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Multi-layer Representation learning for Medical Concepts 16

Multi-layer Representation Learning for Medical Concepts

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Choi, Edward Bahadori, Mohammad Taha Searles, Elizabeth Coffey, Catherine Thompson, Michael Bost, James Tejedor-Sojo, Javier Sun, Jimeng Georgia Inst Technol Atlanta GA 30332 USA Childrens Healthcare Atlanta Atlanta GA USA

ISBN: (纸本)9781450342322

Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits from Electronic Health Records (EHR) has broad applications in healthcare analytics. Patient EHR data consists of a sequence of visits over time, where each visit includes multiple medical concepts, e.g., diagnosis, procedure, and medication codes. This hierarchical structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within a visit. In this work, we propose Med2Vec, which not only learns the representations for both medical codes and visits from large EHR datasets with over million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec shows significant improvement in prediction accuracy in clinical applications compared to baselines such as Skip-gram, GloVe, and stacked autoencoder, while providing clinically meaningful interpretation.

关键词： Representation learning Medical Concepts Healthcare Analytics Neural Networks

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Question Independent Grading using Machine learning: The Case of Computer Program Grading 16

Question Independent Grading using Machine Learning: The Cas...

引用

22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Singh, Gursimran Srikant, Shashank Aggarwal, Varun Aspiring Minds Gurgaon Haryana India

ISBN: (纸本)9781450342322

learning supervised models to grade open-ended responses is an expensive process. A model has to be trained for every prompt/question separately, which in turn requires graded samples. In automatic programming evaluation specifically, the focus of this work, this issue is amplified. The models have to be trained not only for every question but also for every language the question is offered in. Moreover, the availability and time taken by experts to create a labeled set of programs for each question is a major bottleneck in scaling such a system. We address this issue by presenting a method to grade computer programs which requires no manually assigned labeled samples for grading responses to a new, unseen question. We extend our previous work [25] wherein we introduced a grammar of features to learn question specific models. In this work, we propose a method to transform those features into a set of features that maintain their structural relation with the labels across questions. Using these features we learn one supervised model, across questions for a given language, which can then be applied to an ungraded response to an unseen question. We show that our method rivals the performance of both, question specific models and the consensus among human experts while substantially outperforming extant ways of evaluating codes. We demonstrate the system's value by deploying it to grade programs in a high stakes assessment. The learning from this work is transferable to other grading tasks such as math question grading and also provides a new variation to the supervised learning approach.

关键词： Recruitment Automatic grading MOOC Feature engineering Supervised learning One-class learning Question independent learning

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LTE in Unlicensed Spectrum: Are We There Yet? 16

LTE in Unlicensed Spectrum: Are We There Yet?

引用

22nd Annual international conference on Mobile Computing and Networking (MobiCom)

作者： Chai, Eugene Sundaresan, Karthik Khojastepour, Mohammad A. Rangarajan, Sampath NEC Labs Mobile Commun & Networking Princeton NJ 08540 USA

ISBN: (纸本)9781450342261

In this work, we explore the potential and impact of unlicensed LTE on WiFi in unlicensed spectrum. Our experiments demonstrate that the large asymmetry in the channel access methodologies employed by WiFi and LTE (carrier sensing/notification in WiFi, energy sensing alone in LTE-U), can result LTE-U completely blocking WiFi transmissions, and causing significant degradation to either technologies from collisions. We address this critical sensing asymmetry with ULTRON, a LTE-WiFi co-existence solution that integrates WiFi's carrier sensing and notification mechanisms into LTE, without any modifications to the LTE PHY standard. ULTRON operates at the LTE base station and consists of two key components: (a) WiFi embedding that embeds appropriate data into the LTE-subframes through an intelligent reverse-engineering of the LTE PHY, so as to realize a WiFi PLCP preamble-header transmission over the air directly using the LTE PHY;and (b) scalable WiFi sensing that employs a single WiFi interface and maximizes its carrier sensing benefits to all the unlicensed channels operating at the LTE node. Our evaluations demonstrate that ULTRON can increase the WiFi and LTE throughput by 5 x and 6 x respectively, resulting from a sharp reduction in LTE-WiFi interference.

关键词： Reverse engineering

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Predicting Matchups and Preferences in Context 16

Predicting Matchups and Preferences in Context

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Chen, Shuo Joachims, Thorsten Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9781450342322

We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce. Unlike existing models for these tasks, our model not only learns representations of the items in a more expressive latent vector space, but also models how context modifies matchup and preference outcomes. For example, the context "weather" may alter the winning probability in a tennis match, or the fact that the user is on a mobile device may alter his preferences among restaurants. More generally, the model is capable of handling any symmetric game/comparison problem that can be described by vectorized player/item and game/context features. We provide a comprehensive evaluation of its predictive performance with real datasets from both domains to show its ability to predict preference and game outcomes more accurately than existing models. Furthermore, we demonstrate on synthetic datasets the expressiveness of the model when compared against theoretical limits.

关键词： preference ranking games matchup pairwise comparison representation learning sports

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Robust and Effective Metric learning Using Capped Trace Norm 22

Robust and Effective Metric Learning Using Capped Trace Norm

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Huo, Zhouyuan Nie, Feiping Huang, Heng Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781450342322

Metric learning aims at automatically learning a metric from pair or triplet based constraints in data, and it can be potentially beneficial whenever the notion of metric between instances plays a nontrivial role. In Mahalanobis distance metric learning, distance matrix M is in symmetric positive semi-definite cone, and in order to avoid overfitting and to learn a better Mahalanobis distance from weakly supervised constraints, the low-rank regularization has been often imposed on matrix M to learn the correlations between features and samples. As the approximations of the rank minimization function, the trace norm and Fantope have been utilized to regularize the metric learning objectives and achieve good performance. However, these low-rank regularization models are either not tight enough to approximate rank minimization or time-consuming to tune an optimal rank. In this paper, we introduce a novel metric learning model using the capped trace norm based regularization, which uses a singular value threshold to constraint the metric matrix M as low-rank explicitly such that the rank of matrix M is stable when the large singular values vary. The capped trace norm regularization can also be viewed as the adaptive Fantope regularization. We minimize singular values which are less than threshold value and the rank of M is not necessary to be k, thus our method is more stable and applicable in practice when we do not know the optimal rank of matrix M. We derive an efficient optimization algorithm to solve the proposed new model and the algorithm convergence proof is also provided in this paper. We evaluate our method on a variety of challenging benchmarks, such as LFW and Pubfig datasets. Face verification experiments are performed and results show that our method consistently outperforms the state-of-the-art metric learning algorithms.

关键词： Metric learning Capped Trace Norm Low-Rank Approximation Face Verification

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Proceedings - 2016 IEEE 22nd international conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2016

Proceedings - 2016 IEEE 22nd International Conference on Emb...

引用

22nd IEEE international conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2016

ISBN: (纸本)9781509024797

The proceedings contain 50 papers. The topics discussed include: schedule management framework for cloud-based future automotive software systems;design and implementation of kernel binder cache to accelerate Android IPC;transforming real-time task graphs to improve schedulability;a tool for measuring supply functions of execution platforms;parameter adaption for generalized multiframe tasks and applications to self-suspending tasks;a simplex architecture for intelligent and safe unmanned aerial vehicles;building a large scale wireless sensor network for the industrial environment;dynamic low-power listening with data-rate proportional wakeup period management;scheduling low-utilized real-time systems with end-to-end timing constraints;lightweight authentication method for controller area network;machine learning-based image classification for wireless camera sensor networks;a magnetic fingerprinting-based precise automobile localization using smartphones;GPS-less localization system in vehicular networks using dedicated short range communication;and scheduling real-time packets with non-preemptive regions on priority-based NoCs.

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A Multi-Task learning Formulation for Survival Analysis 16

A Multi-Task Learning Formulation for Survival Analysis

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Li, Yan Wang, Jie Ye, Jieping Reddy, Chandan K. Wayne State Univ Detroit MI 48202 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450342322

Predicting the occurrence of a particular event of interest at future time points is the primary goal of survival analysis. The presence of incomplete observations due to time limitations or loss of data traces is known as censoring which brings unique challenges in this domain and differentiates survival analysis from other standard regression methods. The popularly used survival analysis methods such as Cox proportional hazard model and parametric survival regression suffer from some strict assumptions and hypotheses that are not realistic in most of the real-world applications. To overcome the weaknesses of these two types of methods, we reformulate the survival analysis problem as a multi-task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration. We propose an indicator matrix to enable the multi-task learning algorithm to handle censored instances and incorporate some of the important characteristics of survival problems such as non-negative non-increasing list structure into our model through max-heap projection. We employ the l(2,1)-norm penalty to learn a shared representation across related tasks and hence select important features and alleviate over-fitting in high-dimensional feature spaces;thus, reducing the prediction error of each task. To efficiently handle the two non-smooth constraints, in this paper, we propose an optimization method which employs Alternating Direction Method of Multipliers (ADMM) algorithm to solve the proposed multi-task learning problem. We demonstrate the performance of the proposed method using real world microarray gene expression datasets and show that our methods outperform state-of-the-art methods.

关键词： Survival analysis Multi-task learning regularization high-dimensional data

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Unbounded Human learning: Optimal Scheduling for Spaced Repetition 16

Unbounded Human Learning: Optimal Scheduling for Spaced Repe...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Reddy, Siddharth Labutov, Igor Banerjee, Siddhartha Joachims, Thorsten Cornell Univ Dept Comp Sci Ithaca NY 14853 USA Cornell Univ Elect & Comp Engn Ithaca NY 14853 USA Cornell Univ Operat Res & Informat Engn Ithaca NY 14853 USA

ISBN: (纸本)9781450342322

In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays a crucial role in the design of educational software, leading to a trade-off between teaching new material and reviewing what has already been taught. A common way to balance this trade-off is spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flash card software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.

关键词： Spaced Repetition Queueing Models Human Memory

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Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries 16

Predictors without Borders: Behavioral Modeling of Product A...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Khan, Muhammad Raza Blumenstock, Joshua E. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450342322

Billions of people around the world live without access to banks or other formal financial institutions. In the past several years, many mobile operators have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, in many countries adoption of Mobile Money still remains anemic. In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption. We describe a novel approach to feature engineering that uses a Deterministic Finite Automaton to construct thousands of behavioral metrics of phone use from a concise set of recursive rules. These features provide the foundation for a predictive model that is tested on mobile phone operators logs from Ghana, Pakistan, and Zambia, three very different developing-country contexts. The results highlight the key correlates of Mobile Money use in each country, as well as the potential for such methods to predict and drive adoption. More generally, our analysis provides insight into the extent to which homogenized supervised learning methods can generalize across geographic contexts. We find that without careful tuning, a model that performs very well in one country frequently does not generalize to another.

关键词： Mobile Money Feature engineering Gradient boosting Product adoption Supervised learning

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Collaborative Multi-View Denoising 16

Collaborative Multi-View Denoising

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Zhang, Lei Wang, Shupeng Zhang, Xiaoyu Wang, Yong Li, Binbin Shen, Dinggang Ji, Shuiwang Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ N Carolina BRIC Chapel Hill NC 27599 USA Washington State Univ Dept Elect Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9781450342322

In multi-view learning applications, like multimedia analysis and information retrieval, we often encounter the corrupted view problem in which the data are corrupted by two different types of noises, i.e., the intra- and inter-view noises. The noises may affect these applications that commonly acquire complementary representations from different views. Therefore, how to denoise corrupted views from multi-view data is of great importance for applications that integrate and analyze representations from different views. However, the heterogeneity among multi-view representations brings a significant challenge on denoising corrupted views. To address this challenge, we propose a general framework to jointly denoise corrupted views in this paper. Specifically, aiming at capturing the semantic complementarity and distributional similarity among different views, a novel Heterogeneous Linear Metric learning (HLML) model with low-rank regularization, leave-one-out validation, and pseudo-metric constraints is proposed. Our method linearly maps multi view data to a high-dimensional feature-homogeneous space that embeds the complementary information from different views. Furthermore, to remove the intra- and inter-view noises, we present a new Multi-view Semi-supervised Collaborative Denoising (MSCD) method with elementary transformation constraints and gradient energy competition to establish the complementary relationship among the heterogeneous representations. Experimental results demonstrate that our proposed methods are effective and efficient.

关键词： Multi-view denoising heterogeneity metric learning

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