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Duong Nguyen

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5 papers

2 author rows

ECAI Conference 2025 Conference Paper

Domain Generalization via Pareto Optimal Gradient Matching

Khoi Do
Nam-Khanh Le
Quoc-Viet Pham
Binh-Son Hua
Won-Joo Hwang
Duong Nguyen

In this study, we address the gradient-based domain generalization problem, where predictors aim for consistent gradient directions across different domains. Existing methods have two main challenges. First, minimization of gradient empirical distance or gradient inner products (GIP) leads to gradient fluctuations among domains, thereby hindering straightforward learning. Second, the direct application of gradient learning to the joint loss function can incur high computation overheads due to second-order derivative approximation. To tackle these challenges, we propose a new Pareto Optimality Gradient Matching (POGM) method. In contrast to existing methods that add gradient matching as regularization, we leverage gradient trajectories as collected data and apply independent training at the meta-learner. In the meta-update, we maximize GIP while limiting the learned gradient from deviating too far from the empirical risk minimization gradient trajectory. By doing so, the aggregate gradient can incorporate knowledge from all domains without suffering gradient fluctuation towards any particular domain. Experimental evaluations on datasets from DomainBed demonstrate competitive results yielded by POGM against other baselines while achieving computational efficiency. The code is available at https: //github. com/skydvn/POGM.

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NeurIPS Conference 2025 Conference Paper

Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data

Duong Nguyen
Nghia Hoang
Thanh Trung Huynh
Quoc Viet Hung Nguyen
Phi Le Nguyen

Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations that limit the effectiveness of model aggregation. Unlike prior work that assumes either differing modality sets without missing input features or a shared modality set with missing features across clients, we consider a more general and realistic setting where each client observes a different subset of modalities and might also have missing input features within each modality. To address the resulting misalignment in learned representations, we propose a new federated learning framework featuring locally adaptive representations based on learnable client-side embedding controls that encode each client’s data-missing patterns. These embeddings serve as reconfiguration signals that align the globally aggregated representation with each client's local context, enabling more effective use of shared information. Furthermore, the embedding controls can be algorithmically aggregated across clients with similar data-missing patterns to enhance the robustness of reconfiguration signals in adapting the global representation. Empirical results on multiple federated multimodal benchmarks with diverse data-missing patterns across clients demonstrate the efficacy of the proposed method, achieving up to 36. 45\% performance improvement under severe data incompleteness. The method is also supported by a theoretical analysis with an explicit performance bound that matches our empirical observations. Our source codes are provided at https: //github. com/nmduonggg/PEPSY

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ECAI Conference 2024 Conference Paper

United We Stand: Decentralized Multi-Agent Planning with Attrition

Nhat Nguyen
Duong Nguyen
Gianluca Rizzo
Hung Nguyen 0004

Decentralized planning is a key element of cooperative multi-agent systems for information gathering tasks. However, despite the high frequency of agent failures in realistic large deployment scenarios, current approaches perform poorly in the presence of failures, by not converging at all, and/or by making very inefficient use of resources (e. g. energy). In this work, we propose Attritable MCTS (A-MCTS), a decentralized MCTS algorithm capable of timely and efficient adaptation to changes in the set of active agents. It is based on the use of a global reward function for the estimation of each agent’s local contribution, and regret matching for coordination. We evaluate its effectiveness in realistic data-harvesting problems under different scenarios. We show both theoretically and experimentally that A-MCTS enables efficient adaptation even under high failure rates. Results suggest that, in the presence of frequent failures, our solution improves substantially over the best existing approaches in terms of global utility and scalability.

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AAMAS Conference 2021 Conference Paper

HOAD: The Hanabi Open Agent Dataset

Aron Sarmasi
Timothy Zhang
Chu-Hung Cheng
Huyen PHAM
Xuanchen Zhou
Duong Nguyen
Soumil Shekdar
Joshua McCoy

In this work we present the Hanabi Open Agent Dataset (HOAD)— meant to address the current lack of Hanabi datasets, HOAD is an easily extensible, open-sourced, and comprehensive collection of existing Hanabi playing agents, all ported to the Hanabi Learning Environment (HLE). We give a description and analysis of each agent’s strategy, and we also show cross-play performance between all the agents, demonstrating both their high quality and diversity of strategy. These properties make HOAD especially well suited to studies involving meta-learning and transfer learning. Finally, we describe in detail an easy way to add new agents to HOAD regardless of the origin codebase of the agent and make our code and dataset publicly available at https: //github. com/aronsar/hoad.

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NeurIPS Conference 2021 Conference Paper

Structured Dropout Variational Inference for Bayesian Neural Networks

Son Nguyen
Duong Nguyen
Khai Nguyen
Khoat Than
Hung Bui
Nhat Ho

Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability. We tackle this challenge by introducing a novel variational structured approximation inspired by the Bayesian interpretation of Dropout regularization. Concretely, we focus on the inflexibility of the factorized structure in Dropout posterior and then propose an improved method called Variational Structured Dropout (VSD). VSD employs an orthogonal transformation to learn a structured representation on the variational Gaussian noise with plausible complexity, and consequently induces statistical dependencies in the approximate posterior. Theoretically, VSD successfully addresses the pathologies of previous Variational Dropout methods and thus offers a standard Bayesian justification. We further show that VSD induces an adaptive regularization term with several desirable properties which contribute to better generalization. Finally, we conduct extensive experiments on standard benchmarks to demonstrate the effectiveness of VSD over state-of-the-art variational methods on predictive accuracy, uncertainty estimation, and out-of-distribution detection.

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