2 citations · 7 across the 14 of their papers we have counts for
15 papers
Non-monotone direct-search methods for deterministic and stochastic derivative-free optimization
Anjie Ding, Trang H. Tran, Luis Nunes Vicente
In derivative-free optimization (DFO), one minimizes functions for which the gradient is unavailable or expensive to compute. In many applications, objective function values and gr…
Stochastic block coordinate and function alternation for multi-objective optimization and learning
Trang H. Tran, Luis Nunes Vicente
Multi-objective optimization is central to many engineering and machine learning applications, where multiple objectives must be optimized in balance. While multi-gradient based op…
Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen
In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework using a dynamic weighting mechanism to…
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
Quoc Tran-Dinh, Trang H. Tran, Lam M. Nguyen
This paper aims at developing novel shuffling gradient-based methods for tackling two classes of minimax problems: nonconvex-linear and nonconvex-strongly concave settings. The fir…
Shuffling Momentum Gradient Algorithm for Convex Optimization
Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen
The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning an…
Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
Lam M. Nguyen, Katya Scheinberg, Trang H. Tran
We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without th…
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +2
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing. While researchers had attempted to extend t…
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…
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…
Box Embeddings: An open-source library for representation learning using geometric structures
Tejas Chheda, Purujit Goyal, Trang Tran +4
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (eg. d…
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…