waashk/dblp huggingface dataset:
large-scale behavioral targeting. behavioral targeting (bt) leverages historical user behavior to select the ads most relevant to users to display. the state-of-the-art of bt derives a linear poisson regression model from fine-grained user behavioral data and predicts click-through rate (ctr) from user history. we designed and implemented a highly scalable and efficient solution to bt using hadoop mapreduce framework. with our parallel algorithm and the resulting system, we can build above 450 bt-category models from the entire yahoo's user base within one day, the scale that one can not even imagine with prior systems. moreover, our approach has yielded 20% ctr lift over the existing production system by leveraging the well-grounded probabilistic model fitted from a much larger training dataset. specifically, our major contributions include: (1) a mapreduce statistical learning algorithm and implementation that achieve optimal data parallelism, task parallelism, and load balance in spite of the typically skewed distribution of domain data. (2) an in-place feature vector generation algorithm with linear time complexity o(n) regardless of the granularity of sliding target window. (3) an in-memory caching scheme that significantly reduces the number of disk ios to make large-scale learning practical. (4) highly efficient data structures and sparse representations of models and data to enable fast model updates. we believe that our work makes significant contributions to solving large-scale machine learning problems of industrial relevance in general. finally, we report comprehensive experimental results, using industrial proprietary codebase and datasets.
multi-focal learning and its application to customer service support. in this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. the multi-focal learning problem is motivated by numerous real-world learning applications. for instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. the experienced customers usually give more precise and focused descriptions about the problem. in contrast, the inexperienced customers usually provide more diverse descriptions. in this case, the examples from the same class in the training data can be naturally in different focal groups. as a result, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. the key developmental challenge is how to identify those focal groups in the training data. as a case study, we exploit multi-focal learning for profiling problems in customer service centers. the results show that multifocal learning can significantly boost the learning accuracies of existing learning algorithms, such as support vector machines (svms), for classifying customer problems.
learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology. for a training dataset with a nonexhaustive list of classes, i.e. some classes are not yet known and hence are not represented, the resulting learning problem is ill-defined. in this case a sample from a missing class is incorrectly classified to one of the existing classes. for some applications the cost of misclassifying a sample could be negligible. however, the significance of this problem can better be acknowledged when the potentially undesirable consequences of incorrectly classifying a food pathogen as a nonpathogen are considered. our research is directed towards the real-time detection of food pathogens using optical-scattering technology. bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. these spectral signatures contain descriptive characteristics of bacterial colonies, which can be used to identify bacteria cultures in real time. one bottleneck that remains to be addressed is the nonexhaustive nature of the training library. it is very difficult if not impractical to collect samples from all possible bacteria colonies and construct a digital library with an exhaustive set of scatter signatures. this study deals with the real-time detection of samples from a missing class and the associated problem of learning with a nonexhaustive training dataset. our proposed method assumes a common prior for the set of all classes, known and missing. the parameters of the prior are estimated from the samples of the known classes. this prior is then used to generate a large number of samples to simulate the space of missing classes. finally a bayesian maximum likelihood classifier is implemented using samples from real as well as simulated classes. experiments performed with samples collected for 28 bacteria subclasses favor the proposed approach over the state of the art.
combining link and content for community detection: a discriminative approach. in this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and word wide web. most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. these generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. to explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. first, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. these two models are unified seamlessly via the community memberships. we present efficient algorithms to solve the related optimization problems based on bound optimization and alternating projection. extensive experiments with benchmark data sets show that the proposed framework significantly outperforms the state-of-the-art approaches for combining link and content analysis for community detection.
scalable pseudo-likelihood estimation in hybrid random fields. learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. in many interesting applications, the domain dimensionality is such as to prevent state-of-the-art statistical learning techniques from delivering accurate models in reasonable time. this paper presents a hybrid random field model for pseudo-likelihood estimation in high-dimensional domains. a theoretical analysis proves that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by bayesian networks. in order to learn hybrid random fields from data, we develop the markov blanket merging algorithm. theoretical and experimental evidence shows that markov blanket merging scales up very well to high-dimensional datasets. as compared to other widely used statistical learning techniques, markov blanket merging delivers accurate results in a number of link prediction tasks, while achieving also significant improvements in terms of computational efficiency. our software implementation of the models investigated in this paper is publicly available at http://www.dii.unisi.it/~freno/. the same website also hosts the datasets used in this work that are not available elsewhere in the same preprocessing used for our experiments.
detection of unique temporal segments by information theoretic meta-clustering. the central challenge in temporal data analysis is to obtain knowledge about its underlying dynamics. in this paper, we address the observation of noisy, stochastic processes and attempt to detect temporal segments that are related to inconsistencies and irregularities in its dynamics. many conventional anomaly detection approaches detect anomalies based on the distance between patterns, and often provide only limited intuition about the generative process of the anomalies. meanwhile, model-based approaches have difficulty in identifying a small, clustered set of anomalies. we propose information-theoretic meta-clustering (itmc), a formalization of model-based clustering principled by the theory of lossy data compression. itmc identifies a 'unique' cluster whose distribution diverges significantly from the entire dataset. furthermore, itmc employs a regularization term derived from the preference for high compression rate, which is critical to the precision of detection. for empirical evaluation, we apply itmc to two temporal anomaly detection tasks. datasets are taken from generative processes involving heterogeneous and inconsistent dynamics. a comparison to baseline methods shows that the proposed algorithm detects segments from irregular states with significantly high precision and recall.
learning, indexing, and diagnosing network faults. modern communication networks generate massive volume of operational event data, e.g., alarm, alert, and metrics, which can be used by a network management system (nms) to diagnose potential faults. in this work, we introduce a new class of indexable fault signatures that encode temporal evolution of events generated by a network fault as well as topological relationships among the nodes where these events occur. we present an efficient learning algorithm to extract such fault signatures from noisy historical event data, and with the help of novel space-time indexing structures, we show how to perform efficient, online signature matching. we provide results from extensive experimental studies to explore the efficacy of our approach and point out potential applications of such signatures for many different types of networks including social and information networks.
cartesian contour: a concise representation for a collection of frequent sets. in this paper, we consider a novel scheme referred to as cartesian contour to concisely represent the collection of frequent itemsets. different from the existing works, this scheme provides a complete view of these itemsets by covering the entire collection of them. more interestingly, it takes a first step in deriving a generative view of the frequent pattern formulation, i.e., how a small number of patterns interact with each other and produce the complexity of frequent itemsets. we perform a theoretical investigation of the concise representation problem and link it to the biclique set cover problem and prove its np-hardness. we develop a novel approach utilizing the technique developed in frequent itemset mining, set cover, and max k-cover to approximate the minimal biclique set cover problem. in addition, we consider several heuristic techniques to speedup the construction of cartesian contour. the detailed experimental study demonstrates the effectiveness and efficiency of our approach.
mining broad latent query aspects from search sessions. search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. a broad latent query aspect is a set of keywords that succinctly represents one particular sense, or one particular information need, that can aid users in reformulating such queries. we extract such broad latent aspects from query reformulations found in historical search session logs. we propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects the system can store, and the number of aspects that can be shown in response to any given query. we present algorithms to find a good set of aspects, and also to pick the best k aspects matching any query. empirical results on real-world search engine logs show significant gains over a strong baseline that uses single-keyword reformulations: a gain of 14% and 23% in terms of human-judged accuracy and click-through data respectively, and around 20% in terms of consistency among aspects predicted for "similar" queries. this demonstrates both the importance of broad query aspects, and the efficacy of our algorithms for extracting them.
name-ethnicity classification from open sources. the problem of ethnicity identification from names has a variety of important applications, including biomedical research, demographic studies, and marketing. here we report on the development of an ethnicity classifier where all training data is extracted from public, non-confidential (and hence somewhat unreliable) sources. our classifier uses hidden markov models (hmms) and decision trees to classify names into 13 cultural/ethnic groups with individual group accuracy comparable accuracy to earlier binary (e.g., spanish/non-spanish) classifiers. we have applied this classifier to over 20 million names from a large-scale news corpus, identifying interesting temporal and spatial trends on the representation of particular cultural/ethnic groups.
grouped graphical granger modeling methods for temporal causal modeling. we develop and evaluate an approach to causal modeling based on time series data, collectively referred to as "grouped graphical granger modeling methods." graphical granger modeling uses graphical modeling techniques on time series data and invokes the notion of "granger causality" to make assertions on causality among a potentially large number of time series variables through inference on time-lagged effects. the present paper proposes a novel enhancement to the graphical granger methodology by developing and applying families of regression methods that are sensitive to group information among variables, to leverage the group structure present in the lagged temporal variables according to the time series they belong to. additionally, we propose a new family of algorithms we call group boosting, as an improved component of grouped graphical granger modeling over the existing regression methods with grouped variable selection in the literature (e.g group lasso). the introduction of group boosting methods is primarily motivated by the need to deal with non-linearity in the data. we perform empirical evaluation to confirm the advantage of the grouped graphical granger methods over the standard (non-grouped) methods, as well as that specific to the methods based on group boosting. this advantage is also demonstrated for the real world application of gene regulatory network discovery from time-course microarray data.
structured correspondence topic models for mining captioned figures in biological literature. a major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly provide images and other graphical illustrations of key experimental results and other scientific contents. in biological articles, a typical figure often comprises multiple panels, accompanied by either scoped or global captioned text. moreover, the text in the caption contains important semantic entities such as protein names, gene ontology, tissues labels, etc., relevant to the images in the figure. due to the avalanche of biological literature in recent years, and increasing popularity of various bio-imaging techniques, automatic retrieval and summarization of biological information from literature figures has emerged as a major unsolved challenge in computational knowledge extraction and management in the life science. we present a new structured probabilistic topic model built on a realistic figure generation scheme to model the structurally annotated biological figures, and we derive an efficient inference algorithm based on collapsed gibbs sampling for information retrieval and visualization. the resulting program constitutes one of the key ir engines in our slif system that has recently entered the final round (4 out 70 competing systems) of the elsevier grand challenge on knowledge enhancement in the life science. here we present various evaluations on a number of data mining tasks to illustrate our method.
toward autonomic grids: analyzing the job flow with affinity streaming. the affinity propagation (ap) clustering algorithm proposed by frey and dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic computational complexity. this paper, motivated by autonomic computing, extends ap to the data streaming framework. firstly a hierarchical strategy is used to reduce the complexity to o(n1+ε); the distortion loss incurred is analyzed in relation with the dimension of the data items. secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. the presented approach strap is applied to the stream of jobs submitted to the egee grid, providing an understandable description of the job flow and enabling the system administrator to spot online some sources of failures.
mining web logs: applications and challenges. web logs record the primary interaction of users with web pages in general and search engines in particular. there are two sources for such logs: user trails obtained from toolbars and query/click information obtained from search engines. in this talk we will address the task of mining this rich data to improve user experience on the web. we will illustrate a few applications, together with the modeling and algorithmic challenges that stem from these applications. we will also discuss the privacy issues that arise in this context.
mining social networks for personalized email prioritization. email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. a good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. however, research on statistical learning methods for fully personalized email prioritization (pep) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. it is therefore important to develop and evaluate pep methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. this paper presents the first study (to the best of our knowledge) under such an assumption. specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user. we also developed a novel semi-supervised (transductive) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network. these methods together enable us to obtain an enriched vector representation of each new email message, which consists of both standard features of an email message (such as words in the title or body, sender and receiver ids, etc.) and the induced social features from the sender and receivers of the message. using the enriched vector representation as the input in svm classifiers to predict the importance level for each test message, we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection. we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection: the relative error reduction in mae was 31% in micro-averaging, and 14% in macro-averaging.
effective multi-label active learning for text classification. labeling text data is quite time-consuming but essential for automatic text classification. especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. to minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with support vector machines (svm). an effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods.
coco: coding cost for parameter-free outlier detection. how can we automatically spot all outstanding observations in a data set? this question arises in a large variety of applications, e.g. in economy, biology and medicine. existing approaches to outlier detection suffer from one or more of the following drawbacks: the results of many methods strongly depend on suitable parameter settings being very difficult to estimate without background knowledge on the data, e.g. the minimum cluster size or the number of desired outliers. many methods implicitly assume gaussian or uniformly distributed data, and/or their result is difficult to interpret. to cope with these problems, we propose coco, a technique for parameter-free outlier detection. the basic idea of our technique relates outlier detection to data compression: outliers are objects which can not be effectively compressed given the data set. to avoid the assumption of a certain data distribution, coco relies on a very general data model combining the exponential power distribution with independent components. we define an intuitive outlier factor based on the principle of the minimum description length together with an novel algorithm for outlier detection. an extensive experimental evaluation on synthetic and real world data demonstrates the benefits of our technique. availability: the source code of coco and the data sets used in the experiments are available at: http://www.dbs.ifi.lmu.de/forschung/kdd/boehm/coco.