18 citations · 23 across the 18 of their papers we have counts for
19 papers
Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
Lam M. Nguyen, Dzung T. Phan, Jayant Kalagnanam
Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for…
Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models
Yunshi Wen, Wesley M. Gifford, Chandra Reddy +3
The recent surge in Time Series Foundation Models has rapidly advanced the field, yet the heterogeneous training setups across studies make it difficult to attribute improvements t…
Graph Concept Bottleneck Models
Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen +1
Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to adjust final predictions. Existi…
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…
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Pei-Yau Weng, Minh Hoang, Lam M. Nguyen +3
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffect…
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification
Yunshi Wen, Tengfei Ma, Tsui-Wei Weng +2
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation model…
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…
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Anthony Baez, Wang Zhang, Ziwen Ma +3
Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs…
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…
One step closer to unbiased aleatoric uncertainty estimation
Wang Zhang, Ziwen Ma, Subhro Das +4
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties…
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…
Correlated Attention in Transformers for Multivariate Time Series
Quang Minh Nguyen, Lam M. Nguyen, Subhro Das
Multivariate time series (MTS) analysis prevails in real-world applications such as finance, climate science and healthcare. The various self-attention mechanisms, the backbone of…
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen +1
Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. Howe…
Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent
Toan N. Nguyen, Phuong Ha Nguyen, Lam M. Nguyen +1
Each round in Differential Private Stochastic Gradient Descent (DPSGD) transmits a sum of clipped gradients obfuscated with Gaussian noise to a central server which uses this to up…
Label-Free Concept Bottleneck Models
Tuomas Oikarinen, Subhro Das, Lam M. Nguyen +1
Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts. However…
Generalizing DP-SGD with Shuffling and Batch Clipping
Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen +1
Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algo…
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…
Proactive DP: A Multple Target Optimization Framework for DP-SGD
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +2
We introduce a multiple target optimization framework for DP-SGD referred to as pro-active DP. In contrast to traditional DP accountants, which are used to track the expenditure of…