66 citations · 196 across the 21 of their papers we have counts for
6 papers · 1 filter
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…