PUBLICATIONS
Research areas: Optimization, Machine Learning, Deep Learning, Reinforcement Learning, Federated Learning, Stochastic Models, Optimal Control.

JOURNAL & PEER-REVIEWED CONFERENCE PAPERS

  1. Scalable and Secure Federated XGBoost.
    Quang Nguyen*, Nhan Khanh Le*, Lam M. Nguyen.
    The 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), 2023 [PDF] [Bibtex]
    @INPROCEEDINGS{Nguyen2023_FedXGBoost,
      author={Quang Nguyen and Nhan Khanh Le and Lam M. Nguyen},
      booktitle={The 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)}, 
      title={Scalable and Secure Federated XGBoost}, 
      year={2023},
      volume={},
      number={},
      pages={},
      doi={}}
    
  2. Label-free Concept Bottleneck Models.
    Tuomas Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng.
    The 11th International Conference on Learning Representations (ICLR 2023), 2023 [PDF] [Bibtex]
    @INPROCEEDINGS{9482638,
      author={Oikarinen, Tuomas and Das, Subhro and Nguyen, Lam M. and Weng, Tsui-Wei},
      booktitle={International Conference on Learning Representations (ICLR 2023)}, 
      title={Label-free Concept Bottleneck Models}, 
      year={2023},
      volume={},
      number={},
      pages={},
      doi={}}
    
  3. Optimal Control via Linearizable Deep Learning.
    Vinicius Lima, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam.
    The 2023 American Control Conference (ACC 2023), 2023 [PDF] [Bibtex]
    @INPROCEEDINGS{Lima2023_OCLDL,
      author={Lima, Vinicius and Phan, Dzung T. and Nguyen, Lam M. and Kalagnanam, Jayant R.},
      booktitle={2023 American Control Conference (ACC)}, 
      title={Optimal Control via Linearizable Deep Learning}, 
      year={2023},
      volume={},
      number={},
      pages={},
      doi={}}
    
  4. Nesterov Accelerated Shuffling Gradient Method for Convex Optimization.
    Trang H. Tran, Katya Scheinberg, Lam M. Nguyen.
    The 39th International Conference on Machine Learning (ICML 2022), 2022 (21.9% acceptance rate) [PDF] [Poster] [Bibtex]
    @InProceedings{Tran2022_ShufflingNesterov,
      title = 	 {{N}esterov Accelerated Shuffling Gradient Method for Convex Optimization},
      author =       {Tran, Trang H and Scheinberg, Katya and Nguyen, Lam M},
      booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
      pages = 	 {21703--21732},
      year = 	 {2022},
      editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
      volume = 	 {162},
      series = 	 {Proceedings of Machine Learning Research},
      month = 	 {17--23 Jul},
      publisher =    {PMLR},
      pdf = 	 {https://proceedings.mlr.press/v162/tran22a/tran22a.pdf},
      url = 	 {https://proceedings.mlr.press/v162/tran22a.html}
    }
  5. Finite-Sum Smooth Optimization with SARAH.
    Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam.
    Computational Optimization and Applications (COAP), 2022 [PDF] [Bibtex]
    @ARTICLE{Nguyen2019_SARAH,
      author = {Lam M. Nguyen and Marten van Dijk and Dzung T. Phan and Phuong Ha Nguyen and Tsui-Wei Weng and Jayant R. Kalagnanam},
      title = {Finite-Sum Smooth Optimization with SARAH},
      journal = {arXiv preprint arXiv:1901.07648},
      year = {2019}
    }
  6. AI-based Real-time Site-wide Optimization for Process Manufacturing.
    Jayant Kalagnanam, Dzung Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, Dharmashankar Subramanian, Raju Pavuluri, Xiang Ma, Crystal Lui, Giovane Cesar da Silva.
    INFORMS Journal on Applied Analytics (IJAA), 2022 [PDF] [Bibtex]
  7. StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions.
    Dzung Phan, Hongsheng Liu, Lam M. Nguyen.
    SIAM International Conference on Data Mining (SDM22), 2022 (27.8% acceptance rate) [PDF] [Bibtex]
    
    
  8. Besting the Black-Box: Barrier Zones for Adversarial Example Defense.
    Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk.
    IEEE Access, 2022 [PDF] [Bibtex]
    @ARTICLE{Nguyen2019_Buzz,
      author = {Phuong Ha Nguyen and Kaleel Mahmood and Lam M. Nguyen and Thanh Nguyen and and Marten van Dijk},
      title = {BUZz: BUffer Zones for Defending Adversarial Examples in Image Classification},
      journal = {arXiv preprint arXiv:1910.02785},
      year = {2019}
    }
  9. Interpretable Clustering via Multi-Polytope Machines.
    Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung Phan, Chandra Reddy.
    The 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 2022 (15% acceptance rate) [PDF] [Bibtex]
    
    
  10. FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization.
    Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen.
    The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 (26% acceptance rate) [PDF] [CODE] [Bibtex]
    @ARTICLE{TranDinh2021_FLDR,
      author = {Quoc Tran-Dinh and Nhan H. Pham and Dzung T. Phan and Lam M. Nguyen},
      title = {FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization},
      journal = {arXiv preprint arXiv:2103.03452},
      year = {2021}
    }
  11. Ensembling Graph Predictions for AMR Parsing.
    Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramon Fernandez Astudillo.
    The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 (26% acceptance rate) [PDF] [Bibtex]
    
    
  12. On the Equivalence between Neural Network and Support Vector Machine.
    Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng.
    The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 (26% acceptance rate) [PDF] [Bibtex]
    
    
  13. A Unified Convergence Analysis for Shuffling-Type Gradient Methods.
    Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk.
    Journal of Machine Learning Research (JMLR), 2021 [PDF] [Bibtex]
    @article{Nguyen2021ShufflingUnified,
      author  = {Lam M. Nguyen and Quoc Tran-Dinh and Dzung T. Phan and Phuong Ha Nguyen and Marten van Dijk},
      title   = {A Unified Convergence Analysis for Shuffling-Type Gradient Methods},
      journal = {Journal of Machine Learning Research},
      year    = {2021},
      volume  = {22},
      number  = {207},
      pages   = {1-44},
      url     = {http://jmlr.org/papers/v22/20-1238.html}
    }
  14. SMG: A Shuffling Gradient-Based Method with Momentum.
    Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh.
    The 38th International Conference on Machine Learning (ICML 2021), PMLR volume 139, 2021 (21.47% acceptance rate) [PDF] [Poster] [VIDEO] [Bibtex]
    @InProceedings{pmlr-v139-tran21b,
      title = 	 {SMG: A Shuffling Gradient-Based Method with Momentum},
      author =       {Tran, Trang H and Nguyen, Lam M and Tran-Dinh, Quoc},
      booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
      pages = 	 {10379--10389},
      year = 	 {2021},
      editor = 	 {Meila, Marina and Zhang, Tong},
      volume = 	 {139},
      series = 	 {Proceedings of Machine Learning Research},
      month = 	 {18--24 Jul},
      publisher =    {PMLR},
      pdf = 	 {http://proceedings.mlr.press/v139/tran21b/tran21b.pdf},
      url = 	 {http://proceedings.mlr.press/v139/tran21b.html}
    }
  15. Regression Optimization for System-level Production Control.
    Dzung T. Phan, Lam M. Nguyen, Pavankumar Murali, Nhan H. Pham, Hongsheng Liu, Jayant R. Kalagnanam.
    The 2021 American Control Conference (ACC 2021), 2021 [PDF] [Bibtex]
    @INPROCEEDINGS{9482638,
      author={Phan, Dzung T. and Nguyen, Lam M. and Murali, Pavankumar and Pham, Nhan H. and Liu, Hongsheng and Kalagnanam, Jayant R.},
      booktitle={2021 American Control Conference (ACC)}, 
      title={Regression Optimization for System-level Production Control}, 
      year={2021},
      volume={},
      number={},
      pages={5023-5028},
      doi={10.23919/ACC50511.2021.9482638}}
    
  16. Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes.
    Nhuong V. Nguyen, Toan N. Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam M. Nguyen, Marten van Dijk.
    The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021 (29.8% acceptance rate) [PDF] [Bibtex]
    @ARTICLE{vanDijk_HogwildDistributed,
      author = {Marten van Dijk and Nhuong V. Nguyen and Toan N. Nguyen and Lam M. Nguyen and Quoc Tran-Dinh and Phuong Ha Nguyen},
      title = {Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes},
      journal = {arXiv preprint arXiv:2010.14763},
      year = {2020}
    }
  17. A Hybrid Stochastic Optimization Framework for Composite Nonconvex Optimization.
    Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen.
    Mathematical Programming (MAPR), 2021 [PDF] [Bibtex]
    @ARTICLE{Tran-Dinh2019_Hybrid_Composite,
      author = {Quoc Tran-Dinh and Nhan H. Pham and Dzung T. Phan and Lam M. Nguyen},
      title = {A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization},
      journal = {arXiv preprint arXiv:1907.03793},
      year = {2019}
    }
  18. Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems.
    Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen.
    The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 (20.1% acceptance rate) [PDF] [Bibtex]
    @ARTICLE{TranDinh2020_HybridMinMax,
      author = {Quoc Tran-Dinh and Deyi Liu and Lam M. Nguyen},
      title = {Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems},
      journal = {arXiv preprint arXiv:2006.15266},
      year = {2020}
    }
  19. A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees.
    Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam.
    The 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 (20.1% acceptance rate) [PDF] [Bibtex]
    @article{zhu2020scalable,
      title={A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees},
      author={Zhu, Haoran and Murali, Pavankumar and Phan, Dzung T and Nguyen, Lam M and Kalagnanam, Jayant R}
    }
  20. Inexact SARAH Algorithm for Stochastic Optimization.
    Lam M. Nguyen, Katya Scheinberg, Martin Takac.
    Optimization Methods and Software (GOMS), volume 36(1), 237-258, 2020. DOI 10.1080/10556788.2020.1818081 [PDF] [Poster] [Bibtex]
    @article{Nguyen2020_iSARAH_GOMS,
    author = {Lam M.   Nguyen  and  Katya   Scheinberg  and  Martin Takac},
    title = {Inexact SARAH Algorithm for Stochastic Optimization},
    journal = {Optimization Methods and Software},
    volume = {0},
    number = {0},
    pages = {1-22},
    year = {2020},
    publisher = {Taylor & Francis},
    doi = {10.1080/10556788.2020.1818081},
    }
  21. Pruning Deep Neural Networks with L0-constrained Optimization.
    Dzung T. Phan, Lam M. Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam.
    The 20th IEEE International Conference on Data Mining (ICDM 2020), 2020 (19.7% acceptance rate) [PDF] [Bibtex]
    
    
  22. Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization.
    Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen.
    The 37th International Conference on Machine Learning (ICML 2020), PMLR volume 119, 2020 (21.8% acceptance rate) [PDF] [Bibtex]
    @ARTICLE{Tran-Dinh2020_SGN,
      author = {Quoc Tran-Dinh and Nhan H. Pham and Lam M. Nguyen},
      title = {Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization},
      journal = {arXiv preprint arXiv:2002.07290},
      year = {2020}
    }
  23. ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization.
    Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh.
    Journal of Machine Learning Research (JMLR), volume 21, 1-48, 2020 [PDF] [CODE] [Bibtex]
    @article{Pham2020_ProxSARAH,
      author  = {Nhan H. Pham and Lam M. Nguyen and Dzung T. Phan and Quoc Tran-Dinh},
      title   = {ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization},
      journal = {Journal of Machine Learning Research},
      year    = {2020},
      volume  = {21},
      number  = {110},
      pages   = {1-48},
      url     = {http://jmlr.org/papers/v21/19-248.html}
    }
  24. A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning.
    Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh.
    The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), PMLR volume 108, 2020 [PDF] [Bibtex]
    @InProceedings{Pham2020_HyrbidRL,
      title = 	 {A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning},
      author = 	 {Pham, Nhan and Nguyen, Lam and Phan, Dzung and Nguyen, Phuong Ha and van Dijk, Marten and Tran-Dinh, Quoc},
      booktitle = 	 {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
      pages = 	 {374--385},
      year = 	 {2020},
      editor = 	 {Chiappa, Silvia and Calandra, Roberto},
      volume = 	 {108},
      series = 	 {Proceedings of Machine Learning Research},
      address = 	 {Online},
      month = 	 {26--28 Aug},
      publisher = 	 {PMLR},
      pdf = 	 {http://proceedings.mlr.press/v108/pham20a/pham20a.pdf},
      url = 	 {http://proceedings.mlr.press/v108/pham20a.html}
    }
  25. New Convergence Aspects of Stochastic Gradient Algorithms.
    Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtarik, Katya Scheinberg, Martin Takac, Marten van Dijk.
    Journal of Machine Learning Research (JMLR), volume 20(176), 1-49, 2019 [PDF] [Bibtex]
    @article{Nguyen2019_sgd_new_aspects,
      author  = {Lam M. Nguyen and Phuong Ha Nguyen and Peter Richt{{\'a}}rik and Katya Scheinberg and Martin Tak{{\'a}}{\v{c}} and Marten van Dijk},
      title   = {New Convergence Aspects of Stochastic Gradient Algorithms},
      journal = {Journal of Machine Learning Research},
      year    = {2019},
      volume  = {20},
      number  = {176},
      pages   = {1-49},
      url     = {http://jmlr.org/papers/v20/18-759.html}
    }
  26. Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD.
    Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk.
    The 33th Conference on Neural Information Processing Systems (NeurIPS 2019), 2019 (21.17% acceptance rate) [PDF] [Bibtex]
    @incollection{NIPS2019_8624,
    title = {Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD},
    author = {NGUYEN, PHUONG\_HA and Nguyen, Lam and van Dijk, Marten},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
    pages = {3660--3669},
    year = {2019},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/8624-tight-dimension-independent-lower-bound-on-the-expected-convergence-rate-for-diminishing-step-sizes-in-sgd.pdf}
    }
    
  27. PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach.
    Tsui-Wei Weng, Pin-Yu Chen*, Lam M. Nguyen*, Mark S. Squillante*, Akhilan Boopathy, Ivan Oseledets, Luca Daniel.
    The 36th International Conference on Machine Learning (ICML 2019), PMLR volume 97, 6727-6736, 2019 (22.5% acceptance rate) [PDF] [Bibtex]
    @InProceedings{Weng2018_PROVEN,
      title = 	 {PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach},
      author = 	 {Tsui-Wei Weng and Pin-Yu Chen and Lam M. Nguyen and Mark S. Squillante and Akhilan Boopathy and Ivan Oseledets and Luca Daniel},
      booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
      pages = 	 {6727--6736},
      year = 	 {2019},
      volume = 	 {97},
      series = 	 {Proceedings of Machine Learning Research}
    }
  28. Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD.
    Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan.
    The 36th International Conference on Machine Learning (ICML 2019), PMLR volume 97, 6392-6400, 2019 (22.5% acceptance rate) [PDF] [Bibtex]
    @InProceedings{vanDijk2018_sgdconvex,
      title = 	 {Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD},
      author = 	 {Marten van Dijk and Lam M. Nguyen and Phuong Ha Nguyen and Dzung T. Phan},
      booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
      pages = 	 {6392--6400},
      year = 	 {2019},
      volume = 	 {97},
      series = 	 {Proceedings of Machine Learning Research}
    }
  29. ChieF: A Change Pattern based Interpretable Failure Analyzer.
    Dhaval Patel, Lam M. Nguyen, Akshay Rangamani, Shrey Shrivastava, Jayant Kalagnanam.
    2018 IEEE International Conference on Big Data (IEEE BigData 2018). DOI 10.1109/BigData.2018.8622596 [PDF] [Bibtex]
    @INPROCEEDINGS{ChieF_Bigdata, 
    author={D. Patel and L. M. Nguyen and A. Rangamani and S. Shrivastava and J. Kalagnanam}, 
    booktitle={2018 IEEE International Conference on Big Data (Big Data)}, 
    title={ChieF: A Change Pattern based Interpretable Failure Analyzer}, 
    year={2018}, 
    volume={}, 
    number={}, 
    pages={1978-1985}, 
    keywords={Change Pattern Algorithms;Failure-Centric Knowledge Extraction;Data Analysis}, 
    doi={10.1109/BigData.2018.8622596}, 
    ISSN={}, 
    month={Dec},}
    
  30. SGD and Hogwild! Convergence Without the Bounded Gradients Assumption.
    Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac.
    The 35th International Conference on Machine Learning (ICML 2018), PMLR volume 80, 3747-3755, 2018. (25% acceptance rate) [PDF] [Poster] [VIDEO] [Bibtex]
    @InProceedings{Nguyen2018_sgdhogwild,
      title = 	 {{SGD} and {H}ogwild! {C}onvergence Without the Bounded Gradients Assumption},
      author = 	 {Nguyen, Lam and Nguyen, Phuong Ha and van Dijk, Marten and Richtarik, Peter and Scheinberg, Katya and Takac, Martin},
      booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
      pages = 	 {3747--3755},
      year = 	 {2018},
      volume = 	 {80},
      series = 	 {Proceedings of Machine Learning Research}
    }
  31. SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient.
    Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takac.
    The 34th International Conference on Machine Learning (ICML 2017), PMLR volume 70, 2613-2621, 2017. (25% acceptance rate) [PDF] [Poster] [VIDEO] [Bibtex]
    @InProceedings{Nguyen2017_sarah,
      title = {{SARAH}: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient},
      author = {Lam M. Nguyen and Jie Liu and Katya Scheinberg and Martin Takac},
      booktitle = {Proceedings of the 34th International Conference on Machine Learning},
      pages = {2613--2621},
      year = {2017},
      volume = {70},
      series = {Proceedings of Machine Learning Research},
    }
  32. A Queueing System with On-demand Servers: Local Stability of Fluid Limits.
    Lam M. Nguyen, Alexander L. Stolyar.
    Queueing Systems (QUESTA), volume 89, issue 3-4, 243-268, 2018, (Springer). DOI 10.1007/s11134-017-9564-8 [PDF] [Bibtex]
    @Article{Nguyen2017_fluid,
    author = "Nguyen, Lam M. and Stolyar, Alexander L.",
    title = "A queueing system with on-demand servers: local stability of fluid limits",
    journal = "Queueing Systems",
    year = "2017",
    month = "Nov",
    day = "22",
    issn = "1572-9443",
    doi = "10.1007/s11134-017-9564-8",
    url = "https://doi.org/10.1007/s11134-017-9564-8"
    }
  33. A Service System with Randomly Behaving On-demand Agents.
    Lam M. Nguyen, Alexander L. Stolyar.
    The 42nd ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS 2016), ACM SIGMETRICS Performance Evaluation Review, volume 44, issue 1, 365-366, 2016. DOI 10.1145/2964791.2901484 (25% acceptance rate) [PDF] [Poster] [Bibtex]
    @article{Nguyen2016_agent,
     author = {Nguyen, Lam M. and Stolyar, Alexander L.},
     title = {A Service System with Randomly Behaving On-demand Agents},
     journal = {SIGMETRICS Perform. Eval. Rev.},
     issue_date = {June 2016},
     volume = {44},
     number = {1},
     month = jun,
     year = {2016},
     issn = {0163-5999},
     pages = {365--366},
     numpages = {2},
     url = {http://doi.acm.org/10.1145/2964791.2901484},
     doi = {10.1145/2964791.2901484},
     acmid = {2901484},
     publisher = {ACM},
     address = {New York, NY, USA},
    }
  34. CEO Compensation: Does Financial Crisis Matter?
    Prasad Vemala, Lam Nguyen, Dung Nguyen, Alekhya Kommasani.
    International Business Research, volume 7, issue 4, 125-131, 2014. DOI 10.5539/ibr.v7n4p125 [PDF]

PEER-REVIEWED WORKSHOP PAPERS

  1. c-MBA: Adversarial Attack for Cooperative MARL Using Learned Dynamics Model.
    Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng.
    The 36th Conference on Neural Information Processing Systems (NeurIPS 2022), ML Safety, 2022 [PDF] [Bibtex]
    
    
  2. Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping.
    Wang Zhang, Lam M. Nguyen, Subhro Das, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng.
    The 39th International Conference on Machine Learning (ICML 2022), Decision Awareness in Reinforcement Learning, 2022 [PDF] [Bibtex]
    
    
  3. Robust Randomized Smoothing via Two Cost-Effective Approaches.
    Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng.
    The 10th International Conference on Learning Representations (ICLR 2022), PAIR2Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data, 2022 [PDF] [Bibtex]
    
    
  4. Addressing Solution Quality in Data Generated Optimization Models.
    Orit Davidovich, Parikshit Ram, Segev Wasserkrug, Dharmashankar Subramanian, Nianjun Zhou, Dzung Phan, Pavankumar Murali, Lam M. Nguyen.
    The 36th AAAI Conference on Artificial Intelligence (AAAI 2022), AI for Decision Optimization, AI4DO, 2022 [PDF] [Bibtex]
    
    
  5. Automated Decision Optimization: Data and Knowledge Driven Optimization Model Generation with Human-in-the-loop.
    Lisa Amini, Arunima Chaudhary, Yishai Feldman, Pavankumar Murali, Lam M. Nguyen, Dzung Phan, Aviad Sela, Carolina Spina, Dharmashankar Subramanian, Abel Valente, Long Vu, Dakuo Wang, Segev Wasserkrug, Ritesh Yadav, Nianjun Zhou.
    The 36th AAAI Conference on Artificial Intelligence (AAAI 2022), AI for Decision Optimization, AI4DO, 2022 [PDF] [Bibtex]
    
    
  6. Ensuring the Quality of Optimization Solutions in Data Generated Optimization Models.
    Segev Wasserkrug, Orit Davidovith, Evgeny Shindin, Dharmashankar Subramanian, Parikshit Ram, Pavankumar Murali, Dzung Phan, Nianjun Zhou, Lam M. Nguyen.
    The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), Data Science Meets Optimisation, DSO@IJCAI2021, 2021 [PDF] [Bibtex]
    
    

TECHNICAL REPORTS

  1. ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction.
    Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen.
    Technical report, arXiv preprint, 2023 [PDF] [Bibtex]
    @ARTICLE{zhang2023_concernet,
      author = {Wang Zhang and Tsui-Wei Weng and Subhro Das and Alexandre Megretski and Luca Daniel and Lam M. Nguyen},
      title = {ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction},
      journal = {arXiv preprint arXiv:2302.05783},
      year = {2023}
    }
  2. Generalizing DP-SGD with Shuffling and Batching Clipping.
    Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen, Lam M. Nguyen.
    Technical report, arXiv preprint, 2022 [PDF] [Bibtex]
    @ARTICLE{vanDijk2022_SGDDP,
      author = {Marten van Dijk and Phuong Ha Nguyen and Toan N. Nguyen and Lam M. Nguyen},
      title = {Generalizing DP-SGD with Shuffling and Batching Clipping},
      journal = {arXiv preprint arXiv:2212.05796},
      year = {2022}
    }
  3. Finding Optimal Policy for Queueing Models: New Parameterization.
    Trang H. Tran, Lam M. Nguyen, Katya Scheinberg.
    Technical report, arXiv preprint, 2022 [PDF] [Poster] [Bibtex]
    @ARTICLE{Tran2022_Queue,
      author = {Trang H. Tran and Lam M. Nguyen and Katya Scheinberg},
      title = {Finding Optimal Policy for Queueing Models: New Parameterization},
      journal = {arXiv preprint arXiv:2206.10073},
      year = {2022}
    }
  4. On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms.
    Lam M. Nguyen*, Trang H. Tran*.
    Technical report, arXiv preprint, 2022 [PDF] [Bibtex]
    @ARTICLE{Nguyen2022_SGDPL,
      author = {Lam M. Nguyen and Trang H. Tran},
      title = {On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms},
      journal = {arXiv preprint arXiv:2206.05869},
      year = {2022}
    }
  5. Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution.
    Lam M. Nguyen*, Trang H. Tran*, Marten van Dijk.
    Technical report, arXiv preprint, 2022 [PDF] [Bibtex]
    @ARTICLE{Nguyen2022_OptDL,
      author = {Lam M. Nguyen and Trang H. Tran and Marten van Dijk},
      title = {Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution},
      journal = {arXiv preprint arXiv:2202.03524},
      year = {2022}
    }
  6. On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error.
    Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen.
    Technical report, arXiv preprint, 2022 [PDF] [Bibtex]
    @ARTICLE{Nguyen2022_UOT,
      author = {Quang Minh Nguyen and Hoang H. Nguyen and Yi Zhou and Lam M. Nguyen},
      title = {On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error},
      journal = {arXiv preprint arXiv:2202.03618},
      year = {2022}
    }
  7. Evaluating Robustness of Cooperative MARL: A Model-based Approach.
    Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng.
    Technical report, arXiv preprint, 2022 [PDF] [Bibtex]
    @ARTICLE{Pham2022_MARL,
      author = {Nhan H. Pham and Lam M. Nguyen and Jie Chen and Hoang Thanh Lam and Subhro Das and Tsui-Wei Weng},
      title = {Evaluating Robustness of Cooperative MARL: A Model-based Approach},
      journal = {arXiv preprint arXiv:2202.03558},
      year = {2022}
    }
  8. Differential Private Hogwild! over Distributed Local Data Sets.
    Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen.
    Technical report, arXiv preprint, 2021 [PDF] [Bibtex]
    @ARTICLE{vanDijk_HogwildDistributed,
      author = {Marten van Dijk and Nhuong V. Nguyen and Toan N. Nguyen and Lam M. Nguyen and Phuong Ha Nguyen},
      title = {Differential Private Hogwild! over Distributed Local Data Sets},
      journal = {arXiv preprint arXiv:2102.09030},
      year = {2021}
    }
  9. Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning.
    Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg.
    Technical report, arXiv preprint, 2020 [PDF] [Bibtex]
    @ARTICLE{Doan2020_RLMarkovian,
      author = {Thinh T. Doan and Lam M. Nguyen and Nhan H. Pham and Justin Romberg},
      title = {Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning},
      journal = {arXiv preprint arXiv:2002.02873},
      year = {2020}
    }
  10. An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization.
    Deyi Liu, Lam M. Nguyen, Quoc Tran-Dinh.
    Technical report, arXiv preprint, 2020 [PDF] [Bibtex]
    @ARTICLE{tran2020shuffmom,
      author = {Deyi Liu and Lam M. Nguyen and Quoc Tran-Dinh},
      title = {An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization},
      journal = {arXiv preprint arXiv:2008.09055},
      year = {2020}
    }
  11. Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise.
    Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Quoc Tran-Dinh, Phuong Ha Nguyen.
    Technical report, arXiv preprint, 2020 [PDF] [Bibtex]
    @ARTICLE{vanDijk_AsynFL_2020,
      author = {Marten van Dijk and Nhuong V. Nguyen and Toan N. Nguyen and Lam M. Nguyen and Quoc Tran-Dinh and Phuong Ha Nguyen},
      title = {Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise},
      journal = {arXiv preprint arXiv:2007.09208},
      year = {2020}
    }
  12. Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness.
    Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg.
    Technical report, arXiv preprint, 2020 [PDF] [Bibtex]
    @ARTICLE{Doan2020_FTASGDMR,
      author = {Thinh T. Doan and Lam M. Nguyen and Nhan H. Pham and Justin Romberg},
      title = {Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness},
      journal = {arXiv preprint arXiv:2003.10973},
      year = {2020}
    }
  13. Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization.
    Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen.
    Technical report, arXiv preprint, 2019 [PDF] [Bibtex]
    @ARTICLE{Tran-Dinh2019_Hybrid,
      author = {Quoc Tran-Dinh and Nhan H. Pham and Dzung T. Phan and Lam M. Nguyen},
      title = {Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization},
      journal = {arXiv preprint arXiv:1905.05920},
      year = {2019}
    }
  14. When Does Stochastic Gradient Algorithm Work Well?
    Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg.
    Technical report, arXiv preprint, 2018 [PDF] [Bibtex]
    @article{Nguyen2018_sgdlrdnn,
      author    = {Lam M. Nguyen and
                   Nam H. Nguyen and
                   Dzung T. Phan and
                   Jayant R. Kalagnanam and
                   Katya Scheinberg},
      title     = {When Does Stochastic Gradient Algorithm Work Well?},
      journal   = {CoRR},
      volume    = {abs/1801.06159},
      year      = {2018},
      biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1801-06159}
    }
  15. Stochastic Recursive Gradient Algorithm for Nonconvex Optimization.
    Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takac.
    Technical report, arXiv preprint, 2017 [PDF] [Bibtex]
    @article{Nguyen2017_sarahnonconvex,
      author    = {Lam M. Nguyen and
                   Jie Liu and
                   Katya Scheinberg and
                   Martin Tak{\'{a}}c},
      title     = {Stochastic Recursive Gradient Algorithm for Nonconvex Optimization},
      journal   = {CoRR},
      volume    = {abs/1705.07261},
      year      = {2017},
      biburl    = {http://dblp.org/rec/bib/journals/corr/NguyenLST17a}
    }

THESES

  • A Service System with On-Demand Agents, Stochastic Gradient Algorithms and the SARAH Algorithm.
    Lam M. Nguyen.
    Ph.D. dissertation, 2018 [PDF]
  • Methods for Detecting Hidden Period in Some Economics Processes.
    Lam M. Nguyen.
    Undergraduate thesis, 2008

ORGANIZING WORKSHOPS

  1. When Machine Learning meets Dynamical Systems: Theory and Applications.
    Lam M. Nguyen, Trang H. Tran, Wang Zhang, Subhro Das, Tsui-Wei Weng.
    Workshop at The 37th Conference on Artificial Intelligence (AAAI 2023), 2023
  2. New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership.
    Nghia Hoang*, Lam M. Nguyen*, Pin-Yu Chen, Tsui-Wei Weng, Sara Magliacane, Bryan Kian Hsiang Low, Anoop Deoras.
    Workshop at The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021

Lam M. Nguyen

(Nguyễn Minh Lâm)
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