Truyen Tran | Deakin University (original) (raw)

Papers by Truyen Tran

2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2015

2015 24th Australasian Software Engineering Conference, 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, 2015

We propose a new data mining approach in ranking documents based on the concept of cone-based gen... more We propose a new data mining approach in ranking documents based on the concept of cone-based generalized inequalities between vectors. A partial ordering between two vectors is made with respect to a proper cone and thus learning the preferences is formulated as learning proper cones. A pairwise learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative subspace, formulated as a polyhedral cone, over document-pair differences. The algorithm is regularized by controlling the 'volume' of the cone. The experimental studies on the latest and largest ranking dataset LETOR 4.0 shows that ConeRank is competitive against other recent ranking approaches.

Stability in clinical prediction models is crucial for transferability between studies, yet has r... more Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in highdimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using clinical structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using a feature graph derived from two types of EMR structures: temporal structure of disease and intervention recurrences, and hierarchical structure of medical knowledge and practices. We demonstrate the efficacy of the method in predicting time-to-readmission of heart failure patients. On two stability measures -the Jaccard index and the Consistency index -the use of clinical structures significantly increased feature stability without hurting discriminative power. Our model reported a competitive AUC of 0.64 (95% CIs: [0.58,0.69]) for 6 months prediction.

Fusion 2012 Proceedings of the 15th International Conference on Information Fusion, 2012

Analysis and fusion of social measurements is important to understand what shapes the public's op... more Analysis and fusion of social measurements is important to understand what shapes the public's opinion and the sustainability of the global development. However, modeling data collected from social responses is challenging as the data is typically complex and heterogeneous, which might take the form of stated facts, subjective assessment, choices, preferences or any combination thereof. Model-wise, these responses are a mixture of data types including binary, categorical, multicategorical, continuous, ordinal, count and rank data. The challenge is therefore to effectively handle mixed data in the a unified fusion framework in order to perform inference and analysis. To that end, this paper introduces eRBM (Embedded Restricted Boltzmann Machine) -a probabilistic latent variable model that can represent mixed data using a layer of hidden variables transparent across different types of data. The proposed model can comfortably support large-scale data analysis tasks, including distribution modelling, data completion, prediction and visualisation. We demonstrate these versatile features on several moderate and large-scale publicly available social survey datasets.

Inference and learning for general structure MRFs are usually intractable problems in computer vi... more Inference and learning for general structure MRFs are usually intractable problems in computer vision. In this paper, we exploit a set of tree-based methods to efficiently address this problem and evaluate these methods against some current state-of-the-art approaches in three problems: scene segmentation, stereo matching and image denoising. Our method takes advantage of the tractability of treestructures embedded in MRFs to derive a tractable lower bound of the true likelihood, propose the use of tree-based pseudo-likelihood (PL) for parameter estimation, and the use of tree-based ICM (T-ICM) for MAP assignment. Unlike loopy belief propagation, our method is guaranteed to converge and it does so with limited memory required to store the messages. Further, unlike Graph-Cuts, our T-ICM can be applied with arbitrary cost functions such as those estimated during learning.

There has been a growing interest in stochastic modelling and learning with complex data, whose e... more There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening the current applicability of CRFs in the real world and (b) deepening the understanding of theoretical aspects of CRFs. On the application side, we empirically investigate the applications of CRFs in two real world settings. The first application is on a novel domain of Vietnamese accent restoration, in which we need to restore accents of an accent-less Vietnamese sentence. Experiments on half ...

Information Sciences, 2015

In this paper, we discuss issues in designing undergraduate software engineering curricula in the... more In this paper, we discuss issues in designing undergraduate software engineering curricula in the Vietnam environment with regard to current circumstance and its potential changes. The field is still new in institutions, because traditionally they are much more concerned with the science of information processing rather than a systematic study of software engineering. Furthermore, rapid changes in software technology and

Knowledge and Information Systems, 2015

Lecture Notes in Computer Science, 2015

Ranking is a key aspect of many applications, such as information retrieval, question answering, ... more Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query. In this paper, we investigate key issues in designing an effective learning to rank algorithm. These include data representation, the choice of rank functionals, the design of the loss function so that it is correlated with the rank metrics used in evaluation. For the loss function, we study three techniques: approximating the rank metric by a smooth function, decomposition of the loss into a weighted sum of element-wise losses and into a weighted sum of pairwise losses. We then present derivations of piecewise losses using the theory of high-order Markov chains and Markov random fields. In experiments, we evaluate these design aspects on two tasks: answer ranking in a Social Question Answering site, and Web Information Retrieval.

We study the problem of collaborative filtering where ranking information is available. Focusing ... more We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption that the user makes successive choice of items in a stage-wise manner. In particular, we extend the Plackett-Luce model in two ways -introducing parameter factoring to account for user-specific contribution, and modelling the latent community in a generative setting. The second approach relies on loglinear parameterisation, which relaxes the discrete-choice assumption, but makes learning and inference much more involved. We propose MCMC-based learning and inference methods and derive linear-time prediction algorithms.

Learning structured outputs with general structures is computationally challenging, except for tr... more Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our algorithm can handle partial labelling, and thus is particularly attractive in practice where reliable labels are often sparsely observed. In addition, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to an indoor video surveillance scenario, where activities are modelled at multiple levels.

The success of any machine learning system depends critically on effective representations of dat... more The success of any machine learning system depends critically on effective representations of data. In many cases, especially those in vision, it is desirable that a representation scheme uncovers the parts-based, additive nature of the data. Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings. However, when it comes to parts-based discovery, RBMs do not usually produce satisfactory results. We enhance such capacity of RBMs by introducing nonnegativity into the model weights, resulting in a variant called nonnegative restricted Boltzmann machine (NRBM). The NRBM produces not only controllable decomposition of data into interpretable parts but also offers a way to estimate the intrinsic nonlinear dimensionality of data. We demonstrate the capacity of our model on well-known datasets of handwritten digits, faces and documents. The decomposition quality on images is comparable with or better than what produced by the nonnegative matrix factorisation (NMF), and the thematic features uncovered from text are qualitatively interpretable in a similar manner to that of the latent Dirichlet allocation (LDA). However, the learnt features, when used for classification, are more discriminative than those discovered by both NMF and LDA and comparable with those by RBM.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006

Activity recognition is an important issue in building intelligent monitoring systems. We address... more Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithm to a home video surveillance application and demonstrate its efficacy.