Publications
Here is my Google Scholar page.
2025
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Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm.
T. Pham, Y. Wang, C. Yin, X. Zhang and P. Zhang
In the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025.
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DroughtSet: Understanding Drought through Spatial-Temporal Learning.
X. Tan, Q. Zhao, Y. Liu and X. Zhang
In the 39th AAAI Conference on Artificial Intelligence: Special Track on AI for Social Impact (AAAI-AISI), 2025.
2024
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Learning under Imitative Strategic Behavior with Unforeseeable Outcomes.
T. Xie, Z. Zuo, M. Khalili and X. Zhang
In Transactions on Machine Learning Research (TMLR), 2024.
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Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions.
T. Xie and X. Zhang
In the 38th Conference on Neural Information Processing Systems (NeurIPS), 2024.
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Federated Learning with Reduced Information Leakage and Computation.
T. Yin*, X. Tan*, X. Zhang*,M. Khalili and M. Liu
In Transactions on Machine Learning Research (TMLR), 2024.
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Algorithmic Decision-Making under Agents with Persistent Improvement.
T. Xie, X. Tan and X. Zhang
In the 7th AAAI Conference on AI, Ethics, and Society (AIES), Oral presentation, 2024.
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Non-linear Welfare-Aware Strategic Learning.
T. Xie and X. Zhang
In the 7th AAAI Conference on AI, Ethics, and Society (AIES), 2024.
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Privacy-Aware Randomized Quantization via Linear Programming.
Z. Cai, X. Zhang and M. Khalili
In the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
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Non-stationary Domain Generalization: Theory and Algorithm.
T. Pham, X. Zhang and P. Zhang
In the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
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Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts.
K. Jin, T. Yin, Z. Chen, Z. Sun, X. Zhang, Y. Liu and M. Liu
In the 38th AAAI Conference on Artificial Intelligence (AAAI), Oral presentation, 2024.
2023
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Counterfactually Fair Representation.
Z. Zuo, M. Khalili and X. Zhang
In the 37th Conference on Neural Information Processing Systems (NeurIPS), 2023.
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Loss Balancing for Fair Supervised Learning.
M. Khalili, X. Zhang and M. Abroshan
In 40th International Conference on Machine Learning (ICML), 2023.
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Fairness and Accuracy under Domain Generalization.
T. Pham, X. Zhang and P. Zhang
In 11th International Conference on Learning Representations (ICLR), 2023.
2022
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A Fair and Interpretable Network for Clinical Risk Prediction: A Regularized Multi-view Multi-task
Learning Approach.
T. Pham, C. Yin, L. Mehta, X. Zhang and P. Zhang
In Knowledge and Information Systems (KAIS), 2022.
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Fairness Interventions as (Dis)incentives for Strategic Manipulation.
X. Zhang, M. Khalili, K. Jin, P. Naghizadeh and M. Liu
In the 39th International Conference on Machine Learning (ICML), 2022.
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Incentive Mechanisms for Strategic Classification and Regression Problems.
K. Jin, X. Zhang, M. Khalili, P. Naghizadeh and M. Liu
In ACM Conference on Economics and Computation, (EC), 2022.
Contributed Talk in ICLR Workshop on Socially Responsible Machine Learning, 2022.
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Differentially Private Real-Time Release of Sequential Data.
X. Zhang, M. Khalili and M. Liu
ACM Transactions on Privacy and Security (TOPS), 2022.
2021
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Socially Responsible Machine Learning: On the Preservation of Individual Privacy and Fairness.
X. Zhang
PhD Thesis, 2021.
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Fair Sequential Selection Using Supervised Learning Models.
M. Khalili, X. Zhang and M. Abroshan
In the 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
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Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning.
T. Pham, C. Yin, L. Mehta, X. Zhang and P. Zhang
In the IEEE International Conference on Data Mining (ICDM), regular paper, 2021.
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Improving Fairness and Privacy in Selection Problems.
M. Khalili, X. Zhang, M. Abroshan and S. Sojoudi
In the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.
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Fairness in Learning-Based Sequential Decision Algorithms: A Survey.
X. Zhang and M. Liu
Springer Studies in Systems, Decision and Control, Handbook on RL and Control.
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Designing Contracts for Trading Private and Heterogeneous Data Using a Biased Differentially Private Algorithm.
M. Khalili*, X. Zhang* and M. Liu
In IEEE Access, 2021.
2020
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How Do Fair Decisions Fare in Long-term Qualification?
X. Zhang*, R. Tu*, Y. Liu, M. Liu, H. Kjellström, K. Zhang and C. Zhang
In the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
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Resource Pooling for Shared Fate: Incentivizing Effort in Interdependent Security Games through Cross-investments.
M. Khalili, X. Zhang and M. Liu
In IEEE Transactions on Control of Network Systems (TCNS), 2020.
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A Robust Energy and Emissions Conscious Cruise Controller for Connected Vehicles with Privacy Considerations.
C. Huang, X. Zhang, R. Salehi, T. Ersal and A. Stefanopoulou
In 2020 American Control Conference (ACC), 2020.
ASME Automotive and Transportation Systems Best Paper Award Finalist
2019
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Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness.
X. Zhang*, M. Khalili*, C. Tekin and M. Liu
In the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019.
Earlier version: in EC Workshop on Mechanism Design for Social Good (MD4SG), 2019.
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Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms.
X. Zhang, M. Khalili and M. Liu
In IEEE Transactions on Information Forensics and Security (TIFS), 2019.
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Incentivizing Effort in Interdependent Security Games Using Resource Pooling.
M. Khalili, X. Zhang and M. Liu
In the 14th Workshop on the Economics of Networks, Systems and Computation (NetEcon), 2019.
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Effective Premium Discrimination for Designing Cyber Insurance Policies with Rare Losses.
M. Khalili, X. Zhang and M. Liu
In the 10th Conference on Decision and Game Theory for Security (GameSec), 2019.
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Long-term Impacts of Fair Machine Learning.
X. Zhang, M. Khalili and M. Liu
In Ergonomics in Design: The Quarterly of Human Factors Applications, 2019.
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Contract Design for Purchasing Private Data Using a Biased Differentially Private Algorithm.
M. Khalili*, X. Zhang* and M. Liu
In the 14th Workshop on the Economics of Networks, Systems and Computation (NetEcon), 2019.
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Predictive Cruise Control with Private Vehicle-to-Vehicle Communication for Improving Fuel Consumption and Emissions.
X. Zhang*, C. Huang*, M. Liu, A. Stefanopoulou and T. Ersal
In IEEE Communications Magazine, 2019.
2018
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Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms.
X. Zhang, M. Khalili and M. Liu
In the 35th International Conference on Machine Learning (ICML), 2018.
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Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms.
X. Zhang, M. Khalili and M. Liu
In the 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2018.
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Public Good Provision Games on Networks with Resource Pooling.
M. Khalili, X. Zhang and M. Liu
In the International Conference on Network Games Control and Optimization (NetGCoop), 2018.