1 citations · 3 across the 17 of their papers we have counts for
19 papers
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Duong M. Nguyen, Trong Nghia Hoang, Hang Thi Nguyen +3
Histological whole-slide images (WSIs) are central to computational pathology but pose severe computational challenges due to their extremely high resolution, often spanning severa…
Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space
Long Minh Bui, Tuan Anh Le Van, Tung Phi Duc +3
Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing approaches achieve this using geom…
Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
Trong Khiem Tran, Anh Duc Chu, Quang Hung Pham +2
Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another typ…
Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
Azza Fadhel, The Hung Tran, Trong Nghia Hoang +1
We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge…
Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
Hang Thi-Thuy Le, Long Minh Bui, Minh Hoang +1
Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data are no longer available. Existing appr…
Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
Thu Hang Phung, Duong M. Nguyen, Thanh Trung Huynh +3
This paper introduces a generalized federated prompt-tuning framework for practical scenarios where local datasets are multi-modal and exhibit different distributional patterns of…
Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
Nang Hung Nguyen, Phi Le Nguyen, Thao Nguyen Truong +2
This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes…
Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration
Manh Cuong Dao, Quang Hung Pham, Phi Le Nguyen +3
Uncertainty calibration in pre-trained transformers is critical for their reliable deployment in risk-sensitive applications. Yet, most existing pre-trained transformers do not hav…
Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting
Trong Khiem Tran, Manh Cuong Dao, Phi Le Nguyen +2
Adapting pre-trained models to unseen feature modalities has become increasingly important due to the growing need for cross-disciplinary knowledge integration. A key challenge her…
ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model
Krishu K Thapa, Supriya Savalkar, Bhupinderjeet Singh +3
Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE) -- a key measure widely used to estimate the water…
Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh +2
Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations tha…
ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge
Manh Cuong Dao, The Hung Tran, Phi Le Nguyen +2
This paper studies the black-box optimization task which aims to find the maxima of a black-box function using a static set of its observed input-output pairs. This is often achiev…
Expressive and Scalable Quantum Fusion for Multimodal Learning
Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen +2
The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Q…
Boosting Offline Optimizers with Surrogate Sensitivity
Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong +1
Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in s…
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…
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
Minh Hoang, Azza Fadhel, Aryan Deshwal +2
Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessita…
FedCert: Federated Accuracy Certification
Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen +4
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models in a decentralized manner, preserving data privacy by keeping local data on clients.…
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham +3
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared gl…
Offline Model-Based Optimization via Policy-Guided Gradient Search
Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang +1
Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation…