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.

ICLR

Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

Nguyen-Tang, Thanh, Gupta, Sunil,
Nguyen, A. Tuan, and Venkatesh, Svetha

International Conference on Learning Representations (ICLR) 2022

Offline policy learning (OPL) leverages existing data collected a priori for policy optimization without any active exploration. Despite the prevalence and recent interest in this problem, its theoretical and algorithmic foundations in function approximation settings remain under-developed. In this paper, we consider this problem on the axes of distributional shift, optimization, and generalization in offline contextual bandits with neural networks. In particular, we propose a provably efficient offline contextual bandit with neural network function approximation that does not require any functional assumption on the reward. We show that our method provably generalizes over unseen contexts under a milder condition for distributional shift than the existing OPL works. Notably, unlike any other OPL method, our method learns from the offline data in an online manner using stochastic gradient descent, allowing us to leverage the benefits of online learning into an offline setting. Moreover, we show that our method is more computationally efficient and has a better dependence on the effective dimension of the neural network than an online counterpart. Finally, we demonstrate the empirical effectiveness of our method in a range of synthetic and real-world OPL problems.

2021

NeurIPS

Domain Invariant Representation Learning with Domain Density Transformations

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.

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

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.

IEEE TMC

Detection of Microsleep Events with a Behind-the-ear Wearable System

Pham, Nhat, Dinh, Tuan, Kim, Taeho, Raghebi, Zohreh, Bui, Nam, Truong, Hoang,
Nguyen, A. Tuan, Banaei-Kashani, Farnoush, Halbower, Ann, Dinh, Thang N., Nguyen, Vp, and Vu, Tam