Lam M. Nguyen

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 84

Top Topics

Convergence Rate

Multivariate Decision Trees

Stochastic Gradient

Top Venues

Publications

Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam, Chandra Reddy
Multi-polytope Machine for Classification. SDM (2024)
Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Lily Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
One Step Closer to Unbiased Aleatoric Uncertainty Estimation. AAAI (2024)
Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen
Shuffling Momentum Gradient Algorithm for Convex Optimization. CoRR (2024)
Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. AAAI (2024)
Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung T. Phan, Hoang Thanh Lam
TabularFM: An Open Framework For Tabular Foundational Models. CoRR (2024)
Quang Minh Nguyen, Lam M. Nguyen, Subhro Das
Correlated Attention in Transformers for Multivariate Time Series. CoRR (2023)
Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng
Attacking c-MARL More Effectively: A Data Driven Approach. ICDM (2023)
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. CoRR (2023)
Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. CoRR (2023)
Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng
Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS (2023)
Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. ICML (2023)
Vinícius Lima, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
Optimal Control via Linearizable Deep Learning. ACC (2023)
Anh Duy Nguyen, Trang H. Tran, Hieu H. Pham, Phi Le Nguyen, Lam M. Nguyen
Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach. CoRR (2023)
Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng
Label-Free Concept Bottleneck Models. CoRR (2023)
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung Phan, Roman Vaculín, Jayant Kalagnanam
An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR (2023)
Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. CoRR (2023)
Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng
Label-free Concept Bottleneck Models. ICLR (2023)
Wang Zhang, Ziwen Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
One step closer to unbiased aleatoric uncertainty estimation. CoRR (2023)
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM (2023)
Quang Minh Nguyen, Nhan Khanh Le, Lam M. Nguyen
Scalable and Secure Federated XGBoost. ICASSP (2023)
Toan N. Nguyen, Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent. CoRR (2023)
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR (2023)
Lam M. Nguyen, Trang H. Tran, Marten van Dijk
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution. CoRR (2022)
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy
Interpretable Clustering via Multi-Polytope Machines. AAAI (2022)
Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. CoRR (2022)
Trang H. Tran, Katya Scheinberg, Lam M. Nguyen
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. ICML (2022)
Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen, Lam M. Nguyen
Generalizing DP-SGD with Shuffling and Batching Clipping. CoRR (2022)
Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
Finding Optimal Policy for Queueing Models: New Parameterization. CoRR (2022)
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
A hybrid stochastic optimization framework for composite nonconvex optimization. Math. Program. 191 (2) (2022)
Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen
On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport. CoRR (2022)
Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen
StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. SDM (2022)
Lam M. Nguyen, Trang H. Tran
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. CoRR (2022)
Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk
Besting the Black-Box: Barrier Zones for Adversarial Example Defense. IEEE Access 10 (2022)
Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam
Finite-sum smooth optimization with SARAH. Comput. Optim. Appl. 82 (3) (2022)
Jayant Kalagnanam, Dzung T. Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, Dharmashankar Subramanian, Raju Pavuluri, Xiang Ma, Crystal Lui, Giovane Cesar Da Silva
AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. INFORMS J. Appl. Anal. 52 (4) (2022)
Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng
Evaluating Robustness of Cooperative MARL: A Model-based Approach. CoRR (2022)
Lam M. Nguyen, Katya Scheinberg, Martin Takác
Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. 36 (1) (2021)
Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk
A Unified Convergence Analysis for Shuffling-Type Gradient Methods. J. Mach. Learn. Res. 22 (2021)
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
Federated Learning with Randomized Douglas-Rachford Splitting Methods. CoRR (2021)
Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo
Ensembling Graph Predictions for AMR Parsing. NeurIPS (2021)
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen
Differential Private Hogwild! over Distributed Local Data Sets. CoRR (2021)
Hoang Thanh Lam, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo
Ensembling Graph Predictions for AMR Parsing. CoRR (2021)
Nhuong V. Nguyen, Toan N. Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam M. Nguyen, Marten van Dijk
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. AISTATS (2021)
Dzung T. Phan, Lam M. Nguyen, Pavankumar Murali, Nhan H. Pham, Hongsheng Liu, Jayant R. Kalagnanam
Regression Optimization for System-level Production Control. ACC (2021)
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. NeurIPS (2021)
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy
Interpretable Clustering via Multi-Polytope Machines. CoRR (2021)
Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
On the Equivalence between Neural Network and Support Vector Machine. CoRR (2021)
Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
On the Equivalence between Neural Network and Support Vector Machine. NeurIPS (2021)
Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
SMG: A Shuffling Gradient-Based Method with Momentum. ICML (2021)
Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. CoRR (2020)
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. AISTATS (2020)
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Quoc Tran-Dinh, Phuong Ha Nguyen
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. CoRR (2020)
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. J. Mach. Learn. Res. 21 (2020)
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. CoRR (2020)
Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk
A Unified Convergence Analysis for Shuffling-Type Gradient Methods. CoRR (2020)
Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. CoRR (2020)
Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
Shuffling Gradient-Based Methods with Momentum. CoRR (2020)
Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. NeurIPS (2020)
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Quoc Tran-Dinh, Phuong Ha Nguyen
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise. CoRR (2020)
Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. NeurIPS (2020)
Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. ICML (2020)
Dzung T. Phan, Lam M. Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam
Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization. ICDM (2020)
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. CoRR (2019)
Phuong Ha Nguyen, Kaleel Mahmood, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk
BUZz: BUffer Zones for defending adversarial examples in image classification. CoRR (2019)
Lily Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Akhilan Boopathy, Ivan V. Oseledets, Luca Daniel
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. ICML (2019)
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. CoRR (2019)
Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. ICML (2019)
Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. CoRR (2019)
Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD. NeurIPS (2019)
Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. 20 (2019)
Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg
When Does Stochastic Gradient Algorithm Work Well? CoRR (2018)
Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. CoRR (2018)
Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD. CoRR (2018)
Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. CoRR (2018)
Dhaval Patel, Lam M. Nguyen, Akshay Rangamani, Shrey Shrivastava, Jayant Kalagnanam
ChieF: A Change Pattern based Interpretable Failure Analyzer. IEEE BigData (2018)
Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
New Convergence Aspects of Stochastic Gradient Algorithms. CoRR (2018)
Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. ICML (2018)
Lam M. Nguyen, Katya Scheinberg, Martin Takác
Inexact SARAH Algorithm for Stochastic Optimization. CoRR (2018)
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan V. Oseledets, Luca Daniel
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. CoRR (2018)
Lam M. Nguyen, Alexander L. Stolyar
A queueing system with on-demand servers: local stability of fluid limits. Queueing Syst. Theory Appl. 89 (3-4) (2018)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR (2017)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR (2017)
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML (2017)
Lam M. Nguyen, Alexander L. Stolyar
A Service System with Randomly Behaving On-demand Agents. SIGMETRICS (2016)