2 citations · 7 across the 14 of their papers we have counts for
4 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…
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution
Lam M. Nguyen, Trang H. Tran, Marten van Dijk
Deep neural networks (DNNs) have shown great success in many machine learning tasks. Their training is challenging since the loss surface of the network architecture is generally n…
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…