Lam M. Nguyen - RankMe

104 papers A* 29A 4B 1C 2Misc 4Journal 63Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Graph Concept Bottleneck Models. Trans. Mach. Learn. Res. Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen, Tengfei Ma
2026	J	`jnl`	Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models. CoRR Yunshi Wen, Wesley M. Gifford, Chandra Reddy, Lam M. Nguyen, Jayant Kalagnanam, Anak Agung Julius
2025	J	`jnl`	Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting. CoRR Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen
2025	Misc	`conf`	Correlated Attention in Transformers for Multivariate Time Series. ICASSP Quang Minh Nguyen, Lam M. Nguyen, Subhro Das
2025	Misc	`conf`	Dilated Convolution for Time Series Learning. ICASSP Wang Zhang, Subhro Das, Lam M. Nguyen, Luca Daniel
2025	Misc	`conf`	Foundation Model and Temporal Priors-guided Transductive Few-shot Action Recognition. ICASSP Bach Vu, Hoang Nguyen, Quang Minh Nguyen, Duong Le, Hieu Pham, Phi Le Nguyen, Lam M. Nguyen
2025	J	`jnl`	Graph Concept Bottleneck Models. CoRR Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen, Tengfei Ma
2025	J	`jnl`	Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks. CoRR Anthony Baez, Wang Zhang, Ziwen Ma, Lam M. Nguyen, Subhro Das, Luca Daniel
2025	J	`jnl`	Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. CoRR Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Tsui-Wei Weng, Trong Nghia Hoang
2025	J	`jnl`	Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization. J. Optim. Theory Appl. Lam M. Nguyen, Katya Scheinberg, Trang H. Tran
2024	A*	`conf`	Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. NeurIPS Yunshi Wen, Tengfei Ma, Lily Weng, Lam M. Nguyen, Anak Agung Julius
2024	J	`jnl`	Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. CoRR Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, Anak Agung Julius
2024	J	`jnl`	Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks. CoRR Anthony Baez, Wang Zhang, Ziwen Martin Ma, Subhro Das, Lam M. Nguyen, Luca Daniel
2024	A*	`conf`	Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder. ICDM Duy A. Nguyen, Trang H. Tran, Huy Hieu Pham, Phi Le Nguyen, Lam M. Nguyen
2024	A	`conf`	Multi-polytope Machine for Classification. SDM Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam, Chandra Reddy
2024	A*	`conf`	On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. AAAI Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh
2024	A*	`conf`	One Step Closer to Unbiased Aleatoric Uncertainty Estimation. AAAI Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Lily Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
2024	A*	`conf`	Proactive DP: A Multiple Target Optimization Framework for DP-SGD. ICML Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen
2024	A*	`conf`	Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. NeurIPS Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Lily Weng, Trong Nghia Hoang
2024	A*	`conf`	Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization. NeurIPS Quoc Tran-Dinh, Trang H. Tran, Lam M. Nguyen
2024	J	`jnl`	Shuffling Momentum Gradient Algorithm for Convex Optimization. CoRR Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen
2024	B	`conf`	TabularFM: An Open Framework For Tabular Foundational Models. IEEE Big Data Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung T. Phan, Hoang Thanh Lam
2024	J	`jnl`	TabularFM: An Open Framework For Tabular Foundational Models. CoRR Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung T. Phan, Hoang Thanh Lam
2023	J	`jnl`	A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. CoRR Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín
2023	J	`jnl`	An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung T. Phan, Roman Vaculín, Jayant Kalagnanam
2023	A*	`conf`	Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng
2023	A*	`conf`	Attacking c-MARL More Effectively: A Data Driven Approach. ICDM Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng
2023	J	`jnl`	Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent. CoRR Toan N. Nguyen, Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
2023	A*	`conf`	ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. ICML Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
2023	J	`jnl`	ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. CoRR Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
2023	J	`jnl`	Correlated Attention in Transformers for Multivariate Time Series. CoRR Quang Minh Nguyen, Lam M. Nguyen, Subhro Das
2023	J	`jnl`	Label-Free Concept Bottleneck Models. CoRR Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng
2023	A*	`conf`	Label-free Concept Bottleneck Models. ICLR Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng
2023	J	`jnl`	Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach. CoRR Anh Duy Nguyen, Trang H. Tran, Hieu H. Pham, Phi Le Nguyen, Lam M. Nguyen
2023	J	`jnl`	On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. CoRR Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh
2023	J	`jnl`	On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error. J. Mach. Learn. Res. Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen
2023	A*	`conf`	On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. NeurIPS Lam M. Nguyen, Trang H. Tran
2023	J	`jnl`	One step closer to unbiased aleatoric uncertainty estimation. CoRR Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
2023	C	`conf`	Optimal Control via Linearizable Deep Learning. ACC Vinícius Lima, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
2023	A*	`conf`	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
2023	J	`jnl`	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
2023	Misc	`conf`	Scalable and Secure Federated XGBoost. ICASSP Quang Minh Nguyen, Nhan Khanh Le, Lam M. Nguyen
2022	J	`jnl`	A hybrid stochastic optimization framework for composite nonconvex optimization. Math. Program. Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
2022	J	`jnl`	AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. INFORMS J. Appl. Anal. Jayant Kalagnanam, Dzung T. Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, Dharmashankar Subramanian, Raju Pavuluri, Xiang Ma, Crystal Lui, Giovane Cesar Da Silva
2022	J	`jnl`	Besting the Black-Box: Barrier Zones for Adversarial Example Defense. IEEE Access Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk
2022	J	`jnl`	Evaluating Robustness of Cooperative MARL: A Model-based Approach. CoRR Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng
2022	J	`jnl`	Finding Optimal Policy for Queueing Models: New Parameterization. CoRR Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
2022	J	`jnl`	Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution. CoRR Lam M. Nguyen, Trang H. Tran, Marten van Dijk
2022	J	`jnl`	Finite-sum smooth optimization with SARAH. Comput. Optim. Appl. Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam
2022	J	`jnl`	Generalizing DP-SGD with Shuffling and Batching Clipping. CoRR Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen, Lam M. Nguyen
2022	A*	`conf`	Interpretable Clustering via Multi-Polytope Machines. AAAI Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy
2022	A*	`conf`	Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. ICML Trang H. Tran, Katya Scheinberg, Lam M. Nguyen
2022	J	`jnl`	Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. CoRR Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
2022	J	`jnl`	On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport. CoRR Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou, Lam M. Nguyen
2022	J	`jnl`	On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms. CoRR Lam M. Nguyen, Trang H. Tran
2022	A	`conf`	StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. SDM Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen
2021	J	`jnl`	A Unified Convergence Analysis for Shuffling-Type Gradient Methods. J. Mach. Learn. Res. Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk
2021	J	`jnl`	Differential Private Hogwild! over Distributed Local Data Sets. CoRR Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen
2021	A*	`conf`	Ensembling Graph Predictions for AMR Parsing. NeurIPS Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo
2021	J	`jnl`	Ensembling Graph Predictions for AMR Parsing. CoRR Hoang Thanh Lam, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo
2021	A*	`conf`	FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. NeurIPS Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
2021	J	`jnl`	Federated Learning with Randomized Douglas-Rachford Splitting Methods. CoRR Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
2021	A	`conf`	Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. AISTATS Nhuong V. Nguyen, Toan N. Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam M. Nguyen, Marten van Dijk
2021	J	`jnl`	Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. Lam M. Nguyen, Katya Scheinberg, Martin Takác
2021	J	`jnl`	Interpretable Clustering via Multi-Polytope Machines. CoRR Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy
2021	A*	`conf`	On the Equivalence between Neural Network and Support Vector Machine. NeurIPS Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
2021	J	`jnl`	On the Equivalence between Neural Network and Support Vector Machine. CoRR Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
2021	C	`conf`	Regression Optimization for System-level Production Control. ACC Dzung T. Phan, Lam M. Nguyen, Pavankumar Murali, Nhan H. Pham, Hongsheng Liu, Jayant R. Kalagnanam
2021	A*	`conf`	SMG: A Shuffling Gradient-Based Method with Momentum. ICML Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
2020	A	`conf`	A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. AISTATS Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh
2020	J	`jnl`	A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. CoRR Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh
2020	A*	`conf`	A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. NeurIPS Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
2020	J	`jnl`	A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. CoRR Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam
2020	J	`jnl`	A Unified Convergence Analysis for Shuffling-Type Gradient Methods. CoRR Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk
2020	J	`jnl`	Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise. CoRR Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Quoc Tran-Dinh, Phuong Ha Nguyen
2020	J	`jnl`	Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness. CoRR Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham, Justin Romberg
2020	J	`jnl`	Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. CoRR Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Quoc Tran-Dinh, Phuong Ha Nguyen
2020	A*	`conf`	Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. NeurIPS Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen
2020	J	`jnl`	ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. J. Mach. Learn. Res. Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
2020	A*	`conf`	Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization. ICDM Dzung T. Phan, Lam M. Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam
2020	J	`jnl`	Shuffling Gradient-Based Methods with Momentum. CoRR Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
2020	A*	`conf`	Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. ICML Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen
2019	J	`jnl`	A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. CoRR Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen
2019	J	`jnl`	BUZz: BUffer Zones for defending adversarial examples in image classification. CoRR Phuong Ha Nguyen, Kaleel Mahmood, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk
2019	A*	`conf`	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. ICML Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan
2019	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2019	J	`jnl`	Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. CoRR Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam
2019	A*	`conf`	PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. ICML Lily Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Akhilan Boopathy, Ivan V. Oseledets, Luca Daniel
2019	J	`jnl`	ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. CoRR Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
2019	A*	`conf`	Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD. NeurIPS Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
2018	J	`jnl`	A queueing system with on-demand servers: local stability of fluid limits. Queueing Syst. Theory Appl. Lam M. Nguyen, Alexander L. Stolyar
2018	J	`jnl`	Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. CoRR Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan
2018	—	`conf`	ChieF: A Change Pattern based Interpretable Failure Analyzer. IEEE BigData Dhaval Patel, Lam M. Nguyen, Akshay Rangamani, Shrey Shrivastava, Jayant Kalagnanam
2018	J	`jnl`	Inexact SARAH Algorithm for Stochastic Optimization. CoRR Lam M. Nguyen, Katya Scheinberg, Martin Takác
2018	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2018	J	`jnl`	PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. CoRR Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan V. Oseledets, Luca Daniel
2018	A*	`conf`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. ICML Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2018	J	`jnl`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2018	J	`jnl`	Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD. CoRR Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk
2018	J	`jnl`	When Does Stochastic Gradient Algorithm Work Well? CoRR Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg
2017	A*	`conf`	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2017	J	`jnl`	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2017	J	`jnl`	Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2016	A*	`conf`	A Service System with Randomly Behaving On-demand Agents. SIGMETRICS Lam M. Nguyen, Alexander L. Stolyar