Learning from crowds , where the labels of data instances are collected using a crowdsourcing way, has attracted much attention during the past few years. In contrast to a typical crowdsourcing setting where all data instances are assigned to annotators for labeling,  active learning from crowds actively selects a subset of data instances and assigns them to the annotators, thereby reducing the cost of labeling. This paper goes a step further. Rather than assume all annotators must provide labels, we allow the annotators to express that they are unsure about the assigned data instances. By adding the “unsure” option, the workloads for the annotators are somewhat reduced, because saying “unsure” will be easier than trying to provide a crisp label for some difficult data instances. Moreover, it is safer to use “unsure” feedback than to use labels from reluctant annotators because the latter has more chance to be misleading. Furthermore, different annotators may experience difficulty in different data instances, and thus the unsure option provides a valuable ingredient for modeling crowds’ expertise. We propose the ALCU-SVM algorithm for this new learning problem. Experimental studies on simulated and real crowdsourcing data show that, by exploiting the unsure option, ALCU-SVM achieves very promising performance.

We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences. This network can capture active features for unseen segments of a sentence to measure how likely the segments are merged to be the constituents. Given an input sentence, after all the scores of possible segments are computed, an efficient dynamic programming parsing algorithm is used to find the globally optimal parse tree. A similar network is then applied to predict syntactic categories for every node in the parse tree. Our networks archived competitive performance to existing benchmark parsers on the CTB-5 dataset without any task-specific feature engineering.

The recently proposed Gaussian process dynamical models (GPDMs) have been successfully applied to time series modeling. There are four learning algorithms for GPDMs: maximizing a posterior (MAP), fixing the kernel hyperparameters α _ (Fix.α _ ), balanced GPDM (B-GPDM) and two-stage MAP (T.MAP), which are designed for model training with complete data. When data are incomplete, GPDMs reconstruct the missing data using a function of the latent variables before parameter updates, which, however, may cause cumulative errors. In this paper, we present four new algorithms (MAP+, Fix.α + , B-GPDM+ and T.MAP+) for learning GPDMs with incomplete training data and a new conditional model (CM+) for recovering incomplete test data. Our methods adopt the Bayesian framework and can fully and properly use the partially observed data. We conduct experiments on incomplete motion capture data (walk, run, swing and multiple-walker) and make comparisons with the existing four algorithms as well as k-NN, spline interpolation and VGPDS. Our methods perform much better on both training with incomplete data and recovering incomplete test data.

Reordering model adaptation remains a big challenge in statistical machine translation because reordering patterns of translation units often vary dramatically from one domain to another. In this paper, we propose a novel adaptive discriminative reordering model (DRM) based on structural learning, which can capture correspondences among reordering features from two different domains. Exploiting both in-domain and out-of-domain monolingual corpora, our model learns a shared feature representation for cross-domain phrase reordering. Incorporating features of this representation, the DRM trained on out-of-domain corpus generalizes better to in-domain data. Experiment results on the NIST Chinese-English translation task show that our approach significantly outperforms a variety of baselines.

It is notable In real world applications, data are often with multiple that strong modal features can lead to a better performance, modalities. Previous works assumed that each nevertheless, are more expensive, therefore a group of serialized modality contains sufficient information for target feature extraction methods were proposed. These methods and can be treated with equal importance. However, extract weak modal features firstly, and then extract more it is often that different modalities are of various strong modal features gradually to improve the performance importance in real tasks, e.g., the facial feature and reduce the overall cost as well. Marcialis et al.[2010] proposed is weak modality and the fingerprint feature is a serial fusion technique for multiple biometric modal strong modality in ID recognition. In this paper, we features through extracting gaits information and face information point out that different modalities should be treated step by step; Zhang et al.[2014] addressed the serialized with different strategies and propose the Auxiliary multi-modal learning techniques in a semi-supervised information Regularized Machine (ARM), which learning scenario. These methods handle strong and weak works by extracting the most discriminative feature modalities independently while leaving the fact of unsatisfied subspace of weak modality while regularizing the performance on weak modality unexplained.

Crowdsourcing machine translation shows advantages of lower expense in money to collect the translated data. Yet, when compared with translation by trained professionals, results collected from non-professional translators might yield low-quality outputs. A general solution for crowdsourcing practitioners is to employ a large amount of labor force to gather enough redundant data and then solicit from it. Actually we can further save money by avoid collecting bad translations. We propose to score Turkers by their authorities during observation, and then stop hiring the unqualified Turkers. In this way, we bring both opportunities and risks in crowdsourced translation: we can make it cheaper than cheaper while we might suffer from quality loss. In this paper, we propose a graph-based PageRank-HITS Hybrid model to distinguish authoritative workers from unreliable ones. The algorithm captures the intuition that good translation and good workers are mutually reinforced iteratively in the proposed frame. We demonstrate the algorithm will keep the performance while reduce work force and hence cut cost. We run experiments on the NIST 2009 Urdu-to-English evaluation set with Mechanical Turk, and quantitatively evaluate the performance in terms of BLEU score, Pearson correlation and real money.

Cognitive modelling can discover the latent characteristics of examinees for predicting their performance (i.e. scores) on each problem. As cognitive modelling is important for numerous applications, e.g. personalized remedy recommendation, some solutions have been designed in the literature. However, the problem of extracting information from both objective and subjective problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems. Further, we simulate the generation of examination scores by considering both slip and guess factors. Extensive experiments on three real-world datasets prove that FuzzyCDF can predict examinee performance more effectively, and the output of FuzzyCDF is also interpretative.

Covariate shift is a fundamental problem for learning in non-stationary environments where the conditional distribution p(y|x) is the same between training and test data while their marginal distributions p tr (x) and p te (x) are different. Although many covariate shift correction techniques remain effective for real world problems, most do not scale well in practice. In this paper, using inspiration from recent optimization techniques, we apply the Frank-Wolfe algorithm to two well-known covariate shift correction techniques, Kernel Mean Matching (KMM) and Kullback-Leibler Importance Estimation Procedure (KLIEP), and identify an important connection between kernel herding and KMM. Our complexity analysis shows the benefits of the Frank-Wolfe approach over projected gradient methods in solving KMM and KLIEP. An empirical study then demonstrates the effectiveness and efficiency of the Frank-Wolfe algorithm for correcting covariate shift in practice.

Differential privacy preserving regression models guarantee protection against attempts to infer whether a subject was included in the training set used to derive a model. It is not designed to protect attribute privacy of a target individual when model inversion attacks are launched. In model inversion attacks, an adversary uses the released model to make predictions of sensitive attributes (used as input to the model) of a target individual when some background information about the target individual is available. Previous research showed that existing differential privacy mechanisms cannot effectively prevent model inversion attacks while retaining model efficacy. In this paper, we develop a novel approach which leverages the functional mechanism to perturb coefficients of the polynomial representation of the objective function but effectively balances the privacy budget for sensitive and non-sensitive attributes in learning the differential privacy preserving regression model. Theoretical analysis and empirical evaluations demonstrate our approach can effectively prevent model inversion attacks and retain model utility.

Emotion detection can considerably enhance our understanding of users' emotional states. Understanding users' emotions especially in a real-time setting can be pivotal in improving user interactions and understanding their preferences. In this paper, we propose a constraint optimization framework to discover emotions from social media content of the users. Our framework employs several novel constraints such as emotion bindings, topic correlations, along with specialized features proposed by prior work and well-established emotion lexicons. We propose an efficient inference algorithm and report promising empirical results on three diverse datasets.