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Über dieses Buch

The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.

The total of 104 papers presented in these books was carefully reviewed and selected from 364 submissions. The papers were organized in topical sections named as follows:
Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.
Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.
Part III: applied data science track; nectar track; and demo track.



Anomaly Detection


Concentration Free Outlier Detection

We present a novel notion of outlier, called Concentration Free Outlier Factor (CFOF), having the peculiarity to resist concentration phenomena that affect other scores when the dimensionality of the feature space increases. Indeed we formally prove that $$\hbox {CFOF}$$ does not concentrate in intrinsically high-dimensional spaces. Moreover, $$\hbox {CFOF}$$ is adaptive to different local density levels and it does not require the computation of exact neighbors in order to be reliably computed. We present a very efficient technique, named $${\textit{fast-}\hbox {CFOF}}$$, for detecting outliers in very large high-dimensional datasets. The technique is efficiently parallelizable, and we provide a MIMD-SIMD implementation. Experimental results witness for scalability and effectiveness of the technique and highlight that $$\hbox {CFOF}$$ exhibits state of the art detection performances.

Fabrizio Angiulli

Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection

In this paper we address the anomaly detection problem in a supervised setting where positive examples might be very sparse. We tackle this task with a learning to rank strategy by optimizing a differentiable smoothed surrogate of the so-called Average Precision (AP). Despite its non-convexity, we show how to use it efficiently in a stochastic gradient boosting framework. We show that using AP is much better to optimize the top rank alerts than the state of the art measures. We demonstrate on anomaly detection tasks that the interest of our method is even reinforced in highly unbalanced scenarios.

Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton

Robust, Deep and Inductive Anomaly Detection

PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use for anomaly detection. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some data points to be arbitrarily corrupted; however, this does not resolve the second issue, and indeed introduces the new issue that one can no longer inductively find anomalies on a test set. This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model’s effectiveness.

Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla

Sentiment Informed Cyberbullying Detection in Social Media

Cyberbullying is a phenomenon which negatively affects the individuals, the victims suffer from various mental issues, ranging from depression, loneliness, anxiety to low self-esteem. In parallel with the pervasive use of social media, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include the use of standards and guidelines, human moderators, and blacklists based on the profane words. However, these mechanisms fall short in social media and cannot scale well. Therefore, it is necessary to develop a principled learning framework to automatically detect cyberbullying behaviors. However, it is a challenging task due to short, noisy and unstructured content information and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behaviors and the correlation with emotions, we propose to leverage sentiment information to detect cyberbullying behaviors in social media by proposing a sentiment informed cyberbullying detection framework. Experimental results on two real-world, publicly available social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.

Harsh Dani, Jundong Li, Huan Liu

zooRank: Ranking Suspicious Entities in Time-Evolving Tensors

Most user-based websites such as social networks (Twitter, Facebook) and e-commerce websites (Amazon) have been targets of group fraud (multiple users working together for malicious purposes). How can we better rank malicious entities in such cases of group-fraud? Most of the existing work in group anomaly detection detects lock-step behavior by detecting dense blocks in matrices, and recently, in tensors. However, there is no principled way of scoring the users based on their participation in these dense blocks. In addition, existing methods do not take into account temporal features while detecting dense blocks, which are crucial to uncover bot-like behaviors. In this paper (a) we propose a systematic way of handling temporal information; (b) we give a list of axioms that any individual suspiciousness metric should satisfy; (c) we propose zooRank, an algorithm that finds and ranks suspicious entities (users, targeted products, days, etc.) effectively in real-world datasets. Experimental results on multiple real-world datasets show that zooRank detected and ranked the suspicious entities with high accuracy, while outperforming the baseline approach.

Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, Jürgen Pfeffer

Computer Vision


Alternative Semantic Representations for Zero-Shot Human Action Recognition

A proper semantic representation for encoding side information is key to the success of zero-shot learning. In this paper, we explore two alternative semantic representations especially for zero-shot human action recognition: textual descriptions of human actions and deep features extracted from still images relevant to human actions. Such side information are accessible on Web with little cost, which paves a new way in gaining side information for large-scale zero-shot human action recognition. We investigate different encoding methods to generate semantic representations for human actions from such side information. Based on our zero-shot visual recognition method, we conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic representations. The results suggest that our proposed text- and image-based semantic representations outperform traditional attributes and word vectors considerably for zero-shot human action recognition. In particular, the image-based semantic representations yield the favourable performance even though the representation is extracted from a small number of images per class.Code related to this chapter is available at: related to this chapter are available at:

Qian Wang, Ke Chen

Early Active Learning with Pairwise Constraint for Person Re-identification

Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised re-id models is an extremely challenging and time-consuming task because it requires every pair of images across no-overlapping camera views to be labeled. Moreover, in the early stage of experiments, when labor resources are limited, only a small number of data can be labeled. Thus, it is essential to design an effective algorithm to select the most representative samples. This is referred as early active learning or early stage experimental design problem. The pairwise relationship plays a vital role in the re-id problem, but most of the existing early active learning algorithms fail to consider this relationship. To overcome this limitation, we propose a novel and efficient early active learning algorithm with a pairwise constraint for person re-identification in this paper. By introducing the pairwise constraint, the closeness of similar representations of instances is enforced in active learning. This benefits the performance of active learning for re-id. Extensive experimental results on four benchmark datasets confirm the superiority of the proposed algorithm.

Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang

Guiding InfoGAN with Semi-supervision

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as $$0.22\%$$, max. $$10\%$$ of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training convergence. The architecture maintains the ability to disentangle latent variables for which no labels are available. Finally, we contribute an information-theoretic reasoning on how introducing semi-supervision increases mutual information between synthetic and real data. Code related to this chapter is available at:

Adrian Spurr, Emre Aksan, Otmar Hilliges

Scatteract: Automated Extraction of Data from Scatter Plots

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.

Mathieu Cliche, David Rosenberg, Dhruv Madeka, Connie Yee

Unsupervised Diverse Colorization via Generative Adversarial Networks

Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.

Yun Cao, Zhiming Zhou, Weinan Zhang, Yong Yu

Ensembles and Meta Learning


Dynamic Ensemble Selection with Probabilistic Classifier Chains

Dynamic ensemble selection (DES) is the problem of finding, given an input $$\mathbf{x }$$, a subset of models among the ensemble that achieves the best possible prediction accuracy. Recent studies have reformulated the DES problem as a multi-label classification problem and promising performance gains have been reported. However, their approaches may converge to an incorrect, and hence suboptimal, solution as they don’t optimize the true - but non standard - loss function directly. In this paper, we show that the label dependencies have to be captured explicitly and propose a DES method based on Probabilistic Classifier Chains. Experimental results on 20 benchmark data sets show the effectiveness of the proposed method against competitive alternatives, including the aforementioned multi-label approaches. This study is reproducible and the source code has been made available online (

Anil Narassiguin, Haytham Elghazel, Alex Aussem

Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks

Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denoted as MA-DNN, in which parallel workers conduct respective training based on their own local data while the parameters of local models are periodically communicated and averaged to obtain a global model which serves as the new start of local models. However, since DNN is a highly non-convex model, averaging parameters cannot ensure that such global model can perform better than those local models. To tackle this problem, we introduce a new parallel training framework called Ensemble-Compression, denoted as EC-DNN. In this framework, we propose to aggregate the local models by ensemble, i.e., averaging the outputs of local models instead of the parameters. As most of prevalent loss functions are convex to the output of DNN, the performance of ensemble-based global model is guaranteed to be at least as good as the average performance of local models. However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment. Thus, we carry out model compression after each ensemble, specialized by a distillation based method in this paper, to reduce the size of the global model to be the same as the local ones. Our experimental results demonstrate the prominent advantage of EC-DNN over MA-DNN in terms of both accuracy and speedup.

Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu

Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks

Cutset Networks (CNets) are density estimators leveraging context-specific independencies recently introduced to provide exact inference in polynomial time. Learning a CNet is done by firstly building a weighted probabilistic OR tree and then estimating tractable distributions as its leaves. Specifically, selecting an optimal OR split node requires cubic time in the number of the data features, and even approximate heuristics still scale in quadratic time. We introduce Extremely Randomized Cutset Networks (XCNets), CNets whose OR tree is learned by performing random conditioning. This simple yet surprisingly effective approach reduces the complexity of OR node selection to constant time. While the likelihood of an XCNet is slightly worse than an optimally learned CNet, ensembles of XCNets outperform state-of-the-art density estimators on a series of standard benchmark datasets, yet employing only a fraction of the time needed to learn the competitors. Code and data related to this chapter are available at:

Nicola Di Mauro, Antonio Vergari, Teresa M. A. Basile, Floriana Esposito

Feature Selection and Extraction


Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: (1) how to directly learn discrete binary codes? (2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of mAP for image retrieval and object recognition. Code is available at

Jingkuan Song, Tao He, Hangbo Fan, Lianli Gao

Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets

Feature ranking is beneficial to gain knowledge and to identify the relevant features from a high-dimensional dataset. However, in several datasets, few features by itself might have small correlation with the target classes, but by combining these features with some other features, they can be strongly correlated with the target. This means that multiple features exhibit interactions among themselves. It is necessary to rank the features based on these interactions for better analysis and classifier performance. However, evaluating these interactions on large datasets is computationally challenging. Furthermore, datasets often have features with redundant information. Using such redundant features hinders both efficiency and generalization capability of the classifier. The major challenge is to efficiently rank the features based on relevance and redundance on mixed datasets. In this work, we propose a filter-based framework based on Relevance and Redundancy (RaR), RaR computes a single score that quantifies the feature relevance by considering interactions between features and redundancy. The top ranked features of RaR are characterized by maximum relevance and non-redundance. The evaluation on synthetic and real world datasets demonstrates that our approach outperforms several state-of-the-art feature selection techniques. Code and data related to this chapter are available at:

Arvind Kumar Shekar, Tom Bocklisch, Patricia Iglesias Sánchez, Christoph Nikolas Straehle, Emmanuel Müller

Non-redundant Spectral Dimensionality Reduction

Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization. However, despite their popularity, these algorithms suffer from a major limitation known as the “repeated eigen-directions” phenomenon. That is, many of the embedding coordinates they produce typically capture the same direction along the data manifold. This leads to redundant and inefficient representations that do not reveal the true intrinsic dimensionality of the data. In this paper, we propose a general method for avoiding redundancy in spectral algorithms. Our approach relies on replacing the orthogonality constraints underlying those methods by unpredictability constraints. Specifically, we require that each embedding coordinate be unpredictable (in the statistical sense) from all previous ones. We prove that these constraints necessarily prevent redundancy, and provide a simple technique to incorporate them into existing methods. As we illustrate on challenging high-dimensional scenarios, our approach produces significantly more informative and compact representations, which improve visualization and classification tasks.

Yochai Blau, Tomer Michaeli

Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-Links

High-dimensional data are prevalent in various machine learning applications. Feature selection is a useful technique for alleviating the curse of dimensionality. Unsupervised feature selection problem tends to be more challenging than its supervised counterpart due to the lack of class labels. State-of-the-art approaches usually use the concept of pseudo labels to select discriminative features by their regression coefficients and the pseudo-labels derived from clustering is usually inaccurate. In this paper, we propose a new perspective for unsupervised feature selection by Discriminatively Exploiting Similarity (DES). Through forming similar and dissimilar data pairs, implicit discriminative information can be exploited. The similar/dissimilar relationship of data pairs can be used as guidance for feature selection. Based on this idea, we propose hypothesis testing based and classification based methods as instantiations of the DES framework. We evaluate the proposed approaches extensively using six real-world datasets. Experimental results demonstrate that our approaches achieve significantly outperforms the state-of-the-art unsupervised methods. More surprisingly, our unsupervised method even achieves performance comparable to a supervised feature selection method. Code related to this chapter is available at:

Xiaokai Wei, Sihong Xie, Bokai Cao, Philip S. Yu

SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

Corpus-based set expansion (i.e., finding the “complete” set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search.To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.Code related to this chapter is available at: related to this chapter are available at:

Jiaming Shen, Zeqiu Wu, Dongming Lei, Jingbo Shang, Xiang Ren, Jiawei Han

Kernel Methods


Bayesian Nonlinear Support Vector Machines for Big Data

We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.Code related to this chapter is available at: related to this chapter are available at: and

Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft

Entropic Trace Estimates for Log Determinants

The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others. In this work, we estimate log determinants under the framework of maximum entropy, given information in the form of moment constraints from stochastic trace estimation. The estimates demonstrate a significant improvement on state-of-the-art alternative methods, as shown on a wide variety of matrices from the SparseSuite Matrix Collection. By taking the example of a general Markov random field, we also demonstrate how this approach can significantly accelerate inference in large-scale learning methods involving the log determinant.

Jack Fitzsimons, Diego Granziol, Kurt Cutajar, Michael Osborne, Maurizio Filippone, Stephen Roberts

Fair Kernel Learning

New social and economic activities massively exploit big data and machine learning algorithms to do inference on people’s lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the lack of fairness, equity and ethics in machine learning to treat these problems. It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included. Instead, including fairness in the objective function has been shown to be more efficient.We present novel fair regression and dimensionality reduction methods built on a previously proposed fair classification framework. Both methods rely on using the Hilbert Schmidt independence criterion as the fairness term. Unlike previous approaches, this allows us to simplify the problem and to use multiple sensitive variables simultaneously. Replacing the linear formulation by kernel functions allows the methods to deal with nonlinear problems. For both linear and nonlinear formulations the solution reduces to solving simple matrix inversions or generalized eigenvalue problems. This simplifies the evaluation of the solutions for different trade-off values between the predictive error and fairness terms. We illustrate the usefulness of the proposed methods in toy examples, and evaluate their performance on real world datasets to predict income using gender and/or race discrimination as sensitive variables, and contraceptive method prediction under demographic and socio-economic sensitive descriptors.

Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, Gustau Camps-Valls

GaKCo: A Fast Gapped k-mer String Kernel Using Counting

String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size ($$\varSigma $$) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to $$O(\varSigma ^{M})$$. We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger $$\varSigma $$ and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the $$\varSigma ^{M}$$ term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets). (GaKCo is shared as an open source tool at Code and data related to this chapter are available at:

Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi

Graph Enhanced Memory Networks for Sentiment Analysis

Memory networks model information and knowledge as memories that can be manipulated for prediction and reasoning about questions of interest. In many cases, there exists complicated relational structure in the data, by which the memories can be linked together into graphs to propagate information. Typical examples include tree structure of a sentence and knowledge graph in a dialogue system. In this paper, we present a novel graph enhanced memory network GEMN to integrate relational information between memories for prediction and reasoning. Our approach introduces graph attentions to model the relations, and couples them with content-based attentions via an additional neural network layer. It thus can better identify and manipulate the memories related to a given question, and provides more accurate prediction about the final response. We demonstrate the effectiveness of the proposed approach with aspect based sentiment classification. The empirical analysis on real data shows the advantages of incorporating relational dependencies into the memory networks.

Zhao Xu, Romain Vial, Kristian Kersting

Kernel Sequential Monte Carlo

We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance structure and gradients of the target. The emulator’s geometry is adaptively updated and subsequently used to inform local proposals. Unlike in adaptive Markov chain Monte Carlo, continuous adaptation does not compromise convergence of the sampler. KSMC combines the strengths of sequental Monte Carlo and kernel methods: superior performance for multimodal targets and the ability to estimate model evidence as compared to Markov chain Monte Carlo, and the emulator’s ability to represent targets that exhibit high degrees of nonlinearity. As KSMC does not require access to target gradients, it is particularly applicable on targets whose gradients are unknown or prohibitively expensive. We describe necessary tuning details and demonstrate the benefits of the the proposed methodology on a series of challenging synthetic and real-world examples.

Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

Learning Łukasiewicz Logic Fragments by Quadratic Programming

In this paper we provide a framework to embed logical constraints into the classical learning scheme of kernel machines, that gives rise to a learning algorithm based on a quadratic programming problem. In particular, we show that, once the constraints are expressed using a specific fragment from the Łukasiewicz logic, the learning objective turns out to be convex. We formulate the primal and dual forms of a general multi–task learning problem, where the functions to be determined are predicates (of any arity) defined on the feature space. The learning set contains both supervised examples for each predicate and unsupervised examples exploited to enforce the constraints. We give some properties of the solutions constructed by the framework along with promising experimental results.

Francesco Giannini, Michelangelo Diligenti, Marco Gori, Marco Maggini

Nyström Sketches

Despite prolific success, kernel methods become difficult to use in many large scale unsupervised problems because of the evaluation and storage of the full Gram matrix. Here we overcome this difficulty by proposing a novel approach: compute the optimal small, out-of-sample Nyström sketch which allows for fast approximation of the Gram matrix via the Nyström method. We demonstrate and compare several methods for computing the optimal Nyström sketch and show how this approach outperforms previous state-of-the-art Nyström coreset methods of similar size. We further demonstrate how this method can be used in an online setting and explore a simple extension to make the method robust to outliers in the training data.

Daniel J. Perry, Braxton Osting, Ross T. Whitaker

Learning and Optimization


Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks

Representations are fundamental to artificial intelligence. The performance of a learning system depends on how the data is represented. Typically, these representations are hand-engineered using domain knowledge. Recently, the trend is to learn these representations through stochastic gradient descent in multi-layer neural networks, which is called backprop. Learning representations directly from the incoming data stream reduces human labour involved in designing a learning system. More importantly, this allows in scaling up a learning system to difficult tasks. In this paper, we introduce a new incremental learning algorithm called crossprop, that learns incoming weights of hidden units based on the meta-gradient descent approach. This meta-gradient descent approach was previously introduced by Sutton (1992) and Schraudolph (1999) for learning step-sizes. The final update equation introduces an additional memory parameter for each of these weights and generalizes the backprop update equation. From our empirical experiments, we show that crossprop learns and reuses its feature representation while tackling new and unseen tasks whereas backprop relearns a new feature representation.

Vivek Veeriah, Shangtong Zhang, Richard S. Sutton

Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem

Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform stochastic optimization in parallel. Unfortunately, many existing stochastic optimization algorithms cannot be parallelized efficiently. In this paper we show that one can rewrite the regularized risk minimization problem as an equivalent saddle-point problem, and propose an efficient distributed stochastic optimization (DSO) algorithm. We prove the algorithm’s rate of convergence; remarkably, our analysis shows that the algorithm scales almost linearly with the number of processors. We also verify with empirical evaluations that the proposed algorithm is competitive with other parallel, general purpose stochastic and batch optimization algorithms for regularized risk minimization.

Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan

Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves Using Previous Builds

Recent work has shown that the usage of extrapolation of learning curves to determine when to terminate a training build has been shown to be effective in reducing the number of epochs of training required for finding a good performing hyper-parameter configuration. However, the current technique uses the information only from the current build to make the prediction. We propose the usage of a simple regression based extrapolation model that uses the trajectories from previous builds to make predictions of new builds. This can be used to terminate poorly performing builds and thus, speed up hyper-parameter search with performance comparable to non-augmented hyper-parameter optimization techniques. We compare the predictions made by our model against that of the existing extrapolation technique in different tasks. We incorporate our approach into a pre-existing termination criterion. We incorporate this termination criterion into an existing hyper-parameter optimization toolkit. We analyze the performance of our approach and contrast it against a baseline in terms of quality of prediction in three different tasks. We show that our approach yields builds with performance comparable to the non-augmented version with fewer epochs, and outperforms an existing parametric extrapolation technique for two out of three tasks in terms of number of required epochs.

Akshay Chandrashekaran, Ian R. Lane

Thompson Sampling for Optimizing Stochastic Local Search

Stochastic local search (SLS), like many other stochastic optimization algorithms, has several parameters that need to be optimized in order for the algorithm to find high quality solutions within a short amount of time. In this paper, we formulate a stochastic local search bandit ($$\mathtt{SLSB}$$), which is a novel learning variant of SLS based on multi-armed bandits. $$\mathtt{SLSB}$$ optimizes SLS over a sequence of stochastic optimization problems and achieves high average cumulative reward. In $$\mathtt{SLSB}$$, we study how SLS can be optimized via low degree polynomials in its noise and restart parameters. To determine the coefficients of the polynomials, we present polynomial Thompson Sampling ($$\mathtt{PolyTS}$$). We derive a regret bound for $$\mathtt{PolyTS}$$ and validate its performance on synthetic problems of varying difficulty as well as on feature selection problems. Compared to bandits with no assumptions of the reward function and other parameter optimization approaches, our $$\mathtt{PolyTS}$$ assuming polynomial structure can provide substantially better parameter optimization for SLS. In addition, due to its simple model update, $$\mathtt{PolyTS}$$ has low computational cost compared to other SLS parameter optimization methods.

Tong Yu, Branislav Kveton, Ole J. Mengshoel

Matrix and Tensor Factorization


Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real-world datasets. Furthermore, we extend the models with the Bayesian automatic relevance determination prior, allowing the models to perform automatic model selection, and demonstrate its efficiency. Code and data related to this chapter are availabe at:

Thomas Brouwer, Jes Frellsen, Pietro Lió

Content-Based Social Recommendation with Poisson Matrix Factorization

We introduce Poisson Matrix Factorization with Content and Social trust information (PoissonMF-CS), a latent variable probabilistic model for recommender systems with the objective of jointly modeling social trust, item content and user’s preference using Poisson matrix factorization framework. This probabilistic model is equivalent to collectively factorizing a non-negative user–item interaction matrix and a non-negative item–content matrix. The user–item matrix consists of sparse implicit (or explicit) interactions counts between user and item, and the item–content matrix consists of words or tags counts per item. The model imposes additional constraints given by the social ties between users, and the homophily effect on social networks – the tendency of people with similar preferences to be socially connected. Using this model we can account for and fine-tune the weight of content-based and social-based factors in the user preference. We develop approximate variational inference algorithm and perform experiments comparing PoissonMF-CS with competing models. The experimental evaluation indicates that PoissonMF-CS achieves superior predictive performance on held-out data for the top-M recommendations task. Also, we observe that PoissonMF-CS generates compact latent representations when compared with alternative models while maintaining superior predictive performance.Code related to this chapter is available at: related to this chapter are available at:

Eliezer de Souza da Silva, Helge Langseth, Heri Ramampiaro

C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization

Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes. While existing works focus on rank-one factorizations which are either specific or common to the classes, we derive class-specific alterations from common factorizations as well. Therewith, we broaden the applicability of our new method to datasets whose class-dependencies have a more complex structure. On the basis of synthetic and real-world datasets, we show on the one hand that our method is able to filter structure which corresponds to our model assumption, and on the other hand that our model assumption is justified in real-world application. Our method is parameter-free. Code and data related to this chapter are available at:

Sibylle Hess, Katharina Morik

Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition

Extracting features from incomplete tensors is a challenging task which is not well explored. Due to the data with missing entries, existing feature extraction methods are not applicable. Although tensor completion techniques can estimate the missing entries well, they focus on data recovery and do not consider the relationships among tensor samples for effective feature extraction. To solve this problem of feature extraction for incomplete data, we propose an unsupervised method, TDVM, which incorporates low-rankTuckerDecomposition with featureVarianceMaximization in a unified framework. Based on Tucker decomposition, we impose nuclear norm regularization on the core tensors while minimizing reconstruction errors, and meanwhile maximize the variance of core tensors (i.e., extracted features). Here, the relationships among tensor samples are explored via variance maximization while estimating the missing entries. We thus can simultaneously obtain lower-dimensional core tensors and informative features directly from observed entries. The alternating direction method of multipliers approach is utilized to solve the optimization objective. We evaluate the features extracted from two real data with different missing entries for face recognition tasks. Experimental results illustrate the superior performance of our method with a significant improvement over the state-of-the-art methods.

Qiquan Shi, Yiu-ming Cheung, Qibin Zhao

Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries

Understanding spatio-temporal activities in a city is a typical problem of spatio-temporal data analysis. For this analysis, tensor factorization methods have been widely applied for extracting a few essential patterns into latent factors. Non-negative Tensor Factorization (NTF) is popular because of its capability of learning interpretable factors from non-negative data, simple computation procedures, and dealing with missing observation. However, since existing NTF methods are not fully aware of spatial and temporal dependencies, they often fall short of learning latent factors where a large portion of missing observation exist in data. In this paper, we present a novel NTF method for extracting smooth and flat latent factors by leveraging various kinds of spatial and temporal structures. Our method incorporates a unified structured regularizer into NTF that can represent various kinds of auxiliary information, such as an order of timestamps, a daily and weekly periodicity, distances between sensor locations, and areas of locations. For the estimation of the factors for our model, we present a simple and efficient optimization procedure based on the alternating direction method of multipliers. In missing value interpolation experiments of traffic flow data and bike-sharing system data, we demonstrate that our proposed method improved interpolation performances from existing NTF, especially when a large portion of missing values exists.

Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata

Networks and Graphs


Attributed Graph Clustering with Unimodal Normalized Cut

Graph vertices are often associated with attributes. For example, in addition to their connection relations, people in friendship networks have personal attributes, such as interests, age, and residence. Such graphs (networks) are called attributed graphs. The detection of clusters in attributed graphs is of great practical relevance, e.g., targeting ads. Attributes and edges often provide complementary information. The effective use of both types of information promises meaningful results. In this work, we propose a method called UNCut (for Unimodal Normalized Cut) to detect cohesive clusters in attributed graphs. A cohesive cluster is a subgraph that has densely connected edges and has as many homogeneous (unimodal) attributes as possible. We adopt the normalized cut to assess the density of edges in a graph cluster. To evaluate the unimodality of attributes, we propose a measure called unimodality compactness which exploits Hartigans’ dip test. Our method UNCut integrates the normalized cut and unimodality compactness in one framework such that the detected clusters have low normalized cut and unimodality compactness values. Extensive experiments on various synthetic and real-world data verify the effectiveness and efficiency of our method UNCut compared with state-of-the-art approaches. Code and data related to this chapter are available at:

Wei Ye, Linfei Zhou, Xin Sun, Claudia Plant, Christian Böhm

K-Clique-Graphs for Dense Subgraph Discovery

Finding dense subgraphs in a graph is a fundamental graph mining task, with applications in several fields. Algorithms for identifying dense subgraphs are used in biology, in finance, in spam detection, etc. Standard formulations of this problem such as the problem of finding the maximum clique of a graph are hard to solve. However, some tractable formulations of the problem have also been proposed, focusing mainly on optimizing some density function, such as the degree density and the triangle density. However, maximization of degree density usually leads to large subgraphs with small density, while maximization of triangle density does not necessarily lead to subgraphs that are close to being cliques.In this paper, we introduce the k-clique-graph densest subgraph problem, $$k \ge 3$$, a novel formulation for the discovery of dense subgraphs. Given an input graph, its k-clique-graph is a new graph created from the input graph where each vertex of the new graph corresponds to a k-clique of the input graph and two vertices are connected with an edge if they share a common $$k - 1$$-clique. We define a simple density function, the k-clique-graph density, which gives compact and at the same time dense subgraphs, and we project its resulting subgraphs back to the input graph. In this paper, we focus on the triangle-graph densest subgraph problem obtained for $$k = 3$$. To optimize the proposed function, we provide an exact algorithm. Furthermore, we present an efficient greedy approximation algorithm that scales well to larger graphs.We evaluate the proposed algorithms on real datasets and compare them with other algorithms in terms of the size and the density of the extracted subgraphs. The results verify the ability of the proposed algorithms in finding high-quality subgraphs in terms of size and density. Finally, we apply the proposed method to the important problem of keyword extraction from textual documents. Code related to this chapter is available at:

Giannis Nikolentzos, Polykarpos Meladianos, Yannis Stavrakas, Michalis Vazirgiannis

Learning and Scaling Directed Networks via Graph Embedding

Reliable evaluation of network mining tools implies significance and scalability testing. This is usually achieved by picking several graphs of various size from different domains. However, graph properties and thus evaluation results could be dramatically different from one domain to another. Hence the necessity of aggregating results over a multitude of graphs within each domain.The paper introduces an approach to automatically learn features of a directed graph from any domain and generate similar graphs while scaling input graph size with a real-valued factor. Generating multiple graphs with similar size allows significance testing, while scaling graph size makes scalability evaluation possible. The proposed method relies on embedding an input graph into low-dimensional space, thus encoding graph features in a set of node vectors. Edge weights and node communities could be imitated as well in optional steps.We demonstrate that embedding-based approach ensures variability of synthetic graphs while keeping degree and subgraphs distributions close to the original graphs. Therefore, the method could make significance and scalability testing of network algorithms more reliable without the need to collect additional data. We also show that embedding-based approach preserves various features in generated graphs which can’t be achieved by other generators imitating a given graph.

Mikhail Drobyshevskiy, Anton Korshunov, Denis Turdakov

Local Lanczos Spectral Approximation for Community Detection

We propose a novel approach called the Local Lanczos Spectral Approximation (LLSA) for identifying all latent members of a local community from very few seed members. To reduce the computation complexity, we first apply a fast heat kernel diffusing to sample a comparatively small subgraph covering almost all possible community members around the seeds. Then starting from a normalized indicator vector of the seeds and by a few steps of Lanczos iteration on the sampled subgraph, a local eigenvector is gained for approximating the eigenvector of the transition matrix with the largest eigenvalue. Elements of this local eigenvector is a relaxed indicator for the affiliation probability of the corresponding nodes to the target community. We conduct extensive experiments on real-world datasets in various domains as well as synthetic datasets. Results show that the proposed method outperforms state-of-the-art local community detection algorithms. To the best of our knowledge, this is the first work to adapt the Lanczos method for local community detection, which is natural and potentially effective. Also, we did the first attempt of using heat kernel as a sampling method instead of detecting communities directly, which is proved empirically to be very efficient and effective.

Pan Shi, Kun He, David Bindel, John E. Hopcroft

Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms

Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In this paper, we consider the problem of regularizing the training of neural knowledge graph embeddings by leveraging external background knowledge. We propose a principled and scalable method for leveraging equivalence and inversion axioms during the learning process, by imposing a set of model-dependent soft constraints on the predicate embeddings. The method has several advantages: (i) the number of introduced constraints does not depend on the number of entities in the knowledge base; (ii) regularities in the embedding space effectively reflect available background knowledge; (iii) it yields more accurate results in link prediction tasks over non-regularized methods; and (iv) it can be adapted to a variety of models, without affecting their scalability properties. We demonstrate the effectiveness of the proposed method on several large knowledge graphs. Our evaluation shows that it consistently improves the predictive accuracy of several neural knowledge graph embedding models (for instance, the MRR of TransE on WordNet increases by 11%) without compromising their scalability properties.

Pasquale Minervini, Luca Costabello, Emir Muñoz, Vít Nováček, Pierre-Yves Vandenbussche

Survival Factorization on Diffusion Networks

In this paper we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which encodes: (a) the relevance of a information cascade on a topic; (b) the topical authoritativeness and the susceptibility of each individual involved in the information cascade, and (c) temporal topical patterns. By exploiting the cumulative properties of the survival function and of the likelihood of the model on a given adoption log, which records the observed activation times of users and side-information for each cascade, we show that the inference phase is linear in the number of users and in the number of adoptions. The evaluation on both synthetic and real-world data shows the effectiveness of the model in detecting the interplay between topics and social influence patterns, which ultimately provides high accuracy in predicting users activation times. Code and data related to this chapter are available at:

Nicola Barbieri, Giuseppe Manco, Ettore Ritacco

The Network-Untangling Problem: From Interactions to Activity Timelines

In this paper we study a problem of determining when entities are active based on their interactions with each other. More formally, we consider a set of entities V and a sequence of time-stamped edges E among the entities. Each edge $$(u,v,t)\in E$$ denotes an interaction between entities u and v that takes place at time t. We view this input as a temporal network. We then assume a simple activity model in which each entity is active during a short time interval. An interaction (u, v, t) can be explained if at least one of u or v are active at time t. Our goal is to reconstruct the activity intervals, for all entities in the network, so as to explain the observed interactions. This problem, which we refer to as the network-untangling problem, can be applied to discover timelines of events from complex interactions among entities.We provide two formulations for the network-untangling problem: (i) minimizing the total interval length over all entities, and (ii) minimizing the maximum interval length. We show that the sum problem is NP-hard, while, surprisingly, the max problem can be solved optimally in linear time, using a mapping to 2-SAT. For the sum problem we provide efficient and effective algorithms based on realistic assumptions. Furthermore, we complement our study with an evaluation on synthetic and real-world datasets, which demonstrates the validity of our concepts and the good performance of our algorithms.

Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis

TransT: Type-Based Multiple Embedding Representations for Knowledge Graph Completion

Knowledge graph completion with representation learning predicts new entity-relation triples from the existing knowledge graphs by embedding entities and relations into a vector space. Most existing methods focus on the structured information of triples and maximize the likelihood of them. However, they neglect semantic information contained in most knowledge graphs and the prior knowledge indicated by the semantic information. To overcome this drawback, we propose an approach that integrates the structured information and entity types which describe the categories of entities. Our approach constructs relation types from entity types and utilizes type-based semantic similarity of the related entities and relations to capture prior distributions of entities and relations. With the type-based prior distributions, our approach generates multiple embedding representations of each entity in different contexts and estimates the posterior probability of entity and relation prediction. Extensive experiments show that our approach outperforms previous semantics-based methods. The source code of this paper can be obtained from

Shiheng Ma, Jianhui Ding, Weijia Jia, Kun Wang, Minyi Guo

Neural Networks and Deep Learning


A Network Architecture for Multi-Multi-Instance Learning

We study an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as graph classification, image classification and translation-invariant pooling in convolutional neural network. In order to learn multi-multi instance data, we introduce a special neural network layer, called bag-layer, whose units aggregate sets of inputs of arbitrary size. We prove that the associated class of functions contains all Boolean functions over sets of sets of instances. We present empirical results on semi-synthetic data showing that such class of functions can be actually learned from data. We also present experiments on citation graphs datasets where our model obtains competitive results. Code and data related to this chapter are available at:

Alessandro Tibo, Paolo Frasconi, Manfred Jaeger

Con-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec

We present a novel approach to learn distributed representation of sentences from unlabeled data by modeling both content and context of a sentence. The content model learns sentence representation by predicting its words. On the other hand, the context model comprises a neighbor prediction component and a regularizer to model distributional and proximity hypotheses, respectively. We propose an online algorithm to train the model components jointly. We evaluate the models in a setup, where contextual information is available. The experimental results on tasks involving classification, clustering, and ranking of sentences show that our model outperforms the best existing models by a wide margin across multiple datasets.Code related to this chapter is available at: related to this chapter are available at:

Tanay Kumar Saha, Shafiq Joty, Mohammad Al Hasan

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings.

Shin Ando, Chun Yuan Huang

FCNN: Fourier Convolutional Neural Networks

The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.

Harry Pratt, Bryan Williams, Frans Coenen, Yalin Zheng

Joint User Modeling Across Aligned Heterogeneous Sites Using Neural Networks

The quality of user modeling is crucial for personalized recommender systems. Traditional within-site recommender systems aim at modeling user preferences using only actions within target site, thus suffer from cold-start problem. To alleviate such problem, researchers propose cross-domain models to leverage user actions from other domains within same site. Joint user modeling is later proposed to further integrate user actions from aligned sites for data enrichment. However, there are still limitations in existing works regarding the modeling of heterogeneous actions, the requirement of full alignment and the design of preferences coupling. To tackle these, we propose JUN: a joint user modeling framework using neural network. We take advantage of neural network’s capability of capturing different data types and its ability for mining high-level non-linear correlations. Specifically, in additional to site-specific preferences models, we further introduce an auxiliary neural network to transfer knowledge between sites by fine-tuning the user embeddings using alignment information. We adopt JUN for item-based and text-based site to demonstrate its performance. Experimental results indicate that JUN outperforms both within-site and cross-site models. Specifically, JUN achieves relative improvement of 2.96% and 2.37% for item-based and text-based sites (5.77% and 13.54% for cold-start scenarios). Besides performance gain, JUN also achieves great generality and significantly extends the use scenarios.

Xuezhi Cao, Yong Yu

Sequence Generation with Target Attention

Source-target attention mechanism (briefly, source attention) has become one of the key components in a wide range of sequence generation tasks, such as neural machine translation, image caption, and open-domain dialogue generation. In these tasks, the attention mechanism, typically in control of information flow from the encoder to the decoder, enables to generate every component in the target sequence relying on different source components. While source attention mechanism has attracted many research interests, few of them turn eyes to if the generation of target sequence can additionally benefit from attending back to itself, which however is intuitively motivated by the nature of attention. To investigate the question, in this paper, we propose a new target-target attention mechanism (briefly, target attention). Along the progress of generating target sequence, target attention mechanism takes into account the relationship between the component to generate and its preceding context within the target sequence, such that it can better keep the coherent consistency and improve the readability of the generated sequence. Furthermore, it complements the information from source attention so as to further enhance semantic adequacy. After designing an effective approach to incorporate target attention in encoder-decoder framework, we conduct extensive experiments on both neural machine translation and image caption. Experimental results clearly demonstrate the effectiveness of our design of integrating both source and target attention for sequence generation tasks.

Yingce Xia, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu

Wikipedia Vandal Early Detection: From User Behavior to User Embedding

Wikipedia is the largest online encyclopedia that allows anyone to edit articles. In this paper, we propose the use of deep learning to detect vandals based on their edit history. In particular, we develop a multi-source long-short term memory network (M-LSTM) to model user behaviors by using a variety of user edit aspects as inputs, including the history of edit reversion information, edit page titles and categories. With M-LSTM, we can encode each user into a low dimensional real vector, called user embedding. Meanwhile, as a sequential model, M-LSTM updates the user embedding each time after the user commits a new edit. Thus, we can predict whether a user is benign or vandal dynamically based on the up-to-date user embedding. Furthermore, those user embeddings are crucial to discover collaborative vandals. Code and data related to this chapter are available at:

Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang


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