13 papers · 1 filter
Volatility-Clustering Adaptation for Financial Time Series
Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen +2
Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more target data yields better forecast…
Memory in Deep Time-Series Models
Minh Hoang Nguyen, Huu Hiep Nguyen, Manh Nguyen +3
Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state-space models, retrieval-augmen…
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen +2
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints th…
ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models
Kejing Wang, Toan Nguyen, Minh Hoang Nguyen +2
Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional autoregressive action polici…
Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts
Tien-Hung Nguyen, Tien-Dat Tran, M. -Duong Nguyen +1
Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training. A common…
Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Multimodal time series forecasting, which pairs numerical sequences with domain-relevant textual reports, promises to inject world knowledge into forecasting pipelines. However, we…
Spectral Retrieval-Augmented Time-Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are…
Reviving Error Correction in Modern Deep Time-Series Forecasting
Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3
Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autore…
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1
Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively…
Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting
Hung Le, Sherif Abbas, Minh Hoang Nguyen +3
Efficient molecular dynamics (MD) simulation is vital for understanding atomic-scale processes in materials science and biophysics. Traditional density functional theory (DFT) meth…
DmC: Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Duc Kieu +3
Cross-domain offline reinforcement learning (RL) seeks to enhance sample efficiency in offline RL by utilizing additional offline source datasets. A key challenge is to identify an…
Hybrid Cross-domain Robust Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Hung Le +2
Robust reinforcement learning (RL) aims to learn policies that remain effective despite uncertainties in its environment, which frequently arise in real-world applications due to v…
Variable-Agnostic Causal Exploration for Reinforcement Learning
Minh Hoang Nguyen, Hung Le, Svetha Venkatesh
Modern reinforcement learning (RL) struggles to capture real-world cause-and-effect dynamics, leading to inefficient exploration due to extensive trial-and-error actions. While rec…