18 citations · 28 across the 44 of their papers we have counts for
46 papers
DREAM: Dynamic Resolution Assignment For Multimodal Multi-agent Debate
Khanh-Binh Nguyen, Van Dai Do, Tien Anh Nguyen +2
Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingly being extended to multimodal…
A Statistical Approach to Estimating Sample Size of Machine Learning Models
Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh
Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and e…
Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?
Tien Anh Nguyen, Khanh-Binh Nguyen, Van Dai Do +2
Creative generation tasks, such as narrative writing and scientific ideation, demand both high-quality outputs and distinct responses across independent runs to maximize exploratio…
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…
SMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University Students
Dang Nguyen, Arun Kumar A, Taylor A. Braund +8
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well…
Continual Fine-Tuning of Large Language Models via Program Memory
Hung Le, Svetha Venkatesh
Parameter-Efficient Fine-Tuning (PEFT), particularly Low-Rank Adaptation (LoRA), has become a standard approach for adapting Large Language Models (LLMs) under limited compute. How…
Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models
Giang Ngo, Dat Phan Trong, Dang Nguyen +2
Bayesian Optimization critically depends on the choice of acquisition function, but no single strategy is universally optimal; the best choice is non-stationary and problem-depende…
Federated Domain Generalization with Latent Space Inversion
Ragja Palakkadavath, Hung Le, Thanh Nguyen-Tang +2
Federated domain generalization (FedDG) addresses distribution shifts among clients in a federated learning framework. FedDG methods aggregate the parameters of locally trained cli…
Uncertainty-Guided Checkpoint Selection for Reinforcement Finetuning of Large Language Models
Manh Nguyen, Dung Nguyen, Dai Do +2
Reinforcement learning (RL) finetuning is crucial to aligning large language models (LLMs), but the process is notoriously unstable and exhibits high variance across model checkpoi…
Large Language Models for Imbalanced Classification: Diversity makes the difference
Dang Nguyen, Sunil Gupta, Kien Do +4
Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minority samples to rebalance the dataset.…
SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning
Dai Do, Manh Nguyen, Svetha Venkatesh +1
Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods require extensive data and compute, ma…
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
Hung Le, Dai Do, Dung Nguyen +1
Recent advances in fine-tuning large language models (LLMs) with reinforcement learning (RL) have shown promising improvements in complex reasoning tasks, particularly when paired…
Predicting the Reliability of an Image Classifier under Image Distortion
Dang Nguyen, Sunil Gupta, Kien Do +1
In image classification tasks, deep learning models are vulnerable to image distortions i.e. their accuracy significantly drops if the input images are distorted. An image-classifi…
Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks
Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran +3
Discovering new solid-state materials requires rapidly exploring the vast space of crystal structures and locating stable regions. Generating stable materials with desired properti…
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…
Large Language Models Prompting With Episodic Memory
Dai Do, Quan Tran, Svetha Venkatesh +1
Prompt optimization is essential for enhancing the performance of Large Language Models (LLMs) in a range of Natural Language Processing (NLP) tasks, particularly in scenarios of f…
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…
Composite Concept Extraction through Backdooring
Banibrata Ghosh, Haripriya Harikumar, Khoa D Doan +2
Learning composite concepts, such as \textquotedbl red car\textquotedbl , from individual examples -- like a white car representing the concept of \textquotedbl car\textquotedbl{}…
Multi-Reference Preference Optimization for Large Language Models
Hung Le, Quan Tran, Dung Nguyen +4
How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingl…
Novel Kernel Models and Exact Representor Theory for Neural Networks Beyond the Over-Parameterized Regime
Alistair Shilton, Sunil Gupta, Santu Rana +1
This paper presents two models of neural-networks and their training applicable to neural networks of arbitrary width, depth and topology, assuming only finite-energy neural activa…
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Arun Kumar A, Alistair Shilton, Sunil Gupta +3
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive…
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…
Embedding material graphs using the electron-ion potential: application to material fracture
Sherif Abdulkader Tawfik, Tri Minh Nguyen, Salvy P. Russo +3
At the heart of the flourishing field of machine learning potentials are graph neural networks, where deep learning is interwoven with physics-informed machine learning (PIML) arch…