Spoken Language Understanding aims at mapping a natural language spoken sentence into a semantic representation. In the last decade two main approaches have been pursued: generative and discriminative models. The former is more robust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the way in which these approaches encode prior knowledge is very diffe...

Direct feedback of users of search engines by click information is naturally noisy. Ranking models that integrate such feedback in their training process must cope with this noise. In worst case such noise can lead to large variance among the results for different queries in the resulting rankings. We propose to integrate model averaging like bagging and random forest methods to reduce the vari...

Deep neural networks are a promising technology achieving state-of-the-art results in biological and healthcare domains. Unfortunately, DNNs are notorious for their non-interpretability. Clinicians are averse to black boxes and thus interpretability is paramount to broadly adopting this technology. We aim to close this gap by proposing a new general feature ranking method for deep learning. We ...

Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking functions from supervised data. We assume that each instance in the training data is associated with a list of preferences over the label-set, however we do not assume that this list is either complete or consistent. Thi...

This paper investigates two strategies for improving coreference resolution: (1) training separate models that specialize in particular types of mentions (e.g., pronouns versus proper nouns) and (2) using a ranking loss function rather than a classification function. In addition to being conceptually simple, these modifications of the standard single-model, classification-based approach also de...

Ranking models have recently been proposed for cascaded object detection, and have been shown to improve over regression or binary classification in this setting [1, 2]. Rather than train a classifier in a binary setting and interpret the function post hoc as a ranking objective, these approaches directly optimize regularized risk objectives that seek to score highest the windows that most clos...

The logistic Generalized Estimating Equations (logisticGEE) models have been extensively used for analyzing clustered binary data. However, assessing the goodness-of-fit and predictability of these models is problematic due to the fact that no likelihood is available and the observations can be correlated within a cluster. In this paper we propose a new measure for estimating the generalization...

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends...

Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant or background words, or represent insignificant themes. ...

Traditional data envelopment analysis models split decision making units into two basic groups, efficient and inefficient. They are based on solving linear optimization problems and currently they represent very popular tool for efficiency and performance evaluation. Efficiency scores of inefficient units allows their ranking but efficient units cannot be ranked directly because of their maximu...