18 citations · 73 across the 28 of their papers we have counts for
17 papers · 1 filter
Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method
Dang Nguyen, Bao Duong, Arun Kumar +12
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well…
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…
Learning Structural Causal Models from Ordering: Identifiable Flow Models
Minh Khoa Le, Kien Do, Truyen Tran
In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can…
Generating Realistic Tabular Data with Large Language Models
Dang Nguyen, Sunil Gupta, Kien Do +2
While most generative models show achievements in image data generation, few are developed for tabular data generation. Recently, due to success of large language models (LLM) in d…
Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning
Hung Le, Kien Do, Dung Nguyen +2
Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models s…
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory
Hung Le, Dung Nguyen, Kien Do +2
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data. PANM integrates an external neura…
Revisiting the Dataset Bias Problem from a Statistical Perspective
Kien Do, Dung Nguyen, Hung Le +6
In this paper, we study the "dataset bias" problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u a…
Beyond Surprise: Improving Exploration Through Surprise Novelty
Hung Le, Kien Do, Dung Nguyen +1
We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations. The reward is the novelty…
Learning Theory of Mind via Dynamic Traits Attribution
Dung Nguyen, Phuoc Nguyen, Hung Le +3
Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the m…
Learning to Constrain Policy Optimization with Virtual Trust Region
Hung Le, Thommen Karimpanal George, Majid Abdolshah +4
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update. In addition to using th…
Episodic Policy Gradient Training
Hung Le, Majid Abdolshah, Thommen K. George +3
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.…
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
Kien Do, Truyen Tran, Svetha Venkatesh
We propose two generic methods for improving semi-supervised learning (SSL). The first integrates weight perturbation (WP) into existing "consistency regularization" (CR) based met…
Unsupervised Anomaly Detection on Temporal Multiway Data
Duc Nguyen, Phuoc Nguyen, Kien Do +3
Temporal anomaly detection looks for irregularities over space-time. Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on…
Theory and Evaluation Metrics for Learning Disentangled Representations
Kien Do, Truyen Tran
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluat…
Attentional Multilabel Learning over Graphs: A Message Passing Approach
Kien Do, Truyen Tran, Thin Nguyen +1
We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the l…
Learning Deep Matrix Representations
Kien Do, Truyen Tran, Svetha Venkatesh
We present a new distributed representation in deep neural nets wherein the information is represented in native form as a matrix. This differs from current neural architectures th…
Multilevel Anomaly Detection for Mixed Data
Kien Do, Truyen Tran, Svetha Venkatesh
Anomalies are those deviating from the norm. Unsupervised anomaly detection often translates to identifying low density regions. Major problems arise when data is high-dimensional…