JOURNAL & PEER-REVIEWED CONFERENCE PAPERS
- On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. Lam M. Nguyen*, Trang H. Tran*. The 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 (26.1% acceptance rate) [PDF] [Bibtex]
- Analyzing Generalization of Neural Networks through Loss Path Kernels. Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Tsui-Wei Weng. The 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2021 (26.1% acceptance rate) [PDF] [Bibtex]
- Attacking c-MARL More Effectively: A Data Driven Approach. Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng. The 23rd IEEE International Conference on Data Mining (ICDM 2023), 2023 (19.94% acceptance rate) [PDF] [Bibtex]
- Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, and Tsui-Wei Weng. The 23rd IEEE International Conference on Data Mining (ICDM 2023), 2023 (19.94% acceptance rate) [PDF] [Bibtex]
- 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. The 40th International Conference on Machine Learning (ICML 2023), 2023 (27.9% acceptance rate) [PDF] [Poster] [Bibtex]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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
- 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]
- 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]
- 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]
- 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]
- 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]
- 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
- Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent. Toan N. Nguyen*, Phuong Ha Nguyen*, Lam M. Nguyen, Marten Van Dijk. Technical report, arXiv preprint, 2023 [PDF] [Bibtex]
- Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach. Anh Duy Nguyen, Trang H. Tran, Hieu H. Pham, Phi Le Nguyen, Lam M. Nguyen. Technical report, arXiv preprint, 2023 [PDF] [Bibtex]
- An End-to-End Time Series Model for Simultaneous Imputation and Forecast. Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung Phan, Roman Vaculin, Jayant Kalagnanam. Technical report, arXiv preprint, 2023 [PDF] [Bibtex]
- 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]
- Finding Optimal Policy for Queueing Models: New Parameterization. Trang H. Tran, Lam M. Nguyen, Katya Scheinberg. Technical report, arXiv preprint, 2022 [PDF] [Poster] [Bibtex]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takac. Technical report, arXiv preprint, 2017 [PDF] [Bibtex]
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
- 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
- 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