99 citations · 510 across the 39 of their papers we have counts for
42 papers
Breaking the Shot-Noise Barrier: Cached Recycled Variance-Reduced Gradients for Quantum Optimization
Lam M. Nguyen, Sumanta Mukherjee, Dzung T. Phan
Variational Quantum Algorithms (VQAs) rely heavily on classical optimization routines to navigate high-dimensional, noisy parameter landscapes. However, the evaluation of analytica…
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder
Duy A. Nguyen, Trang H. Tran, Huy Hieu Pham +2
In this work, we investigate the time series representation learning problem using self-supervised techniques. Contrastive learning is well-known in this area as it is a powerful m…
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Wang Zhang, Tsui-Wei Weng, Subhro Das +3
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws. This pape…
Finding Optimal Policy for Queueing Models: New Parameterization
Trang H. Tran, Lam M. Nguyen, Katya Scheinberg
Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework i…
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Lam M. Nguyen, Trang H. Tran
Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In…
StepDIRECT -- A Derivative-Free Optimization Method for Stepwise Functions
Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen
In this paper, we propose the StepDIRECT algorithm for derivative-free optimization (DFO), in which the black-box objective function has a stepwise landscape. Our framework is base…
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
Quang Minh Nguyen, Hoang H. Nguyen, Yi Zhou +1
We study the Unbalanced Optimal Transport (UOT) between two measures of possibly different masses with at most components, where the marginal constraints of standard Optimal Tr…
Attacking c-MARL More Effectively: A Data Driven Approach
Nhan H. Pham, Lam M. Nguyen, Jie Chen +3
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial…
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
Trang H. Tran, Katya Scheinberg, Lam M. Nguyen
In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG), a new algorithm for the convex finite-sum minimization problems. Our method integrates the traditional Nes…
Interpretable Clustering via Multi-Polytope Machines
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen +2
Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use…
On the Equivalence between Neural Network and Support Vector Machine
Yilan Chen, Wei Huang, Lam M. Nguyen +1
Recent research shows that the dynamics of an infinitely wide neural network (NN) trained by gradient descent can be characterized by Neural Tangent Kernel (NTK) \citep{jacot2018ne…
Ensembling Graph Predictions for AMR Parsing
Hoang Thanh Lam, Gabriele Picco, Yufang Hou +5
In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into depe…
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan +1
We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a nove…
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Haoran Zhu, Pavankumar Murali, Dzung T. Phan +2
Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a gr…
SMG: A Shuffling Gradient-Based Method with Momentum
Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with mo…
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations an…
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
Deyi Liu, Lam M. Nguyen, Quoc Tran-Dinh
In this note we propose a new variant of the hybrid variance-reduced proximal gradient method in [7] to solve a common stochastic composite nonconvex optimization problem under sta…
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 +3
The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices…
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen
We develop a novel and single-loop variance-reduced algorithm to solve a class of stochastic nonconvex-convex minimax problems involving a nonconvex-linear objective function, whic…
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham +1
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underly…
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan +3
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARA…
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan +2
In this paper, we propose a unified convergence analysis for a class of generic shuffling-type gradient methods for solving finite-sum optimization problems. Our analysis works wit…
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen
We develop two new stochastic Gauss-Newton algorithms for solving a class of non-convex stochastic compositional optimization problems frequently arising in practice. We consider b…