11 citations · 18 across the 27 of their papers we have counts for
15 papers · 1 filter
Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards
Hieu Trung Nguyen, Bao Nguyen, Wenao Ma +3
Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number o…
Exploring Diverse Generation Paths via Inference-time Stiefel Activation Steering
Dongxuan Zhu, Ly Tran Ho Khanh, Andy Yat-Ming Cheung +2
Language models often default to a narrow set of high-probability outputs, leaving their generation paths homogeneous and prone to mode collapse. Sampling-based strategies inject r…
Test-time Diverse Reasoning by Riemannian Activation Steering
Ly Tran Ho Khanh, Dongxuan Zhu, Man-Chung Yue +1
Best-of- reasoning improves the accuracy of language models in solving complex tasks by sampling multiple candidate solutions and then selecting the best one based on some crite…
Reasoning Planning for Language Models
Bao Nguyen, Hieu Trung Nguyen, Ruifeng She +2
Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate response…
Mixture-of-Personas Language Models for Population Simulation
Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar +4
Advances in Large Language Models (LLMs) paved the way for their emerging applications in various domains, such as human behavior simulations, where LLMs could augment human-genera…
Probe-Free Low-Rank Activation Intervention
Chonghe Jiang, Bao Nguyen, Anthony Man-Cho So +1
Language models (LMs) can produce texts that appear accurate and coherent but contain untruthful or toxic content. Inference-time interventions that edit the hidden activations hav…
Distributional Surgery for Language Model Activations
Bao Nguyen, Binh Nguyen, Duy Nguyen +1
Language models, while capable of generating remarkably coherent and seemingly accurate text, can occasionally produce undesirable content, including harmful or toxic outputs. In t…
Generative Conditional Distributions by Neural (Entropic) Optimal Transport
Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen +1
Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the c…
Retrospective Feature Estimation for Continual Learning
Nghia D. Nguyen, Hieu Trung Nguyen, Ang Li +3
The intrinsic capability to continuously learn a changing data stream is a desideratum of deep neural networks (DNNs). However, current DNNs suffer from catastrophic forgetting, wh…
Explaining Graph Neural Networks via Structure-aware Interaction Index
Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen +1
The Shapley value is a prominent tool for interpreting black-box machine learning models thanks to its strong theoretical foundation. However, for models with structured inputs, su…
Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
Duy Nguyen, Bao Nguyen, Viet Anh Nguyen
Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature gen…
Coverage-Validity-Aware Algorithmic Recourse
Ngoc Bui, Duy Nguyen, Man-Chung Yue +1
Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency, and ethics of machine learning models. Existing algorithmic recourse approaches o…
Feasible Recourse Plan via Diverse Interpolation
Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
Explaining algorithmic decisions and recommending actionable feedback is increasingly important for machine learning applications. Recently, significant efforts have been invested…
Distributionally Robust Recourse Action
Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
A recourse action aims to explain a particular algorithmic decision by showing one specific way in which the instance could be modified to receive an alternate outcome. Existing re…
A Deep Reinforcement Learning-based Adaptive Charging Policy for WRSNs
Ngoc Bui, Phi Le Nguyen, Viet Anh Nguyen +1
Wireless sensor networks consist of randomly distributed sensor nodes for monitoring targets or areas of interest. Maintaining the network for continuous surveillance is a challeng…