Question: A weak supervision method for extracting computer security incidents from Twitter posts.

This article offers an in-depth exploration of Generalized Expectation (GE) criteria, a method that stands out for its simplicity, robustness, and scalability in the realm of semi-supervised learning, particularly when dealing with data that is weakly labeled. GE optimizes model parameters by prioritizing those that align closely with specific expectation constraints, such as the marginal distributions of labels, on the dataset lacking labels. The paper illustrates how to apply these generalized expectation criteria to two categories of parametric models: maximum entropy models and conditional random fields. Through experimental outcomes, it is shown that GE criteria lead to accuracy enhancements over supervised training methods and surpass several other cutting-edge semi-supervised learning techniques for these models.

**Question**: The Impact of Social Media on Mental Health Among Adolescents

**Documents**: The Role of Social Media in the Development of Mental Health Disorders Among Adolescents
This study explores the relationship between social media usage and the development of mental health disorders among adolescents. The findings indicate that excessive use of social media is associated with increased risk of depression, anxiety, and other mental health issues. The study also highlights the role of cyberbullying, comparison with peers, and sleep disruption as key factors contributing to this relationship.

**Similar Documents**: This research delves into the connection between the frequency of social media engagement and the emergence of mental health disorders in the adolescent population. Results show a correlation between high levels of social media consumption and a heightened likelihood of depression, anxiety, and various mental health challenges. The research underscores the significance of cyberbullying, peer comparison, and disrupted sleep patterns as pivotal elements exacerbating this association.

A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA

Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning. We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases. We present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.

It is becoming increasingly difficult to ignore the importance of using online social networks (OSNs) for various purposes such as marketing, education, entertainment, and business. However, OSNs also open the door for harmful activities and behaviors. Committing financial fraud and propagating malware and spam advertisements are very common criminal actions that people engage in by accessing uniform resource locators (URLs). It has been reported that advanced attackers tend to exploit human flaws rather than system flaws, thus, users are targeted in social media threats by hour. This research aims to understand the state of literature on detecting malicious URLs in OSNs, with a focus on two major aspects: URL and OSN objects. Although the literature presents these two aspects in a different context, this paper will mainly focus on their features in relation to malicious URL detection using classification methods. We firstly review three features of URLs: lexical features, hosts, and domains, then we discuss their limitations. We then introduce social spam analytics and detection models using combined features from both URLs and OSNs, particularly the use of user profiles and posts together with URL features, to enhance the detection of malicious behavior. This combination can help in understanding the interests of the user either explicitly, by stating choices in the profile, or implicitly, by analyzing the post behavior, as the spammers do not maintain a regular interest and tend to exploit events or top trend topics.

Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.

In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content.
 The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100.
 In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.

There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.

Widespread use of social media during crises has become commonplace, as shown by the volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011. Location mentions are particularly important in disaster messages as they can show emergency responders where problems have occurred. This article explores the sorts of locations that occur in disaster-related social messages, how well off-theshelf software identifies those locations, and what is needed to improve automated location identification, called geo-parsing. To do this, we have sampled Twitter messages from the February 2011 earthquake in Christchurch, Canterbury, New Zealand. We annotated locations in messages manually to make a gold standard by which to measure locations identified by a Named Entity Recognition software. The Stanford NER software found some locations that were proper nouns, but did not identify locations that were not capitalized, local streets and buildings, or nonstandard place abbreviations and mis-spellings that are plentiful in microtext. We review how these problems might be solved in software research, and model a readable crisis map that shows crisis location clusters via enlarged place labels.

Social network sites are becoming great through millions of users and information have been gathered from the users. This information have equal advantages to their friends and spammers. Twitter is one of the most popular social networks that, users can send short textual messages namely tweet. Researches have shown that this network is subject to spammer's invasion more than other social networks and more than six percent of its tweets are spam. So diagnose of the spam tweets is very important. Firstly, in this research, we determine various features for spam detection and then by using a clustering algorithm based on the data stream, we identify spam tweets. previous works in the field of spam tweets were done by classification algorithms. it is the first time that for spam tweets detection, an data stream clustering algorithm is used. Den stream Algorithm can cluster tweets and consider Outliers as spam. Results show, when this algorithm is set properly the amount of accuracy and precision of spam tweets detection will improve and false positive rate will reach to the minimum value in comparison with previous works.

As computers and information technologies become ubiquitous throughout society, the security of our networks and information technologies is a growing concern. As a result, many researchers have become interested in the security domain. Among them, there is growing interest in observing hacker communities for early detection of developing security threats and trends. Research in this area has often reported hackers openly sharing cybercriminal assets and knowledge with one another. In particular, the sharing of raw malware source code files has been documented in past work. Unfortunately, malware code documentation appears often times to be missing, incomplete, or written in a language foreign to researchers. Thus, analysis of such source files embedded within hacker communities has been limited. Here we utilize a subset of popular machine learning methodologies for the automated analysis of malware source code files. Specifically, we explore genetic algorithms to resolve questions related to feature selection within the context of malware analysis. Next, we utilize two common classification algorithms to test selected features for identification of malware attack vectors. Results suggest promising direction in utilizing such techniques to help with the automated analysis of malware source code.

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.


Question: Learning the structure of graphical models by utilizing L1-regularization paths.

This paper introduces a Bayesian framework for the discovery of network structure in multivariate domains, focusing on understanding the dependency relationships between variables. In domains where the dependency structure of the underlying distribution is crucial, Bayesian network models provide a powerful tool for representation. When analyzing a dataset, traditional Bayesian model selection aims to identify the single most probable (MAP) model to infer these relationships. However, in cases where the dataset is limited, multiple models may possess substantial posterior probability, necessitating a more nuanced approach.

To address this, we propose a novel method for computing the Bayesian posterior of a feature, which involves calculating the total posterior probability of all models that incorporate this feature. Our method begins by efficiently computing a sum over the vast number of networks that are consistent with a predetermined order of network variables. This computation enables us to determine, for a given order, both the marginal probability of the observed data and the posterior probability of a feature.

Building upon this foundation, we develop an algorithm that approximates the Bayesian posterior of a feature using a Markov Chain Monte Carlo (MCMC) method. Unlike conventional MCMC approaches that operate over network structures, our method operates over orders of variables. This approach is advantageous because the space of orders is smaller, more regular, and exhibits a smoother posterior probability landscape compared to the space of network structures. We present empirical results on both synthetic and real-world datasets that compare our method to full model averaging (where feasible), MCMC over network structures, and a non-Bayesian bootstrap technique, showcasing its effectiveness and reliability in structure discovery within Bayesian networks.

We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Lévy processes can be used to generate infinite-dimensional versions of standard normal scale-mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.

We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques.

(Abstract) Counting the number of paths in a graph, for example the number of nonintersecting (or self-avoiding) rook paths joining opposite corners of an n × n grid, is not an easy problem since no mathematical formula is found and the number becomes too large to enumerate one by one except for small graphs. We have implemented software based on the ZDD technique in Knuth's " The Art of Computer Programming " carefully so as to achieve better memory efficiency, and have succeeded in computing the exact numbers for some graphs that were not known until now.

By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progress toward convergence. This result motivates methods for optimizing algorithmic parameters and discarding irrelevant variables as iterations progress. Applied to `1-regularized learning, BLITZ convincingly outperforms existing solvers in sequential, limited-memory, and distributed settings. BLITZ is not specific to `1-regularized learning, making the algorithm relevant to many applications involving sparsity or constraints.

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem and arises in many important applications as in the task of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices {Xk ,Y k}, and at each step mainly performs a soft-thresholding operation on the singular values of the matrix Y k. There are two remarkable features making this attractive for low-rank matrix completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {Xk} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal storage space and keep the computational cost of each iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence of iterates converges. On the practical side, we provide numerical examples in which 1, 000 × 1, 000 matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate that our approach is amenable to very large scale problems by recovering matrices of rank about 10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are connected with the recent literature on linearized Bregman iterations for 1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.

In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance.

A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(/m), where land mare the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

Several languages, such as LOREL and UnQL, support querying of semi-structured data. Others, such as WebSQL and WebLog, query Web sites. All these languages model data as labeled graphs and use regular path expressions to express queries that traverse arbitrary paths in graphs. Naive execution of path expressions is ineecient, however, because it often requires exhaustive graph search. We describe two optimization techniques for queries with regular path expressions, which we call regular queries. Both rely on graph schemas, which specify partial knowledge of a graph's structure. Query pruning restricts search to a fragment of the graph; we give an eecient algorithm for rewriting any regular query into a pruned one. Query rewriting using state extents can entirely eliminate or substantially reduce graph traversal; it is reminiscent of optimizing relational queries using indices. There may be several ways to optimize a query using state extents ; we give an exponential-time algorithm that nds all such optimizations. On special forms of regular queries, the algorithm is provably eecient. We also give an eecient approximation algorithm that works on all regular queries. Our target databases are \intranet" sites, i.e., Web sites associated with a company or organization. Intranet sites are good targets for our techniques, because they often reeect the structure of their organization, hence their structure is partially known. Preliminary results show that regular queries applied to an intranet site produce more precise results than keyword-only queries and that our optimizations substantially reduce the cost of evaluating regular queries, indicating regular queries could be evaluated eeciently by Web-site search engines.

To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.


Question: Utilizing L1-regularization paths for learning the structure of graphical models.

This paper introduces a novel technique for parameter estimation in linear models, coined the 'Lasso', which combines the advantages of subset selection and ridge regression while overcoming their limitations. The Lasso method minimizes the residual sum of squares under the constraint that the sum of the absolute values of the coefficients remains below a certain threshold. This unique constraint mechanism leads to a model where some coefficients are shrunk to exactly zero, thus facilitating model interpretation and feature selection. Through extensive simulations, it is observed that the Lasso exhibits a blend of desirable traits, offering the interpretability of subset selection and the stability of ridge regression. Interestingly, the Lasso approach bears a connection to the recent advancements in adaptive function estimation by Donoho and Johnstone. The versatility of the Lasso concept is further highlighted by its potential applications in a variety of statistical models, including generalized regression models and tree-based models, indicating its broad relevance across different domains.

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closedform solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford and Half-Quadratic envelopes. Envelope representations lead to novel proximal algorithms for statistical optimisation of composite objective functions which include both non-smooth and non-convex objectives. We illustrate our methodology with regularized Logistic and Poisson regression and non-convex bridge penalties with a fused lasso norm. We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions. Finally, we provide directions for future research.

Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation – rules for gradient backpropagation through stochastic variables – and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.

Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation (BP), have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model, which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for exact maximum a posterior inference and (ii) back propagation with stochastic gradient descent for model parameter estimation, leading to scalable learning of the model in an end-to-end discriminative manner. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model. Experimental results on two real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised/unsupervised LDA learning methods.

We propose a hypergraph-based active learning scheme which we term HS; HS generalizes the previously reported algorithm S originally proposed for graph-based active learning with pointwise queries [1]. Our HS method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS for the above described generalized settings. Both the theoretical and empirical results show that HS requires a significantly fewer number of queries than S when one uses S over a graph obtained from the corresponding hypergraph via clique expansion.

We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.


Question: L1-regularization paths for learning the structure of graphical models.

This paper introduces the Max-Min Hill-Climbing (MMHC) algorithm, a novel approach to Bayesian network structure learning that effectively integrates elements from local learning, constraint-based, and search-and-score methodologies. The MMHC algorithm first reconstructs the skeleton of a Bayesian network through a combination of these techniques, then proceeds to orient the edges using a Bayesian-scoring greedy hill-climbing search. In an extensive empirical evaluation, MMHC surpasses, on average and across various metrics, several well-established and state-of-the-art algorithms including PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These findings represent the first comprehensive comparison of most major Bayesian network structure learning algorithms against each other. MMHC, in particular, exhibits theoretical advantages over the Sparse Candidate algorithm, a claim supported by our experimental results. The MMHC algorithm and the detailed outcomes of our comparative study are accessible to the public at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html, providing researchers and practitioners with a valuable resource for advancing the field of Bayesian network structure learning.

Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, we can easily express our problem domain knowledge in the constraints of the model at the expense of difficulties during inference. Deterministic deep neural networks are constructed in such a way that inference is straightforward, but we sacrifice the ability to easily incorporate problem domain knowledge. The goal of this paper is to provide a general strategy to obtain the advantages of both approaches while avoiding many of their disadvantages. The general idea can be summarized as follows: given a model-based approach that requires an iterative inference method, we unfold the iterations into a layer-wise structure analogous to a neural network. We then de-couple the model parameters across layers to obtain novel neural-network-like architectures that can easily be trained discriminatively using gradient-based methods. The resulting formula combines the expressive power of a conventional deep network with the internal structure of the model-based approach, while allowing inference to be performed in a fixed number of layers that can be optimized for best performance. We show how this framework can be applied to the non-negative matrix factorization to obtain a novel non-negative deep neural network architecture, that can be trained with a multiplicative back-propagation-style update algorithm. We present experiments in the domain of speech enhancement, where we show that the resulting model is able to outperform conventional neural network while only requiring a fraction of the number of parameters. We believe this is due to the ability afforded by our framework to incorporate problem level assumptions into the architecture of the deep network. arXiv.org This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2014 201 Broadway, Cambridge, Massachusetts 02139

We study the survey propagation algorithm [19, 5, 4], which is an iterative technique that appears to be very effective in solving random k-SAT problems even with densities close to threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), parameterized by a real number ρ. We then show that applying belief propagation---a well-known "message-passing technique---to this family of MRFs recovers various algorithms, ranging from pure survey propagation at one extreme (ρ = 1) to standard belief propagation on the uniform distribution over SAT assignments at the other extreme (ρ = 0). Configurations in these MRFs have a natural interpretation as generalized satisfiability assignments, on which a partial order can be defined. We isolate cores as minimal elements in this partial ordering, and prove that any core is a fixed point of survey propagation. We investigate the associated lattice structure, and prove a weight-preserving identity that shows how any MRF with p > 0 can be viewed as a "smoothed" version of the naive factor graph representation of the k-SAT problem (p = 0). Our experimental results show that message-passing on our family of MRFs is most effective for values of ρ ≠ 1 (i.e., distinct from survey propagation); moreover, they suggest that random formulas may not typically possess non-trivial cores. Finally, we isolate properties of Gibbs sampling and message-passing algorithms that are typical for an ensemble of k-SAT problems. We prove that the space of cores for random formulas is highly disconnected, and show that for values of p sufficiently close to one, either the associated MRF is highly concentrated around the all-star assignment, or it has exponentially small conductance. Similarly, we prove that for p sufficiently close to one, the all-star assignment is attractive for message-passing when analyzed in the density-evolution setting.

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.

We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains. In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively. First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization. We then propose an alternative formulation, as well as an efficient algorithm to optimize it. Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors. We also provide a nonlinear extension that is able to achieve better generalization than existing methods.

We generalize Pearl’s back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.

We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solutions benefit from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the “cluster assumption”. Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces.

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.

This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as “Having observed A, what can we expect of B?”; abductive queries, such as “What is the most plausible explanation for a given set of observations?”; and control queries; such as “What will happen if we intervene and act on the environment?” Answers to the first type of query depend only on probabilistic

Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies.

It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.


Question: Learning the structure of graphical models by utilizing L1-regularization paths.

This paper introduces a novel algorithm for learning Bayesian network structures, which focuses on ordering-based search as a means to simplify and enhance the effectiveness of the learning process. Given the inherent complexity and NP-hard nature of the Bayesian network (BN) structure learning problem, researchers have traditionally resorted to heuristic search methods. Despite numerous proposed strategies, few have surpassed the performance of the basic greedy hill-climbing algorithm with tabu lists, and these advanced algorithms often require sophisticated implementation. The proposed approach seeks to rectify this by simplifying the search process.

The core concept behind this method is the observation that the optimal Bayesian network (with a bounded in-degree) for a given node ordering can be efficiently determined. Instead of conducting a search through the vast space of all possible structures, which can be computationally expensive and prone to local optima, the algorithm searches through the space of node orderings. For each ordering, the best network structure that is consistent with the ordering is selected. This method significantly reduces the search space, takes larger and more global steps, and benefits from a lower branching factor compared to traditional structure learning methods. Notably, it also eliminates the need for costly acyclicity checks that are necessary in structure-based searches.

The paper evaluates the performance of the ordering-based search algorithm using both synthetic and real-world datasets, measuring both the quality of the learned network structure and the computational time required. The results demonstrate that this algorithm outperforms the standard baseline of greedy hill-climbing with tabu lists and is competitive with more complex and difficult-to-implement algorithms. This finding highlights the potential of ordering-based search as an efficient and effective strategy for learning Bayesian network structures.

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is modelagnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.

Structured learning with inexact inference is a fundamental problem. We propose variants of the structured perceptron algorithm under a general “violation-fixing” framework that guarantees convergence. This framework subsumes previous remedies including “early update” as special cases, and also explains why standard perceptron may fail with inexact search. We also propose new update methods within this framework which learn better models with dramatically reduced training times on state-of-the-art part-of-speech tagging and incremental parsing systems.

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.

A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(/m), where land mare the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer “pre-training” phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks.

In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.

We develop a 2nd-order optimization method based on the “Hessian-free” approach, and apply it to training deep auto-encoders. Without using pre-training, we obtain results superior to those reported by Hinton & Salakhutdinov (2006) on the same tasks they considered. Our method is practical, easy to use, scales nicely to very large datasets, and isn’t limited in applicability to autoencoders, or any specific model class. We also discuss the issue of “pathological curvature” as a possible explanation for the difficulty of deeplearning and how 2nd-order optimization, and our method in particular, effectively deals with it.

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.


Question: Learning the structure of graphical models by utilizing L1-regularization paths.

This document explores an innovative technique for learning Bayesian network structures that effectively integrates prior knowledge with statistical data. Unlike traditional approaches that rely solely on statistical inference or expert knowledge, this method employs a combination of both to construct more robust and accurate models. The process begins with a prior network structure informed by domain expertise, which is then refined through statistical learning on empirical data. The algorithm iteratively adjusts the network topology by evaluating conditional independencies and dependencies in the data, ensuring that the final structure not only aligns with statistical evidence but also incorporates expert insights. This hybrid approach to Bayesian network learning is particularly advantageous in scenarios where data is limited or noisy, as it leverages existing knowledge to guide the learning process and mitigate the risk of overfitting. Moreover, it facilitates the incorporation of causal relationships and domain-specific constraints, leading to models that are not only statistically sound but also semantically meaningful within their respective contexts. Through a series of experiments and case studies, the effectiveness of this combined knowledge and data-driven approach to Bayesian network structure learning is demonstrated, highlighting its potential for applications in various fields where complex data analysis and decision-making are critical.

This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as “Having observed A, what can we expect of B?”; abductive queries, such as “What is the most plausible explanation for a given set of observations?”; and control queries; such as “What will happen if we intervene and act on the environment?” Answers to the first type of query depend only on probabilistic

In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance.

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are incorporated by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are merged to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (‘Occam’s Razor’). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based n-grams, and stochastic context-free grammars.

Deep directed generative models have attracted much attention recently due to their generative modeling nature and powerful data representation ability. In this article, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures. We focus on a specific structure that consists of layers of Bayesian networks (BNs) due to the property of capturing inherent and rich dependencies among latent variables. The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations. Current solutions use variational methods, often through an auxiliary network, to approximate the posterior probability inference. In contrast, inference can also be performed directly without using any auxiliary network to maximally preserve the dependencies among the latent variables. Specifically, by exploiting the sparse representation with the latent space, max-max instead of maxsum operation can be used to overcome the exponential number of latent configurations. Furthermore, the max-max operation and augmented coordinate ascent (AugCA) are applied to both supervised and unsupervised learning as well as to various inference. Quantitative evaluations on benchmark data sets of different models are given for both data representation and feature-learning tasks.

In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity of the unknown signal. This signal prior includes some of the existing models as special cases and achieves a high degree of sparsity. We develop a constructive (greedy) algorithm resulting from this formulation where necessary parameters are estimated solely from the observation and therefore no user-intervention is needed. We provide experimental results with synthetic 1D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.

Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state–of–the–art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.

MOTIVATION
KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory.

RESULTS
We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway.

AVAILABILITY
KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml).

In this semitutorial paper we discuss a general message passing algorithm, which we call the generalized distributive law (GDL). The GDL is a synthesis of the work of many authors in the information theory, digital communications, signal processing, statistics, and artificial intelligence communities. It includes as special cases the Baum–Welch algorithm, the fast Fourier transform (FFT) on any finite Abelian group, the Gallager–Tanner–Wiberg decoding algorithm, Viterbi’s algorithm, the BCJR algorithm, Pearl’s “belief propagation” algorithm, the Shafer–Shenoy probability propagation algorithm, and the turbo decoding algorithm. Although this algorithm is guaranteed to give exact answers only in certain cases (the “junction tree” condition), unfortunately not including the cases of GTW with cycles or turbo decoding, there is much experimental evidence, and a few theorems, suggesting that it often works approximately even when it is not supposed to.

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.

Neural networks have been shown to be an effective tool for learning algorithms over graph-structured data. However, graph representation techniques—that convert graphs to real-valued vectors for use with neural networks—are still in their infancy. Recent works have proposed several approaches (e.g., graph convolutional networks), but these methods have difficulty scaling and generalizing to graphs with different sizes and shapes. We present GRAPH2SEQ, a new technique that represents vertices of graphs as infinite time-series. By not limiting the representation to a fixed dimension, GRAPH2SEQ scales naturally to graphs of arbitrary sizes and shapes. GRAPH2SEQ is also reversible, allowing full recovery of the graph structure from the sequences. By analyzing a formal computational model for graph representation, we show that an unbounded sequence is necessary for scalability. Our experimental results with GRAPH2SEQ show strong generalization and new state-of-the-art performance on a variety of graph combinatorial optimization problems.


Question: Statistical signal processing using hidden Markov models based on wavelet analysis.

This paper explores the estimation of parameters governing a mixture density, a topic that has garnered extensive scholarly attention over nearly nine decades. Over the past two decades, the popularity of the maximum likelihood method has surged, largely due to advancements in high-speed computing technology. Initially, we provide a concise overview of the relevant literature and delve into the principles of maximum likelihood estimation in this context. Subsequently, we shift our focus to an iterative numerical technique specifically designed for approximating maximum likelihood estimates in mixture density scenarios. This technique, known as the EM algorithm, is a specialized adaptation of a broader algorithm with the same name, primarily utilized for approximating maximum likelihood estimates in situations involving incomplete data. We elaborate on the formulation and key attributes of the EM algorithm within the context of mixture densities, with a particular emphasis on mixtures derived from exponential family distributions. This comprehensive discussion encompasses both theoretical underpinnings and practical considerations, highlighting the algorithm's efficacy in addressing complex statistical challenges in signal processing and beyond.

We introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. We derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. We compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser.

This thesis presents work on automated analysis techniques for musical audio. A complete analysis of the content within an audio waveform is termed ‘transcription’ and can be used for intelligent, informed processing of the signal in a variety of applications. However, full transcription is beyond the ability of current methods and hence this thesis concerns subtasks of whole problem. A major part of musical analysis is the extraction of signal information from a time-frequency representation, often the short time Fourier transform or spectrogram. The ‘reassignment’ method is a technique for improving the clarity of these representations. It is comprehensively reviewed and its relationship to a number of classical instantaneous frequency measures is explicitly stated. Performance of reassignment as a sinusoidal frequency estimator is then assessed quantitatively for the first time. This is achieved in the framework of the Cramér-Rao bound. Reassignment is shown to be inferior to some other estimators for simple synthetic signals but achieves comparable results for real musical examples. Several extensions and uses of the reassignment method are then described. These include the derivation of reassignment measures extracted from the amplitude of the spectrogram, rather than the traditional phase-based method and the use of reassignment measures for signal classification. The second major area of musical analysis investigated in this thesis is beat tracking, where the aim is to extract both the tempo implied in the music and also its phase which is analagous to beat location. Three models for the beat in general music examples are described. These are cast in a state-space formulation within the Bayesian paradigm, which allows the use of Monte Carlo methods for inference of the model parameters. The first two models use pre-extracted note onsets and model tempo as either a linear Gaussian process or as Brownian motion. The third model also includes on-line detection of onsets, thus adding an extra layer of complexity. Sequential Monte Carlo algorithms, termed particle filters, are then used for the estimation of the data. The systems are tested on an extensive database, nearly three and a half hours in length and consisting of various styles of music. The results exceed the current published state of the art. The research presented here could form the early stages of a full transcription system, a proposal for which is also expounded. This would use a flow of contextual information from simpler, more global structures to aid the inference of more complex and detailed processes. The global structures present in the music (such as style, structure, tempo, etc.) still have their own uses, making this scheme instantly applicable to real problems.

Signal processing today is performed in the vast majority of systems for ECG analysis and interpretation. The objective of ECG signal processing is manifold and comprises the improvement of measurement accuracy and reproducibility (when compared with manual measurements) and the extraction of information not readily available from the signal through visual assessment. In many situations, the ECG is recorded during ambulatory or strenuous conditions such that the signal is corrupted by different types of noise, sometimes originating from another physiological process of the body. Hence, noise reduction represents another important objective of ECG signal processing; in fact, the waveforms of interest are sometimes so heavily masked by noise that their presence can only be revealed once appropriate signal processing has first been applied. Electrocardiographic signals may be recorded on a long timescale (i.e., several days) for the purpose of identifying intermittently occurring disturbances in the heart rhythm. As a result, the produced ECG recording amounts to huge data sizes that quickly fill up available storage space. Transmission of signals across public telephone networks is another application in which large amounts of data are involved. For both situations, data compression is an essential operation and, consequently, represents yet another objective of ECG signal processing. Signal processing has contributed significantly to a new understanding of the ECG and its dynamic properties as expressed by changes in rhythm and beat morphology. For example, techniques have been developed that characterize oscillations related to the cardiovascular system and reflected by subtle variations in heart rate. The detection of low-level, alternating changes in T wave amplitude is another example of oscillatory behavior that has been established as an indicator of increased risk for sudden, life-threatening arrhythmias. Neither of these two oscillatory signal properties can be perceived by the naked eye from a standard ECG printout. Common to all types of ECG analysis—whether it concerns resting ECG interpretation, stress testing, ambulatory monitoring, or intensive care monitoring—is a basic set of algorithms that condition the signal with respect to different types of noise and artifacts, detect heartbeats, extract basic ECG measurements of wave amplitudes and durations, and compress the data for efficient storage or transmission; the block diagram in Fig. 1 presents this set of signal processing algorithms. Although these algorithms are frequently implemented to operate in sequential order, information on the occurrence time of a heartbeat, as produced by the QRS detector, is sometimes incorporated into the other algorithms to improve performance. The complexity of each algorithm varies from application to application so that, for example, noise filtering performed in ambulatory monitoring is much more sophisticated than that required in resting ECG analysis. Once the information produced by the basic set of algorithms is available, a wide range of ECG applications exist where it is of interest to use signal processing for quantifying heart rhythm and beat morphology properties. The signal processing associated with two such applications—high-resolution ECG and T wave alternans—are briefly described at the end of this article. The interested reader is referred to, for example, Ref. 1, where a detailed description of other ECG applications can be found.

In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.

It is observed that the stress level is function of various statistical parameters like standard deviation, entropy, energy,, mean, Covariance and power of the ECG signals of two states i.e. normal state of mind and stressed state of mind. Further, it is observed that the features extracted are directly from the ECG in frequency domain using db4 wavelet. However, db4 introduces some error on account of db4 wavelet shape. This error in turn amplifies while measuring the features as mentioned above. In order to reduce this error, we propose a Bior 3.9 wavelet family to decompose the ECG signal. The decomposed ECG signal is now analyzed statistically to extract the above feature. Further, in the existing work, all the ECGs are taken by using the 12 leads method. This factor also adds some undue stress to the person under scanner. And this is time consuming too. Therefore, to reduce this complexity, we propose to analyze all above features by using a two lead ECG. Further, a back propagation neural network is trained using the above features as input neurons in order to classify the stress level.

It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.

This paper introduces an approach for flexible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coefficients is shown to yield the best overall performance against some common benchmarks.

The detection of a traffic light on the road is important for the safety of persons who occupy a vehicle, in a normal vehicles or an autonomous land vehicle. In normal vehicle, a system that helps a driver to perceive the details of traffic signals, necessary to drive, could be critical in a delicate driving manoeuvre (i.e crossing an intersection of roads). Furthermore, traffic lights detection by an autonomous vehicle is a special case of perception, because it is important for the control that the autonomous vehicle must take. Multiples authors have used image processing as a base for achieving traffic light detection. However, the image processing presents a problem regarding conditions for capturing scenes, and therefore, the traffic light detection is affected. For this reason, this paper proposes a method that links the image processing with an estimation state routine formed by Hidden Markov Models (HMM). This method helps to determine the current state of the traffic light detected, based on the obtained states by image processing, aiming to obtain the best performance in the determination of the traffic light states. With the proposed method in this paper, we obtained 90.55% of accuracy in the detection of the traffic light state, versus a 78.54% obtained using solely image processing. The recognition of traffic lights using image processing still has a large dependence on the capture conditions of each frame from the video camera. In this context, the addition of a pre-processing stage before image processing could contribute to improve this aspect, and could provide a better results in determining the traffic light state.

Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.