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

no code implementations29 Feb 2020 Xiaopeng Mo, Jie Xu

Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update.

Information Theory Signal Processing Information Theory

Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming

no code implementations28 Feb 2020 Hailiang Xie, Jie Xu, Ya-Feng Liu

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with several multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference.


D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

no code implementations28 Jan 2020 Xiaoran Cai, Xiaopeng Mo, Junyang Chen, Jie Xu

Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources.

Federated Learning for Healthcare Informatics

no code implementations13 Nov 2019 Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.

Federated Learning

D3PG: Deep Differentiable Deterministic Policy Gradients

no code implementations25 Sep 2019 Tao Du, Yunfei Li, Jie Xu, Andrew Spielberg, Kui Wu, Daniela Rus, Wojciech Matusik

Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.

Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

1 code implementation IJCNLP 2019 Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, Jie Xu

Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts.

Denoising Relation Extraction

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

1 code implementation16 Jul 2019 Mengwei Yang, Linqi Song, Jie Xu, Congduan Li, Guozhen Tan

Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.

Anomaly Detection Federated Learning +1

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

no code implementations4 May 2019 Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu

In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.

Bilevel Distance Metric Learning for Robust Image Recognition

no code implementations NeurIPS 2018 Jie Xu, Lei Luo, Cheng Deng, Heng Huang

Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems.

Metric Learning

Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward

no code implementations NeurIPS 2018 Lixing Chen, Jie Xu, Zhuo Lu

In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions.

Decision Making Multi-Armed Bandits +1

Spatio-temporal Edge Service Placement: A Bandit Learning Approach

no code implementations7 Oct 2018 Lixing Chen, Jie Xu, Shaolei Ren, Pan Zhou

To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm.

Decision Making Edge-computing

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

no code implementations14 Mar 2018 Zihao Liu, Tao Liu, Wujie Wen, Lei Jiang, Jie Xu, Yanzhi Wang, Gang Quan

To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification.

Data Compression General Classification +2

A Contextual Bandit Approach for Stream-Based Active Learning

no code implementations24 Jan 2017 Linqi Song, Jie Xu

The key feature of our algorithm is that in addition to sending a query to an annotator for the ground truth, prior information about the ground truth learned by the learner is sent together, thereby reducing the query cost.

Active Learning Decision Making

Efficient Estimation in the Tails of Gaussian Copulas

no code implementations5 Jul 2016 Kalyani Nagaraj, Jie Xu, Raghu Pasupathy, Soumyadip Ghosh

The first of our proposed estimators $\estOpt$ is the "full-information" estimator that actively exploits such local structure to achieve bounded relative error in Gaussian settings.

Personalized Course Sequence Recommendations

no code implementations30 Dec 2015 Jie Xu, Tianwei Xing, Mihaela van der Schaar

Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs.

Multi-Armed Bandits

SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

1 code implementation8 Nov 2015 Duorui Xie, Lingyu Liang, Lianwen Jin, Jie Xu, Mengru Li

In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception.

Predicting Grades

no code implementations16 Aug 2015 Yannick Meier, Jie Xu, Onur Atan, Mihaela van der Schaar

We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years.

Forecasting Popularity of Videos using Social Media

no code implementations22 Mar 2014 Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media.

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