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Edward Chang

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8 papers

1 author row

AAAI Conference 2018 Conference Paper

Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning

Hao-Cheng Kao
Kai-Fu Tang
Edward Chang

Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking ﬁrst assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve a high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which signiﬁcantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.

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NeurIPS Conference 2018 Conference Paper

REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis

Yu-Shao Peng
Kai-Fu Tang
Hsuan-Tien Lin
Edward Chang

This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em feature rebuilding}, to improve the performance of online symptom checking for disease diagnosis. Reward shaping can guide the search of policy towards better directions. Feature rebuilding can guide the agent to learn correlations between features. Together, they can find symptom queries that can yield positive responses from a patient with high probability. Experimental results justify that the two techniques in REFUEL allows the symptom checker to identify the disease more rapidly and accurately.

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NeurIPS Conference 2017 Conference Paper

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

Kristofer Bouchard
Alejandro Bujan
Fred Roosta
Shashanka Ubaru
Mr. Prabhat
Antoine Snijders
Jian-Hua Mao
Edward Chang

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications, e. g. , neuroscience, genetics, systems biology, etc. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce the Union of Intersections (UoI) method, a flexible, modular, and scalable framework for enhanced model selection and estimation. The method performs model selection and model estimation through intersection and union operations, respectively. We show that UoI can satisfy the bi-criteria of low-variance and nearly unbiased estimation of a small number of interpretable features, while maintaining high-quality prediction accuracy. We perform extensive numerical investigation to evaluate a UoI algorithm ($UoI_{Lasso}$) on synthetic and real data. In doing so, we demonstrate the extraction of interpretable functional networks from human electrophysiology recordings as well as the accurate prediction of phenotypes from genotype-phenotype data with reduced features. We also show (with the $UoI_{L1Logistic}$ and $UoI_{CUR}$ variants of the basic framework) improved prediction parsimony for classification and matrix factorization on several benchmark biomedical data sets. These results suggest that methods based on UoI framework could improve interpretation and prediction in data-driven discovery across scientific fields.

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AAAI Conference 2016 Conference Paper

Tweet Timeline Generation with Determinantal Point Processes

Jin-ge Yao
Feifan Fan
Wayne Xin Zhao
Xiaojun Wan
Edward Chang
Jianguo Xiao

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

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AAAI Conference 2015 Conference Paper

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds

Hongyang Zhang
Zhouchen Lin
Chao Zhang
Edward Chang

Subspace recovery from noisy or even corrupted data is critical for various applications in machine learning and data analysis. To detect outliers, Robust PCA (R-PCA) via Outlier Pursuit was proposed and had found many successful applications. However, the current theoretical analysis on Outlier Pursuit only shows that it succeeds when the sparsity of the corruption matrix is of O(n/r), where n is the number of the samples and r is the rank of the intrinsic matrix which may be comparable to n. Moreover, the regularization parameter is suggested as 3/(7 √ γn), where γ is a parameter that is not known a priori. In this paper, with incoherence condition and proposed ambiguity condition we prove that Outlier Pursuit succeeds when the rank of the intrinsic matrix is of O(n/ log n) and the sparsity of the corruption matrix is of O(n). We further show that the orders of both bounds are tight. Thus R-PCA via Outlier Pursuit is able to recover intrinsic matrix of higher rank and identify much denser corruptions than what the existing results could predict. Moreover, we suggest that the regularization parameter be chosen as 1/ √ log n, which is definite. Our analysis waives the necessity of tuning the regularization parameter and also significantly extends the working range of the Outlier Pursuit. Experiments on synthetic and real data verify our theories.

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IJCAI Conference 2015 Conference Paper

Network Representation Learning with Rich Text Information

Cheng Yang
Zhiyuan Liu
Deli Zhao
Maosong Sun
Edward Chang

Representation learning has shown its effectiveness in many tasks such as image classification and text mining. Network representation learning aims at learning distributed vector representation for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Most network representation learning methods investigate network structures for learning. In reality, network vertices contain rich information (such as text), which cannot be well applied with algorithmic frameworks of typical representation learning methods. By proving that DeepWalk, a state-ofthe-art network representation method, is actually equivalent to matrix factorization (MF), we propose text-associated DeepWalk (TADW). TADW incorporates text features of vertices into network representation learning under the framework of matrix factorization. We evaluate our method and various baseline methods by applying them to the task of multi-class classification of vertices. The experimental results show that, our method outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small. The source code of this paper can be obtained from https: //github. com/albertyang33/TADW.

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IJCAI Conference 2015 Conference Paper

Scalable Gaussian Process Regression Using Deep Neural Networks

Wenbing Huang
Deli Zhao
Fuchun Sun
Huaping Liu
Edward Chang

We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pre-train the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.

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NeurIPS Conference 2007 Conference Paper

Parallelizing Support Vector Machines on Distributed Computers

Kaihua Zhu
Hao Wang
Hongjie Bai
Jian Li
Zhihuan Qiu
Hang Cui
Edward Chang

Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let $n$ denote the number of training instances, $p$ the reduced matrix dimension after factorization ($p$ is significantly smaller than $n$), and $m$ the number of machines. PSVM reduces the memory requirement from $\MO$($n^2$) to $\MO$($np/m$), and improves computation time to $\MO$($np^2/m$). Empirical studies on up to $500$ computers shows PSVM to be effective.

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