Gotcha: a mobile urban sensing system. - Xiangxiang Xu 001; Pei Zhang 001; Lin Zhang 001 (SenSys, 2014)

HAP: Fine-Grained Dynamic Air Pollution Map Reconstruction by Hybrid Adaptive Particle Filter: Poster Abstract. - Xinlei Chen; Xiangxiang Xu 001; Xinyu Liu 003; Hae Young Noh; Lin Zhang 001; Pei Zhang 001 (SenSys, 2016)

Gotcha II: Deployment of a Vehicle-based Environmental Sensing System: Poster Abstract. - Xiangxiang Xu 001; Xinlei Chen; Xinyu Liu 003; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (SenSys, 2016)

Delay Effect in Mobile Sensing System for Urban Air Pollution Monitoring. - Xinyu Liu 003; Xinlei Chen; Xiangxiang Xu 001; Enhan Mai; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (SenSys, 2017)

Individualized Calibration of Industrial-Grade Gas Sensors in Air Quality Sensing System. - Xinyu Liu 003; Xiangxiang Xu 001; Xinlei Chen; Enhan Mai; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (SenSys, 2017)

Guiding the Data Learning Process with Physical Model in Air Pollution Inference. - Rui Ma; Xiangxiang Xu 001; Yue Wang 007; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (IEEE BigData, 2018)

PGA: Physics Guided and Adaptive Approach for Mobile Fine-Grained Air Pollution Estimation. - Xinlei Chen; Xiangxiang Xu 001; Xinyu Liu 003; Shijia Pan; Jiayou He; Hae Young Noh; Lin Zhang 001; Pei Zhang 001 (UbiComp/ISWC Adjunct, 2018)

The Geometric Structure of Generalized Softmax Learning. - Xiangxiang Xu 001; Shao-Lun Huang; Lizhong Zheng; Lin Zhang 001 (ITW, 2018)

Generative Model Based Fine-Grained Air Pollution Inference for Mobile Sensing Systems. - Rui Ma; Xiangxiang Xu 001; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (SenSys, 2018)

An Efficient Approach to Informative Feature Extraction from Multimodal Data. - Lichen Wang; Jiaxiang Wu 001; Shao-Lun Huang; Lizhong Zheng; Xiangxiang Xu 001; Lin Zhang 001; Junzhou Huang (AAAI, 2019)

On the Asymptotic Sample Complexity of HGR Maximal Correlation Functions in Semi-supervised Learning. - Xiangxiang Xu 001; Shao-Lun Huang (Allerton, 2019)

A deep autoencoder model for pollution map recovery with mobile sensing networks. - Rui Ma; Ning Liu; Xiangxiang Xu 001; Yue Wang 007; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (UbiComp/ISWC Adjunct, 2019)

Maximal Correlation Embedding Network for Multilabel Learning with Missing Labels. - Lu Li; Yang Li 104; Xiangxiang Xu 001; Shao-Lun Huang; Lin Zhang 001 (ICME, 2019)

A maximal correlation embedding method for multilabel human context recognition: poster abstract. - Lu Li; Yang Li 104; Xiangxiang Xu 001; Lin Zhang 001 (IPSN, 2019)

On the Robustness of Noisy ACE Algorithm and Multi-Layer Residual Learning. - Shao-Lun Huang; Xiangxiang Xu 001 (ISIT, 2019)

On The Sample Complexity of HGR Maximal Correlation Functions. - Shao-Lun Huang; Xiangxiang Xu 001 (ITW, 2019)

On the Sample Complexity of Estimating Small Singular Modes. - Xiangxiang Xu 001; Weida Wang; Shao-Lun Huang (ISIT, 2020)

A Local Characterization for Wyner Common Information. - Shao-Lun Huang; Xiangxiang Xu 001; Lizhong Zheng; Gregory W. Wornell (ISIT, 2020)

Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution Inference. - Rui Ma; Ning Liu; Xiangxiang Xu 001; Yue Wang 007; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (IEEE Access, 2020)

Maximal Correlation Regression. - Xiangxiang Xu 001; Shao-Lun Huang (IEEE Access, 2020)

On the Optimal Tradeoff Between Computational Efficiency and Generalizability of Oja's Algorithm. - Xiangxiang Xu 001; Shao-Lun Huang (IEEE Access, 2020)

Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep Autoencoder Model. - Rui Ma; Ning Liu; Xiangxiang Xu 001; Yue Wang 007; Hae Young Noh; Pei Zhang 001; Lin Zhang 001 (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2020)

An Information-Theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data. - Shao-Lun Huang; Xiangxiang Xu 001; Lizhong Zheng (IEEE J. Sel. Areas Inf. Theory, 2020)

Dual Feature Distributional Regularization for Defending Against Adversarial Attacks. - Mingyang Li; Xiangxiang Xu 001; Shao-Lun Huang; Lin Zhang 001 (ICONIP (6), 2021)

An Information Theoretic Framework for Distributed Learning Algorithms. - Xiangxiang Xu 001; Shao-Lun Huang (ISIT, 2021)

On Distributed Hypothesis Testing with Constant-Bit Communication Constraints. - Xiangxiang Xu 001; Shao-Lun Huang (ITW, 2021)

A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning. - Xinyi Tong 002; Xiangxiang Xu 001; Shao-Lun Huang; Lizhong Zheng (NeurIPS, 2021)

Maximum Likelihood Estimation for Multimodal Learning with Missing Modality. - Fei Ma 006; Xiangxiang Xu 001; Shao-Lun Huang; Lin Zhang 001 (CoRR, 2021)

On the Sample Complexity of HGR Maximal Correlation Functions for Large Datasets. - Shao-Lun Huang; Xiangxiang Xu 001 (IEEE Trans. Inf. Theory, 2021)

On Sample Complexity of Learning Shared Representations: The Asymptotic Regime. - Xinyi Tong 002; Xiangxiang Xu 001; Shao-Lun Huang (Allerton, 2022)

Multivariate Feature Extraction. - Xiangxiang Xu 001; Lizhong Zheng (Allerton, 2022)

Multi-source Transfer Learning for Signal Detection over a Fading Channel with Co-channel Interference. - Ziyan Zheng; Xinyi Tong 002; Xinchun Yu; Xiangxiang Xu 001; Shao-Lun Huang (ICC, 2022)

An Information Theoretic Interpretation to Deep Neural Networks. - Xiangxiang Xu 001; Shao-Lun Huang; Lizhong Zheng; Gregory W. Wornell (Entropy, 2022)

On Distributed Learning With Constant Communication Bits. - Xiangxiang Xu 001; Shao-Lun Huang (IEEE J. Sel. Areas Inf. Theory, 2022)

Adaptive Hybrid Model-Enabled Sensing System (HMSS) for Mobile Fine-Grained Air Pollution Estimation. - Xinlei Chen; Susu Xu; Xinyu Liu 003; Xiangxiang Xu 001; Hae Young Noh; Lin Zhang 001; Pei Zhang 001 (IEEE Trans. Mob. Comput., 2022)

Robust Transfer Learning Based on Minimax Principle. - Xinyi Tong 002; Xiangxiang Xu 001; Shao-Lun Huang; Lizhong Zheng (Allerton, 2023)

Sequential Dependence Decomposition and Feature Learning. - Xiangxiang Xu 001; Lizhong Zheng (Allerton, 2023)

Neural Feature Learning for Engineering Problems. - Xiangxiang Xu 001; Lizhong Zheng; Ishank Agrawal (Allerton, 2023)

Kernel Subspace and Feature Extraction. - Xiangxiang Xu 001; Lizhong Zheng (ISIT, 2023)

A Geometric Framework for Neural Feature Learning. - Xiangxiang Xu 001; Lizhong Zheng (CoRR, 2023)

On the Optimal Error Exponent of Type-Based Distributed Hypothesis Testing. - Xinyi Tong 002; Xiangxiang Xu 001; Shao-Lun Huang (Entropy, 2023)

Dependence Induced Representations. - Xiangxiang Xu 001; Lizhong Zheng (Allerton, 2024)

Operator SVD with Neural Networks via Nested Low-Rank Approximation. - Jongha Jon Ryu; Xiangxiang Xu 001; Hasan Sabri Melihcan Erol; Yuheng Bu; Lizhong Zheng; Gregory W. Wornell (ICML, 2024)

Multiuser Detection With Neural Feature Learning. - Xiangxiang Xu 001; Lizhong Zheng (MILCOM, 2024)

Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning. - Chaoyu Zhang; Shanghao Shi; Ning Wang 022; Xiangxiang Xu 001; Shaoyu Li; Lizhong Zheng; Randy C. Marchany; Mark Gardner; Y. Thomas Hou 001; Wenjing Lou (MobiHoc, 2024)

Neural Feature Learning in Function Space. - Xiangxiang Xu 001; Lizhong Zheng (J. Mach. Learn. Res., 2024)