A. Tuan Nguyen

Selected Publications

1. NeurIPS

   FedSR: A Simple and Effective Domain Generalization Method for Federated Learning

   Nguyen, A. Tuan, Torr, Philip, and Lim, Ser-Nam

   Advances in Neural Information Processing Systems 36 (NeurIPS) 2022

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   Federated Learning (FL) refers to the decentralized and privacy-preserving machine learning framework in which multiple clients collaborate (with the help of a central server) to train a global model without sharing their data. However, most existing FL methods only focus on maximizing the model’s performance on the source clients’ data (e.g., mobile users) without considering its generalization ability to unknown target data (e.g., a new user). In this paper, we incorporate the problem of Domain Generalization (DG) into Federated Learning to tackle the aforementioned issue. However, virtually all existing DG methods require a centralized setting where data is shared across the domains, which violates the principles of decentralized FL and hence not applicable. To this end, we propose a simple yet novel representation learning framework, namely FedSR, which enables domain generalization while still respecting the decentralized and privacy-preserving natures of this FL setting. Motivated by classical machine learning algorithms, we aim to learn a simple representation of the data for better generalization. In particular, we enforce an L2-norm regularizer on the representation and a conditional mutual information (between the representation and the data given the label) regularizer to encourage the model to only learn essential information (while ignoring spurious correlations such as the background). Furthermore, we provide theoretical connections between the above two objectives and representation alignment in domain generalization. Extensive experimental results suggest that our method significantly outperforms relevant baselines in this particular problem.

   @article{nguyen2022fedsr,
     abbr = {NeurIPS},
     pdf = {NeurIPS_nguyen2022fedsr.pdf},
     selected = {true},
     html = {https://openreview.net/forum?id=mrt90D00aQX},
     bibtex_show = {true},
     title = {FedSR: A Simple and Effective Domain Generalization Method for Federated Learning},
     author = {Nguyen, A. Tuan and Torr, Philip and Lim, Ser-Nam},
     journal = {Advances in Neural Information Processing Systems 36 (NeurIPS)},
     year = {2022}
   }

2. ICLR

   KL Guided Domain Adaptation

   Nguyen, A. Tuan, Tran, Toan, Gal, Yarin, Torr, Philip HS, and Baydin, Atılım Güneş

   International Conference on Learning Representations (ICLR) 2022

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   Domain adaptation is an important problem and often needed for real-world ap- plications. In this problem, instead of i.i.d. datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training procedure often does not perform well, since it does not account for the change in the distribution. A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain. However, these approaches often require additional networks and/or optimizing an adversarial (minimax) objective, which can be very expensive or unstable in practice. To tackle this problem, we first derive a generalization bound for the target loss based on the training loss and the reverse Kullback–Leibler (KL) divergence between the source and the target representation distributions. Based on this bound, we derive an algorithm that minimizes the KL term to obtain a better generalization to the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples without any additional network or a minimax objective. This leads to a theoretically sound alignment method which is also very efficient and stable in practice. Experimental results also suggest that our method outperforms other representation-alignment approaches.

   @article{nguyen2022kl,
     abbr = {ICLR},
     bibtex_show = {true},
     selected = {true},
     pdf = {ICLR_nguyen2022kl.pdf},
     html = {https://openreview.net/forum?id=0JzqUlIVVDd},
     title = {KL Guided Domain Adaptation},
     author = {Nguyen, A. Tuan and Tran, Toan and Gal, Yarin and Torr, Philip HS and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}}},
     journal = {International Conference on Learning Representations (ICLR)},
     year = {2022}
   }

3. NeurIPS

   Domain Invariant Representation Learning with Domain Density Transformations

   Nguyen, A. Tuan, Tran, Toan, Gal, Yarin, and Baydin, Atılım Güneş

   Advances in Neural Information Processing Systems 35 (NeurIPS) 2021

   Abs Bib HTML PDF

   Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to target domains. To tackle this problem, a predominant domain generalization approach is to learn some domain-invariant information for the prediction task, aiming at a good generalization across domains. In this paper, we propose a theoretically grounded method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains. We next introduce the use of generative adversarial networks to learn such domain transformations in a possible implementation of our method in practice. We demonstrate the effectiveness of our method on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.

   @article{nguyen2021domain,
     abbr = {NeurIPS},
     bibtex_show = {true},
     selected = {true},
     pdf = {NeurIPS_nguyen2021domain.pdf},
     html = {https://papers.nips.cc/paper/2021/hash/2a2717956118b4d223ceca17ce3865e2-Abstract.html},
     title = {Domain Invariant Representation Learning with Domain Density Transformations},
     author = {Nguyen, A. Tuan and Tran, Toan and Gal, Yarin and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}}},
     journal = {Advances in Neural Information Processing Systems 35 (NeurIPS)},
     year = {2021}
   }

4. AAAI

   Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

   Nguyen, A. Tuan, Jeong, Hyewon, Yang, Eunho, and Hwang, Sung Ju

   Proceedings of the AAAI Conference on Artificial Intelligence May 2021

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   Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since low loss could result from overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model.

   @article{nguyen2021clinical,
     abbr = {AAAI},
     bibtex_show = {true},
     selected = {true},
     pdf = {AAAI_nguyen2021clinical.pdf},
     html = {https://ojs.aaai.org/index.php/AAAI/article/view/17097},
     title = {Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning},
     volume = {35},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/17097},
     number = {10},
     journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
     author = {Nguyen, A. Tuan and Jeong, Hyewon and Yang, Eunho and Hwang, Sung Ju},
     year = {2021},
     month = may,
     pages = {9081-9091}
   }