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ORCIDLam M. Nguyen
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- affiliation: IBM Research, Thomas J. Watson Research Center, USA
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2020 – today
- 2024
[c33]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: 8090-8098- [c32]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: 16857-16864 - [c31]Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen:
Proactive DP: A Multiple Target Optimization Framework for DP-SGD. ICML 2024 - [c30]Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam, Chandra Reddy:
Multi-polytope Machine for Classification. SDM 2024: 109-117 - [i44]Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen:
Shuffling Momentum Gradient Algorithm for Convex Optimization. CoRR abs/2403.03180 (2024) - [i43]Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung T. Phan, Hoang Thanh Lam:
TabularFM: An Open Framework For Tabular Foundational Models. CoRR abs/2406.09837 (2024) - 2023
- [j10]Quang Minh Nguyen, Hoang H. Nguyen
, Yi Zhou, Lam M. Nguyen:
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error. J. Mach. Learn. Res. 24: 384:1-384:41 (2023) - [c29]Vinícius Lima, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam:
Optimal Control via Linearizable Deep Learning. ACC 2023: 100-105 - [c28]Quang Minh Nguyen, Nhan Khanh Le, Lam M. Nguyen:
Scalable and Secure Federated XGBoost. ICASSP 2023: 1-5 - [c27]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM 2023: 1145-1150 - [c26]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: 1271-1276 - [c25]Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng:
Label-free Concept Bottleneck Models. ICLR 2023 - [c24]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: 41694-41714 - [c23]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 - [c22]Lam M. Nguyen, Trang H. Tran:
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. NeurIPS 2023 - [i42]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 abs/2302.05783 (2023) - [i41]Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng:
Label-Free Concept Bottleneck Models. CoRR abs/2304.06129 (2023) - [i40]Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung T. Phan, Roman Vaculín, Jayant Kalagnanam:
An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR abs/2306.00778 (2023) - [i39]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 abs/2306.06579 (2023) - [i38]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 abs/2307.11939 (2023) - [i37]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR abs/2310.07780 (2023) - [i36]Quang Minh Nguyen, Lam M. Nguyen, Subhro Das:
Correlated Attention in Transformers for Multivariate Time Series. CoRR abs/2311.11959 (2023) - [i35]Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín:
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. CoRR abs/2311.12290 (2023) - [i34]Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
One step closer to unbiased aleatoric uncertainty estimation. CoRR abs/2312.10469 (2023) - [i33]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 abs/2312.13970 (2023) - 2022
- [j9]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: 1451-1474 (2022) - [j8]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): 561-593 (2022) - [j7]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): 363-378 (2022) - [j6]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): 1005-1071 (2022) - [c21]Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy:
Interpretable Clustering via Multi-Polytope Machines. AAAI 2022: 7309-7316 - [c20]Trang H. Tran, Katya Scheinberg, Lam M. Nguyen:
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. ICML 2022: 21703-21732 - [c19]Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen:
StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. SDM 2022: 477-485 - [i32]Lam M. Nguyen, Trang H. Tran, Marten van Dijk:
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution. CoRR abs/2202.03524 (2022) - [i31]Trang H. Tran, Lam M. Nguyen, Katya Scheinberg:
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. CoRR abs/2202.03525 (2022) - [i30]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 abs/2202.03558 (2022) - [i29]Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen:
On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport. CoRR abs/2202.03618 (2022) - [i28]Lam M. Nguyen, Trang H. Tran:
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. CoRR abs/2206.05869 (2022) - [i27]Trang H. Tran, Lam M. Nguyen, Katya Scheinberg:
Finding Optimal Policy for Queueing Models: New Parameterization. CoRR abs/2206.10073 (2022) - [i26]Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen, Lam M. Nguyen:
Generalizing DP-SGD with Shuffling and Batching Clipping. CoRR abs/2212.05796 (2022) - 2021
- [j5]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: 207:1-207:44 (2021) - [j4]Lam M. Nguyen, Katya Scheinberg, Martin Takác:
Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. 36(1): 237-258 (2021) - [c18]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: 1207-1215 - [c17]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: 5023-5028 - [c16]Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh:
SMG: A Shuffling Gradient-Based Method with Momentum. ICML 2021: 10379-10389 - [c15]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: 8495-8505 - [c14]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. NeurIPS 2021: 23478-23490 - [c13]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: 30326-30338 - [i25]Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen:
Differential Private Hogwild! over Distributed Local Data Sets. CoRR abs/2102.09030 (2021) - [i24]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh:
Federated Learning with Randomized Douglas-Rachford Splitting Methods. CoRR abs/2103.03452 (2021) - [i23]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 abs/2110.09131 (2021) - [i22]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. CoRR abs/2111.06063 (2021) - [i21]Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy:
Interpretable Clustering via Multi-Polytope Machines. CoRR abs/2112.05653 (2021) - 2020
- [j3]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: 110:1-110:48 (2020) - [c12]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: 374-385 - [c11]Dzung T. Phan, Lam M. Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam:
Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization. ICDM 2020: 1214-1219 - [c10]Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen:
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. ICML 2020: 9572-9582 - [c9]Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen:
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. NeurIPS 2020 - [c8]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 - [i20]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 abs/2002.08246 (2020) - [i19]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 abs/2003.00430 (2020) - [i18]Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg:
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. CoRR abs/2003.10973 (2020) - [i17]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 abs/2007.09208 (2020) - [i16]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 abs/2010.14763 (2020) - [i15]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 abs/2011.03375 (2020) - [i14]Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh:
Shuffling Gradient-Based Methods with Momentum. CoRR abs/2011.11884 (2020)
2010 – 2019
- 2019
- [j2]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: 176:1-176:49 (2019) - [c7]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: 6392-6400 - [c6]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: 6727-6736 - [c5]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: 3660-3669 - [i13]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 abs/1901.07648 (2019) - [i12]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh:
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. CoRR abs/1902.05679 (2019) - [i11]Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen:
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. CoRR abs/1907.03793 (2019) - [i10]Phuong Ha Nguyen, Kaleel Mahmood, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk:
BUZz: BUffer Zones for defending adversarial examples in image classification. CoRR abs/1910.02785 (2019) - 2018
- [j1]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): 243-268 (2018) - [c4]Dhaval Patel, Lam M. Nguyen, Akshay Rangamani, Shrey Shrivastava, Jayant Kalagnanam:
ChieF: A Change Pattern based Interpretable Failure Analyzer. IEEE BigData 2018: 1978-1985 - [c3]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: 3747-3755 - [i9]Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg:
When Does Stochastic Gradient Algorithm Work Well? CoRR abs/1801.06159 (2018) - [i8]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 abs/1802.03801 (2018) - [i7]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 abs/1810.04100 (2018) - [i6]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 abs/1810.04723 (2018) - [i5]Lam M. Nguyen, Katya Scheinberg, Martin Takác:
Inexact SARAH Algorithm for Stochastic Optimization. CoRR abs/1811.10105 (2018) - [i4]Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk:
New Convergence Aspects of Stochastic Gradient Algorithms. CoRR abs/1811.12403 (2018) - [i3]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 abs/1812.08329 (2018) - 2017
- [c2]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML 2017: 2613-2621 - [i2]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR abs/1703.00102 (2017) - [i1]Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác:
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR abs/1705.07261 (2017) - 2016
- [c1]Lam M. Nguyen, Alexander L. Stolyar:
A Service System with Randomly Behaving On-demand Agents. SIGMETRICS 2016: 365-366
Coauthor Index
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