A Service System with Randomly Behaving On-demand Agents. - Lam M. Nguyen; Alexander L. Stolyar (SIGMETRICS, 2016)

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. - Lam M. Nguyen; Jie Liu 036; Katya Scheinberg; Martin Takác 001 (ICML, 2017)

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. - Lam M. Nguyen; Jie Liu 036; Katya Scheinberg; Martin Takác 001 (CoRR, 2017)

ChieF: A Change Pattern based Interpretable Failure Analyzer. - Dhaval Patel 002; Lam M. Nguyen; Akshay Rangamani; Shrey Shrivastava; Jayant Kalagnanam (IEEE BigData, 2018)

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. - Lam M. Nguyen; Phuong Ha Nguyen; Marten van Dijk; Peter Richtárik; Katya Scheinberg; Martin Takác 001 (ICML, 2018)

When Does Stochastic Gradient Algorithm Work Well? - Lam M. Nguyen; Nam H. Nguyen; Dzung T. Phan; Jayant R. Kalagnanam; Katya Scheinberg (CoRR, 2018)

Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD. - Phuong Ha Nguyen; Lam M. Nguyen; Marten van Dijk (CoRR, 2018)

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. - Tsui-Wei Weng; Pin-Yu Chen; Lam M. Nguyen; Mark S. Squillante; Ivan V. Oseledets; Luca Daniel (CoRR, 2018)

A queueing system with on-demand servers: local stability of fluid limits. - Lam M. Nguyen; Alexander L. Stolyar (Queueing Syst. Theory Appl., 2018)

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. - Marten van Dijk; Lam M. Nguyen; Phuong Ha Nguyen; Dzung T. Phan (ICML, 2019)

PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. - Lily Weng; Pin-Yu Chen; Lam M. Nguyen; Mark S. Squillante; Akhilan Boopathy; Ivan V. Oseledets; Luca Daniel (ICML, 2019)

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 (NeurIPS, 2019)

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (CoRR, 2019)

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (CoRR, 2019)

BUZz: BUffer Zones for defending adversarial examples in image classification. - Phuong Ha Nguyen; Kaleel Mahmood; Lam M. Nguyen; Thanh Nguyen; Marten van Dijk (CoRR, 2019)

New Convergence Aspects of Stochastic Gradient Algorithms. - Lam M. Nguyen; Phuong Ha Nguyen; Peter Richtárik; Katya Scheinberg; Martin Takác 001; Marten van Dijk (J. Mach. Learn. Res., 2019)

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 (AISTATS, 2020)

Pruning Deep Neural Networks with $ ell_{0}$-constrained Optimization. - Dzung T. Phan; Lam M. Nguyen; Nam H. Nguyen; Jayant R. Kalagnanam (ICDM, 2020)

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. - Quoc Tran-Dinh; Nhan H. Pham; Lam M. Nguyen (ICML, 2020)

Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. - Quoc Tran-Dinh; Deyi Liu; Lam M. Nguyen (NeurIPS, 2020)

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. - Haoran Zhu; Pavankumar Murali; Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam (NeurIPS, 2020)

Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. - Thinh T. Doan 001; Lam M. Nguyen; Nhan H. Pham; Justin Romberg (CoRR, 2020)

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 (CoRR, 2020)

Shuffling Gradient-Based Methods with Momentum. - Trang H. Tran; Lam M. Nguyen; Quoc Tran-Dinh (CoRR, 2020)

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. - Nhan H. Pham; Lam M. Nguyen; Dzung T. Phan; Quoc Tran-Dinh (J. Mach. Learn. Res., 2020)

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 (AISTATS, 2021)

Regression Optimization for System-level Production Control. - Dzung T. Phan; Lam M. Nguyen; Pavankumar Murali; Nhan H. Pham; Hongsheng Liu; Jayant R. Kalagnanam (ACC, 2021)

SMG: A Shuffling Gradient-Based Method with Momentum. - Trang H. Tran; Lam M. Nguyen; Quoc Tran-Dinh (ICML, 2021)

On the Equivalence between Neural Network and Support Vector Machine. - Yilan Chen 002; Wei Huang 034; Lam M. Nguyen; Tsui-Wei Weng (NeurIPS, 2021)

Ensembling Graph Predictions for AMR Parsing. - Thanh Lam Hoang; Gabriele Picco; Yufang Hou; Young-Suk Lee 001; Lam M. Nguyen; Dzung T. Phan; Vanessa López; Ramón Fernandez Astudillo (NeurIPS, 2021)

FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (NeurIPS, 2021)

Differential Private Hogwild! over Distributed Local Data Sets. - Marten van Dijk; Nhuong V. Nguyen; Toan N. Nguyen; Lam M. Nguyen; Phuong Ha Nguyen (CoRR, 2021)

Federated Learning with Randomized Douglas-Rachford Splitting Methods. - Nhan H. Pham; Lam M. Nguyen; Dzung T. Phan; Quoc Tran-Dinh (CoRR, 2021)

A Unified Convergence Analysis for Shuffling-Type Gradient Methods. - Lam M. Nguyen; Quoc Tran-Dinh; Dzung T. Phan; Phuong Ha Nguyen; Marten van Dijk (J. Mach. Learn. Res., 2021)

Inexact SARAH algorithm for stochastic optimization. - Lam M. Nguyen; Katya Scheinberg; Martin Takác 001 (Optim. Methods Softw., 2021)

Interpretable Clustering via Multi-Polytope Machines. - Connor Lawless; Jayant Kalagnanam; Lam M. Nguyen; Dzung T. Phan; Chandra Reddy (AAAI, 2022)

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. - Trang H. Tran; Katya Scheinberg; Lam M. Nguyen (ICML, 2022)

StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. - Dzung T. Phan; Hongsheng Liu; Lam M. Nguyen (SDM, 2022)

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)

Finite-sum smooth optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (Comput. Optim. Appl., 2022)

Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution. - Lam M. Nguyen; Trang H. Tran; Marten van Dijk (CoRR, 2022)

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 (CoRR, 2022)

On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport. - Quang Minh Nguyen; Hoang H. Nguyen; Yi Zhou; Lam M. Nguyen (CoRR, 2022)

Finding Optimal Policy for Queueing Models: New Parameterization. - Trang H. Tran; Lam M. Nguyen; Katya Scheinberg (CoRR, 2022)

Generalizing DP-SGD with Shuffling and Batching Clipping. - Marten van Dijk; Phuong Ha Nguyen; Toan N. Nguyen; Lam M. Nguyen (CoRR, 2022)

AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. - Jayant Kalagnanam; Dzung T. Phan; Pavankumar Murali; Lam M. Nguyen; Nianjun Zhou; Dharmashankar Subramanian; Raju Pavuluri; Xiang Ma; Crystal Lui; Giovane Cesar Da Silva (INFORMS J. Appl. Anal., 2022)

A hybrid stochastic optimization framework for composite nonconvex optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (Math. Program., 2022)

Optimal Control via Linearizable Deep Learning. - Vinícius Lima 002; Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam (ACC, 2023)

Scalable and Secure Federated XGBoost. - Quang Minh Nguyen; Nhan Khanh Le; Lam M. Nguyen (ICASSP, 2023)

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. - Linbo Liu; Trong Nghia Hoang; Lam M. Nguyen; Tsui-Wei Weng (ICDM, 2023)

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 (ICDM, 2023)

Label-free Concept Bottleneck Models. - Tuomas P. Oikarinen; Subhro Das; Lam M. Nguyen; Tsui-Wei Weng (ICLR, 2023)

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 (ICML, 2023)

Analyzing Generalization of Neural Networks through Loss Path Kernels. - Yilan Chen 002; Wei Huang 034; Hao Wang; Charlotte Loh; Akash Srivastava; Lam M. Nguyen; Lily Weng (NeurIPS, 2023)

On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. - Lam M. Nguyen; Trang H. Tran (NeurIPS, 2023)

An End-to-End Time Series Model for Simultaneous Imputation and Forecast. - Trang H. Tran; Lam M. Nguyen; Kyongmin Yeo; Nam Nguyen; Dzung T. Phan; Roman Vaculín; Jayant Kalagnanam (CoRR, 2023)

Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach. - Anh Duy Nguyen; Trang H. Tran; Hieu H. Pham 001; Phi Le Nguyen; Lam M. Nguyen (CoRR, 2023)

Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent. - Toan N. Nguyen; Phuong Ha Nguyen; Lam M. Nguyen; Marten van Dijk (CoRR, 2023)

A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. - Trang H. Tran; Lam M. Nguyen; Kyongmin Yeo; Nam Nguyen; Roman Vaculín (CoRR, 2023)

On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error. - Quang Minh Nguyen; Hoang H. Nguyen; Yi Zhou; Lam M. Nguyen (J. Mach. Learn. Res., 2023)

On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. - Anh Duc Nguyen; Tuan Dung Nguyen; Quang Minh Nguyen; Hoang H. Nguyen; Lam M. Nguyen; Kim-Chuan Toh (AAAI, 2024)

One Step Closer to Unbiased Aleatoric Uncertainty Estimation. - Wang Zhang; Ziwen Martin Ma; Subhro Das; Tsui-Wei Lily Weng; Alexandre Megretski; Luca Daniel; Lam M. Nguyen (AAAI, 2024)

TabularFM: An Open Framework For Tabular Foundational Models. - Quan M. Tran; Suong N. Hoang; Lam M. Nguyen; Dzung T. Phan; Hoang Thanh Lam (IEEE Big Data, 2024)

Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder. - Duy A. Nguyen; Trang H. Tran; Huy Hieu Pham 001; Phi Le Nguyen; Lam M. Nguyen (ICDM, 2024)

Proactive DP: A Multiple Target Optimization Framework for DP-SGD. - Marten van Dijk; Nhuong V. Nguyen; Toan N. Nguyen; Lam M. Nguyen; Phuong Ha Nguyen (ICML, 2024)

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization. - Quoc Tran-Dinh; Trang H. Tran; Lam M. Nguyen (NeurIPS, 2024)

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. - Yunshi Wen; Tengfei Ma 001; Lily Weng; Lam M. Nguyen; Anak Agung Julius (NeurIPS, 2024)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Lily Weng; Trong Nghia Hoang (NeurIPS, 2024)

Multi-polytope Machine for Classification. - Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam; Chandra Reddy (SDM, 2024)

Shuffling Momentum Gradient Algorithm for Convex Optimization. - Trang H. Tran; Quoc Tran-Dinh; Lam M. Nguyen (CoRR, 2024)

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks. - Anthony Baez; Wang Zhang; Ziwen Martin Ma; Subhro Das; Lam M. Nguyen; Luca Daniel (CoRR, 2024)

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. - Yunshi Wen; Tengfei Ma 001; Tsui-Wei Weng; Lam M. Nguyen; Anak Agung Julius (CoRR, 2024)

Correlated Attention in Transformers for Multivariate Time Series. - Quang Minh Nguyen; Lam M. Nguyen; Subhro Das (ICASSP, 2025)

Foundation Model and Temporal Priors-guided Transductive Few-shot Action Recognition. - Bach Vu; Hoang Nguyen; Quang Minh Nguyen; Duong Le; Hieu Pham; Phi Le Nguyen; Lam M. Nguyen (ICASSP, 2025)

Dilated Convolution for Time Series Learning. - Wang Zhang; Subhro Das; Lam M. Nguyen; Luca Daniel (ICASSP, 2025)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Tsui-Wei Weng; Trong Nghia Hoang (CoRR, 2025)

Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization. - Lam M. Nguyen; Katya Scheinberg; Trang H. Tran (J. Optim. Theory Appl., 2025)

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting. - Duc Toan Nguyen; Trang H. Tran; Lam M. Nguyen (CoRR, 2025)

Graph Concept Bottleneck Models. - Haotian Xu; Tsui-Wei Weng; Lam M. Nguyen; Tengfei Ma 001 (CoRR, 2025)