Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publications and claim those that are theirs. Indirectly, these tools allow for the inexpensive collection of large annotated training data, which can be further leveraged to build a complementary automated disambiguation system capable of inferring patterns for identifying publications written by the same person. Building on more than 1 million publicly released crowdsourced annotations, we propose an automated author disambiguation solution exploiting this data (i) to learn an accurate classifier for identifying coreferring authors and (ii) to guide the clustering of scientific publications by distinct authors in a semi-supervised way. To the best of our knowledge, our analysis is the first to be carried out on data of this size and coverage. With respect to the state of the art, we validate the general pipeline used in most existing solutions, and improve by: (i) proposing phonetic-based blocking strategies, thereby increasing recall; and (ii) adding strong ethnicity-sensitive features for learning a linkage function, thereby tailoring disambiguation to non-Western author names whenever necessary.

Partitioning a dataset into classes based on a similarity between data points, known as cluster analysis, is one of the most basic and practically important problems in data analysis and machine learning. It has a vast array of applications from speech recognition to image analysis to bioinformatics and to data compression. There is an extensive literature on the subject, including a number of different methodologies as well as their various practical and theoretical aspects [11]. In recent years spectral clustering--a class of methods based on the eigenvectors of a certain matrix, typically the graph Laplacian constructed from data--has become a widely used method for cluster analysis. This is due to the simplicity of the algorithm, a number of desirable properties it exhibits and its amenability to theoretical analysis. In its simplest form, spectral bi-partitioning is an attractively straightforward algorithm based on thresholding the second bottom eigenvector of the Laplacian matrix of a graph. However, the more practically significant problem of multiway spectral clustering is considerably more complex. While hierarchical methods based on a sequence of binary splits have been used, the most common approaches use k-means or weighted k-means clustering in the spectral space or related iterative procedures [17, 15, 2, 25].

This study aimed to find temporal clusters for several commodity prices using the threshold nonlinear autoregressive model. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. The clustering of commodity prices was examined using the proposed clustering approach based on time series models to incorporate the time varying properties of price series into the clustering scheme. Accordingly, the primary aim in this study was grouping time series according to the similarity between their Data Generating Mechanisms (DGMs) rather than comparing pattern similarities in the time series traces. The approximation to the DGM of each series was accomplished using threshold autoregressive models, which are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time dimension. Furthermore, generating a time varying commodity price index and sub-indexes can become possible. Consequently, we conducted a simulation study to assess the effectiveness of the proposed clustering approach and the results are presented for both the simulated and real data sets. Keywords: Clustering Nonlinear Time Series Models, Regime Switching, Spectral 1. Introduction The movement of commodity prices and the associated dynamics are interrelated with economics and directly affect many industries.

We develop a framework to solve machine learning problems in cases where latent geometric structure in the feature space may be exploited. We consider cases where the number of training examples is either very large, or signals are sequentially observed by a platform operating in real-time such as an autonomous robot. In the former case, since the sample size is large-scale, processing a few training examples at a time is necessary due to computational cost. However, doing so at a centralized location may be impractical, which motivates the use of learning techniques that may be done collaboratively by a network of interconnected computing servers. In the later case, an autonomous robot with no priors on its operating environment only has access to information based on the path it has traversed, which may omit regions of the feature space crucial for tasks such as learning-based control. By communicating with other robots in a network, individuals may learn over a broader domain associated with that which has been explored by the whole network, and thus more effectively solve autonomous learning tasks.

ABSTRACT In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency. In this paper, we are interested in estimating sparse inverse covariance matrices when samples are distributed into different machines. We address communication efficiency by proposing a method where, in a single round of communication, each machine transfers a small subset of the entries of the inverse covariance matrix. We show that, with this efficient distributed method, the error rates can be comparable with estimation in a non-distributed setting, and correct model selection is still possible. Practical performance is shown through simulations.

Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording processes. We investigate the efficacy of three most effective randomized machine learning techniques - random forests, gradient boosting machines, and deep neural nets with dropout - in predicting suicide risk. Using a cohort of mental health patients from a regional Australian hospital, we compare the predictive performance with popular traditional approaches - clinician judgments based on a checklist, sparse logistic regression and decision trees. The randomized methods demonstrated robustness against data redundancies and superior predictive performance on AUC and F-measure. Keywords: Suicide risk, Electronic medical record, Predictive models, Randomized machine learning, Deep learning 1. Introduction Every year, about 2000 Australians die by suicide causing huge trauma to families, friends, workplaces and communities[1].

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims to discover the number of latent classes in the patients population, and train a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific class. Self-taught transfer learning is used to transfer the knowledge of latent classes learned from the domain of clinically stable patients to the domain of clinically deteriorating patients. For new patients, the posterior beliefs of all GP experts about the patient's clinical status given her physiological data stream are computed, and a personalized risk score is evaluated as a weighted average of those beliefs, where the weights are learned from the patient's hospital admission information. Experiments on a heterogeneous cohort of 6,313 patients admitted to Ronald Regan UCLA medical center show that our risk score outperforms the currently deployed risk scores, such as MEWS and Rothman scores.

We propose an efficient method for approximating natural gradient descent in neural networks which we call Kronecker-Factored Approximate Curvature (K-FAC). K-FAC is based on an efficiently invertible approximation of a neural network's Fisher information matrix which is neither diagonal nor low-rank, and in some cases is completely non-sparse. It is derived by approximating various large blocks of the Fisher (corresponding to entire layers) as being the Kronecker product of two much smaller matrices. While only several times more expensive to compute than the plain stochastic gradient, the updates produced by K-FAC make much more progress optimizing the objective, which results in an algorithm that can be much faster than stochastic gradient descent with momentum in practice. And unlike some previously proposed approximate natural-gradient/Newton methods which use high-quality non-diagonal curvature matrices (such as Hessian-free optimization), K-FAC works very well in highly stochastic optimization regimes. This is because the cost of storing and inverting K-FAC's approximation to the curvature matrix does not depend on the amount of data used to estimate it, which is a feature typically associated only with diagonal or low-rank approximations to the curvature matrix.

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.

This is actually a good question. For a categorical variable, can the model say that some veles are significant, some levels are not. Typically after a regression we look at the ANOVA (Analysis of Variance) table. There we have 1 row per independent variable. In other words, in My example we will see a single row corresponding to the variable COLOR (as opposed to say 2 rows for I_green and I_blue).