5 papers
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…
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…
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…
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…
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…