Sparse frequency diverse MIMO radar imaging. - Changchang Liu; Weidong Chen 010 (ACSCC, 2012)

A correction and generalization to the sparse learning via iterative minimization method for target off the grid in MIMO radar imaging. - Changchang Liu; Li Ding; Weidong Chen 010 (ACSCC, 2012)

Sparse self-calibration by map method for MIMO radar imaging. - Changchang Liu; Jin Yan; Weidong Chen 010 (ICASSP, 2012)

Sparsity-driven high resolution FDLFM-MIMO radar imaging. - Li Ding; Changchang Liu; Weidong Chen 010; Wenyi Zhang 001 (WCSP, 2012)

Sparse imaging for passive radar system based on digital video broadcasting satellites. - Changchang Liu; Tianyun Wang; Li Ding; Weidong Chen 010 (WCSP, 2012)

Analyzing the FD-MIMO sparse imaging under carrier frequency offsets from the perspective of point spread function. - Li Ding; Changchang Liu; Weidong Chen 010 (ACSSC, 2013)

Sparse Self-Calibration Imaging via Iterative MAP in FM-Based Distributed Passive Radar. - Changchang Liu; Weidong Chen 010 (IEEE Geosci. Remote. Sens. Lett., 2013)

Sparse Frequency Diverse MIMO Radar Imaging for Off-Grid Target Based on Adaptive Iterative MAP. - Xuezhi He; Changchang Liu; Bo Liu; Dongjin Wang (Remote. Sens., 2013)

PARS: A Uniform and Open-source Password Analysis and Research System. - Shouling Ji; Shukun Yang; Ting Wang 006; Changchang Liu; Wei-Han Lee; Raheem A. Beyah (ACSAC, 2015)

Exploiting Temporal Dynamics in Sybil Defenses. - Changchang Liu; Peng Gao 008; Matthew K. Wright; Prateek Mittal (CCS, 2015)

LinkMirage: How to Anonymize Links in Dynamic Social Systems. - Changchang Liu; Prateek Mittal (CoRR, 2015)

LinkMirage: Enabling Privacy-preserving Analytics on Social Relationships. - Changchang Liu; Prateek Mittal (NDSS, 2016)

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples. - Changchang Liu; Supriyo Chakraborty; Prateek Mittal (NDSS, 2016)

A parameter-free label propagation algorithm for person identification in stereo videos. - Chongsheng Zhang; Jingjun Bi; Changchang Liu; Ke Chen 004 (Neurocomputing, 2016)

Quantification of De-anonymization Risks in Social Networks. - Wei-Han Lee; Changchang Liu; Shouling Ji; Prateek Mittal; Ruby B. Lee (ICISSP, 2017)

How to Quantify Graph De-anonymization Risks. - Wei-Han Lee; Changchang Liu; Shouling Ji; Prateek Mittal; Ruby B. Lee (ICISSP (Revised Selected Papers), 2017)

Blind De-anonymization Attacks using Social Networks. - Wei-Han Lee; Changchang Liu; Shouling Ji; Prateek Mittal; Ruby B. Lee (WPES@CCS, 2017)

DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications. - Changchang Liu; Supriyo Chakraborty; Prateek Mittal (CoRR, 2017)

Coupling Dimensionality Reduction with Generative Model for Non-Interactive Private Data Release. - Thee Chanyaswad; Changchang Liu; Prateek Mittal (CoRR, 2017)

An up-to-date comparison of state-of-the-art classification algorithms. - Chongsheng Zhang; Changchang Liu; Xiangliang Zhang 001; George Almpanidis (Expert Syst. Appl., 2017)

Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption. - Changchang Liu; Supriyo Chakraborty; Dinesh C. Verma (PADG@ESORICS, 2018)

An empirical evaluation of high utility itemset mining algorithms. - Chongsheng Zhang; George Almpanidis; Wanwan Wang; Changchang Liu (Expert Syst. Appl., 2018)

Selective Detection and Segmentation of Cervical Cells. - Jing Ke; Zhaoming Jiang; Changchang Liu; Tomasz Bednarz; Arcot Sowmya; Xiaoyao Liang (ICBBT, 2019)

Exact Incremental and Decremental Learning for LS-SVM. - Wei-Han Lee; Bong Jun Ko; Shiqiang Wang 001; Changchang Liu; Kin K. Leung (ICIP, 2019)

RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release. - Thee Chanyaswad; Changchang Liu; Prateek Mittal (Proc. Priv. Enhancing Technol., 2019)

Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing. - Changchang Liu; Xi He 001; Thee Chanyaswad; Shiqiang Wang 001; Prateek Mittal (Proc. Priv. Enhancing Technol., 2019)

Rethinking the Science of Statistical Privacy - Changchang Liu (2019)

FIMIL : A high-throughput deep learning model for abnormality detection with weak annotation in microscopy images. - Jing Ke; Changchang Liu; Yizhou Lu; Naifeng Jing; Xiaoyao Liang; Fusong Jiang (ACSW, 2020)

Text Captcha Is Dead? A Large Scale Deployment and Empirical Study. - Chenghui Shi; Shouling Ji; Qianjun Liu; Changchang Liu; Yuefeng Chen; Yuan He 011; Zhe Liu 001; Raheem Beyah; Ting Wang 006 (CCS, 2020)

Neuraltran: Optimal Data Transformation for Privacy-Preserving Machine Learning by Leveraging Neural Networks. - Changchang Liu; Wei-Han Lee; Seraphin B. Calo (DSN (Supplements), 2020)

Communication-efficient k-Means for Edge-based Machine Learning. - Hanlin Lu; Ting He 001; Shiqiang Wang 001; Changchang Liu; Mehrdad Mahdavi; Vijaykrishnan Narayanan; Kevin S. Chan; Stephen Pasteris (ICDCS, 2020)

NeuralFP: Out-of-distribution Detection using Fingerprints of Neural Networks. - Wei-Han Lee; Steve Millman; Nirmit Desai; Mudhakar Srivatsa; Changchang Liu (ICPR, 2020)

Overcoming Noisy and Irrelevant Data in Federated Learning. - Tiffany Tuor; Shiqiang Wang 001; Bong Jun Ko; Changchang Liu; Kin K. Leung (ICPR, 2020)

Joint Coreset Construction and Quantization for Distributed Machine Learning. - Hanlin Lu; Changchang Liu; Shiqiang Wang 001; Ting He 001; Vijaykrishnan Narayanan; Kevin S. Chan; Stephen Pasteris (Networking, 2020)

Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients. - Tiffany Tuor; Shiqiang Wang 001; Bong Jun Ko; Changchang Liu; Kin K. Leung (CoRR, 2020)

Sharing Models or Coresets: A Study based on Membership Inference Attack. - Hanlin Lu; Changchang Liu; Ting He 001; Shiqiang Wang 001; Kevin S. Chan (CoRR, 2020)

AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy. - Chengkun Wei; Shouling Ji; Changchang Liu; Wenzhi Chen; Ting Wang 006 (IEEE Trans. Inf. Forensics Secur., 2020)

AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy. - Linkang Du; Zhikun Zhang 001; Shaojie Bai; Changchang Liu; Shouling Ji; Peng Cheng 001; Jiming Chen 001 (CCS, 2021)

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases. - Matthew E. Berginski; Nienke Moret; Changchang Liu; Dennis Goldfarb; Peter K. Sorger; Shawn M. Gomez (Nucleic Acids Res., 2021)

Fast-RCM: Fast Tree-Based Unsupervised Rare-Class Mining. - Haiqin Weng; Shouling Ji; Changchang Liu; Ting Wang 006; Qinming He; Jianhai Chen (IEEE Trans. Cybern., 2021)

A Practical Black-Box Attack on Source Code Authorship Identification Classifiers. - Qianjun Liu; Shouling Ji; Changchang Liu; Chunming Wu 001 (IEEE Trans. Inf. Forensics Secur., 2021)

Joint Coreset Construction and Quantization for Distributed Machine Learning. - Hanlin Lu; Changchang Liu; Shiqiang Wang 001; Ting He 001; Vijay Narayanan; Kevin S. Chan; Stephen Pasteris (CoRR, 2022)

Focus : Function clone identification on cross-platform. - Lirong Fu; Shouling Ji; Changchang Liu; Peiyu Liu 003; Fuzheng Duan; Zonghui Wang; Whenzhi Chen; Ting Wang 006 (Int. J. Intell. Syst., 2022)

Communication-Efficient $k$k-Means for Edge-Based Machine Learning. - Hanlin Lu; Ting He 001; Shiqiang Wang 001; Changchang Liu; Mehrdad Mahdavi; Vijaykrishnan Narayanan; Kevin S. Chan; Stephen Pasteris (IEEE Trans. Parallel Distributed Syst., 2022)

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms. - Tianshu Zhang 001; Changchang Liu; Wei-Han Lee; Yu Su 001; Huan Sun 001 (ACL (1), 2023)

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules. - Changchang Liu; Peter Kutchukian; Nhan D. Nguyen; Mohammed Alquraishi; Peter K. Sorger (J. Chem. Inf. Model., 2023)

Machine-Assisted Error Discovery in Conversational AI Systems. - Maeda F. Hanafi; Frederick Reiss 001; Yannis Katsis; Robert Moore; David Wood; Mohammad Hassan Falakmasir; Changchang Liu (CHI Extended Abstracts, 2024)

LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis. - Tianyu Cui; Shiyu Ma; Ziang Chen; Tong Xiao; Shimin Tao; Yilun Liu 001; Shenglin Zhang; Duoming Lin; Changchang Liu; Yuzhe Cai; Weibin Meng; Yongqian Sun; et al. (CoRR, 2024)

Diagnosing and Prioritizing Issues in Automated Order-Taking Systems: A Machine-Assisted Error Discovery Approach. - Maeda F. Hanafi; Frederick Reiss 001; Yannis Katsis; Robert Moore; Mohammad Hassan Falakmasir; Pauline Wang; David Wood; Changchang Liu (CHI, 2025)

GneissWeb: Preparing High Quality Data for LLMs at Scale. - Hajar Emami-Gohari; Swanand Ravindra Kadhe; Syed Yousaf Shah; Constantin Adam; Abdulhamid Adebayo; Praneet Adusumilli; Farhan Ahmed; Nathalie Baracaldo Angel; Santosh Borse; Yuan Chi Chang; Xuan-Hong Dang; Nirmit Desai; et al. (CoRR, 2025)