Houping Xiao
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Houping Xiao - Publications

2024
"Asymmetric Mutual Learning for Decentralized Federated Medical Imaging", with Jiaqi Wang and Fenglong Ma, 2024.
ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) (Oral Presentation).
Health Care Federated Learning Mutual Learning Artificial Intelligence
"FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models", with Xiaochen Wang, Jiaqi Wang, Jinghui Chen and Fenglong Ma, 2024.
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Main).
Health Care Federated Learning Multitask Multimodal Learning Artificial Intelligence
"Drivers of Stroke Independence During Inpatient Rehabilitation Care: Causal Forest Modeling", with Anderson, Raeda K., Chloe Sellers, Yusen Xia, and Aaron Baird, 2024.
American Congress of Rehabilitation Medicine Conference. Dallas, Texas. (October – November) (ACRM) (Poster).
Health Care Causal Inference Double Machine Learning Stroke and REhabilitation
"Spinal Cord Injury Inpatient Recovery: Sequence Modeling with Emphasis on Physical and Occupational Therapy", with Anderson, Raeda K., Aaron Baird, Chloe Sellers, Péter Molnár, and Yusen Xia, 2024.
American Congress of Rehabilitation Medicine Conference. Dallas, Texas. (October – November) (ACRM) (Poster).
Health Care Sequence modeling Rehabilitation
"Predicting digital product performance with team composition features derived from a graph network", with Aaron Baird and Yusen Xia, 2024.
Decision Support Systems.
Digital Product Performance Team Composition Graph Network Analysis Machine Learning Interpretability Interpretable Machine Learning
"A Two-Step Item Bank Calibration Strategy based on 1-bit Matrix Completion for Small-Scale Computerized Adaptive Testing", with Yawei Shen and Shiyu Wang, 2024.
British Journal of Mathematical and Statistical Psychology.
Educational Psychology Machine Learning Matrix Completion Small Big Analysis Computerized Adaptive Testing
"MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation", with Yuan Zhong, Suhan Cui, Jiaqi Wang, Xiaochen Wang, Ziyi Yin, Yaqing Wang, Mengdi Huai, Ting Wang, and Fenglong Ma, 2024.
SIAM International Conference on Data Mining (SDM).
Health Risk Prediction EHR Diffusion Model
2023
"Charting By Machines", with Scott Murray and Yusen Xia, 2023.
Journal of Financial Economics, Forthcoming.
Codes Videos Slides Deep Learning CNN LSTM Big Data Efficient Market Hypothesis Charting Cross Section of Stock Returns
"Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT", with Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang and Fenglong Ma, 2023.
SIAM International Conference on Data Mining (SDM).
Federated Learning Semi-Supervised learning Pseudo Labeling
2022
"A Joint Maximum Likelihood Estimation Framework for Truth Discovery: A Unified Perspective", with Shiyu Wang, 2022.
IEEE Transactions on Knowledge and Data Engineering.
Truth Discovery Crowdsourcing Joint Maximum Likelihood Estimation Profile Likelihood Estimation Asymptotic Consistency
"Toward Quality of Information Aware Distributed Machine Learning", with Shiyu Wang, 2022.
ACM Transactions on Knowledge Discovery from Data.
Truth Discovery Distributed Learning Machine Learning Quality of Information Artificial Intelligence
2021
"FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning", with Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang and Fenglong Ma, 2021.
IEEE International Conference on Big Data (Big Data).
Federated Learning Semi-Supervised learning Pseudo Labeling
"Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing With Response Revision", with Shiyu Wang and Allan Cohen, 2021.
Journal of Educational and Behavioral Statistics.
Educational Psychology Machine Learning Adaptive Weight Estimation Robust Estimation Computerized Adaptive Testing Response Revision
"FedCon: A Contrastive Framework for Federated Semi-Supervised Learning", with Zewei Long, Jiaqi Wang, Yaqing Wang and Fenglong Ma, 2021.
Preprint arXiv.
Federated Learning Semi-Supervised learning Contrastive Labeling
"Can Machines Understand Human Decisions? Dissecting Stock Forecasting Skill", with Sean Cao, Xuxi Guo and Baozhong Yang, 2021.
Semi-finalist of the FMA 2021 Best Paper in FinTech
Slides Deep Learning CNN Big Data Artificial Intelligence Analyst Forecast Analyst Skill Crowd Wisdom
Slides Machine Learning Textual Analysis Natural Language Processing Artificial Intelligence Sentiment Analysis Conference Calls
2020
"Fedsiam: Towards adaptive federated semi-supervised learning", with Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, junyu Luo, Jinwe Wu and Fenglong Ma, 2020.
Preprint arXiv.
Federated Learning Semi-Supervised learning No-IID
"Towards differentially private truth discovery for crowd sensing systems", with Yaliang Li, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao Kui Ren and Bolin Ding. 2020.
IEEE International Conference on Distributed Computing Systems (ICDCS).
Truth Discovery Differential Privacy Data Aggregation
"Toward Effective Mobile Promotion: A Survey of Mobile Prediction Techniques and Applications", with Kai Zhao and Arun Rai. 2020.
AMCIS Data Science and Analytics for Decision Support (SIGDSA).
Survey Mobilie Promotion Mobility Predictive Models
"Rare Disease Prediction by Generating Quality-Assured Electronic Health Records", with Fenglong Ma, Yaqing Wang Jing Gao and Jing Zhou. 2020.
SIAM International Conference on Data Mining (SDM).
Data Mining Deep Learning Generative Model Reinforcement Learning Health Care EHR
2019
"IProWA: A novel probabilistic graphical model for crowdsourcing aggregation", with Tianqi Wang, Fenglong Ma and Jing Gao. 2019.
IEEE International Conference on Big Data (Big Data).
Data Mining Crowdsourcing Item Parameter Estimation Probabilistic Graphic Model
"Incorporating medical code descriptions for diagnosis prediction in healthcare", with Fenglong Ma, Yaqing Wang, Ye Yuan, Radha Chitta, Jing Zhou and Jing Gao. 2019.
BMC Medical Informatics and Decision Making.
Healthcare Informatics Diagnosis Prediction Deep Learning Medical Code Embeddings
"Privacy-preserving truth discovery in crowd sensing systems", with Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Jing Gao and Kui Ren. 2019.
ACM Transactions on Sensor Networks.
Crowd Sensing Truth Discovery Privacy-Preserving
Data Mining Machine Learning Anomaly Detection Classification
2018
"Kame: Knowledge-based attention model for diagnosis prediction in healthcare", with Fenglong Ma, Quanzeng You, Radha Chitta, Jing Zhou and Jing Gao. 2018.
ACM International Conference on Information and Knowledge Management (CIKM).
Healthcare Informatics Medical Knowledge Graph Knowledge Attention Mechanism Data Mining EHR
"A general framework for diagnosis prediction via incorporating medical code descriptions", with Fenglong Ma, Yaqing Wang, Ye Yuan, Radha Chitta, Jing Zhou and Jing Gao. 2018.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
Medical Diagnostic Imaging Predictive Models Recurrent Neural Networks Feature Extraction
"Towards confidence interval estimation in truth discovery", with Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng and Aidong Zhang. 2018.
IEEE Transactions on Knowledge and Data Engineering.
Truth Discovery Data Mining Confidence Interval Estimation Bootstrapping
"Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems", with Haiming Jin, Lu Su and Klara Nahrstedt. 2018.
IEEE/ACM Transactions on Networking.
Machine Learning Incentive Mechanism Data Aggregation Privacy Preservation Mobile Crowd Sensing
"Towards data poisoning attacks in crowd sensing systems", with Chenglin Miao, Qi Li, Wenjun Jiang, Mengdi Huai and Lu Su. 2018.
ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).
Crowd Sensing Data Mining Truth Discovery Data Posioning
"Developing synthesis flows without human knowledge", with Cunxi Yu and Giovanni De Micheli. 2018.
Annual Design Automation Conference (DAC).
Deep Learning CNN Synthesis FLow Design Human Knowledge
"eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors", with Fei Wang, Fenglong Ma and Jing Gao. 2018.
IEEE International Conference on Data Mining (ICDM).
Tensor Decomposition Tucker Decomposition Low Rankness Online Learning
"Multi-sourced information trustworthiness analysis: Applications and theory", 2018.
State University of New York at Buffalo.
Best Dissertation Award, CSE@SUNY Buffalo.
Multi-sourced Learning Truth Discovery Crowdsourcing Data Mining Machine Learning
Before 2018
"Learning temporal state of diabetes patients via combining behavioral and demographic data", with Jing Gao, Long Vu and Deepak Turaga. 2017.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
Healthcare Informatics Hidden Markov Model Probablistic Graph Model Data Mining
"Unsupervised discovery of drug side-effects from heterogeneous data sources", with Fenglong Ma, Chuishi Meng, Qi Li, Jing Gao, Lu Su and Aidong Zhang. 2017.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
Healthcare Informatics Truth Discovery Probablistic Graph Model Drug Side-effects
"Detecting malicious behavior in computer networks via cost-sensitive and connectivity constrained classification", with Jing Gao, Long Vu and Deepak Turaga. 2017.
SIAM International Conference on Data Mining (SDM).
Anomaly Detection Optimization Stochastic Gradient Descent
"Unsupervised multisource temporal anomaly detection", with Alain Biem, Jing Gao, Long Vu and Deepak Turaga. 2017.
United States Patent Application Publication.
Multi-sourced learning Anomaly Detection
"Tackling the redundancy and sparsity in crowd sensing applications", with Chuishi Meng, Lu Su and Yun Cheng. 2017.
ACM Conference on Embedded Network Sensor Systems (SenSys).
Crowd Sensing Data Sparsity Matrix Factorization Truth Discovery
"Influence-aware truth discovery", with Hengtong Zhang, Qi Li, Fenglong Ma, Yaliang Li, Jing Gao and Lu Su. 2016.
ACM International on Conference on Information and Knowledge Management (CIKM).
Truth Discovery Unsupervised Learning Probablistic Graph Model
"Inception: Incentivizing privacy-preserving data aggregation for mobile crowd sensing systems", with Haiming Jin, Lu Su and Klara Nahrstedt. 2016.
ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).
Crowd Sensing Incentive Mechanism Data Aggregation Truth Discovery Privacy
"Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach", with Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng and Aidong Zhang. 2016.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
Truth Discovery Data Veracity Optimization Bootstrapping
"A Truth Discovery Approach with Theoretical Guarantee", with Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su and Han Liu. 2016.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
Truth Discovery Mixture Model Asymptotic Consistency
"DRN: Bringing Greedy Layer-Wise Training into Time Dimension", with Xiaoyi Li, Xiaowei Jia, Hui Li, Jing Gao and Aidong Zhang. 2015.
IEEE International Conference on Data Mining (ICDM).
Deep Recurrent Network Sequential Data Modeling
"Multi-source information trustworthiness analysis", with Jing Gao. 2015.
IEEE International Conference on Data Mining Workshop (ICDMW).
Multi-sourced Learning Truth Discovery Trustworthiness Analysis
"Cloud-enabled privacy-preserving truth discovery in crowd sensing systems", with Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Jing Gao and Kui Ren. 2015.
ACM Conference on Embedded Networked Sensor Systems (SenSys).
Crowd Sensing Truth Discovery Privacy Cloud
"Believe it today or tomorrow? detecting untrustworthy information from dynamic multi-source data", with Jing Gao, Long Vu and Deepak Turaga. 2015.
SIAM International Conference on Data Mining (SDM).
Multi-sourced Data Tensor Decomposition Sparsity Gradient Descent
"Temporal multi-view inconsistency detection for network traffic analysis", with Jing Gao, Deepak S Turaga, Long H Vu and Alain Biem. 2015.
International Conference on World Wide Web (WWW).
Multi-sourced Data Tensor Decomposition Gradient Descent