PAC-Bayesian High Dimensional Bipartite Ranking

This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the PAC-Bayesian approach. We consider nonlinear additive scoring functions, and we derive non-asymptotic risk bounds under a sparsity assumption. In particular, oracle inequalities in probability holding under a margin condition assess the performance of our procedure, and prove its minimax optimality. An MCMC-flavored algorithm is proposed to implement our method, along with its behavior on synthetic and real-life datasets

Exploiting Latent Features of Text and Graphs

As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of available information. However, the models that help convert data into knowledge require nontrivial processes to make sense of large collections of text and massive online graphs. In both scenarios, modern machine learning pipelines produce embeddings --- semantically rich vectors of latent features --- to convert human constructs for machine understanding. In this dissertation we focus on information available within biomedical science, including human-written abstracts of scientific papers, as well as machine-generated graphs of biomedical entity relationships. We present the Moliere system, and our method for identifying new discoveries through the use of natural language processing and graph mining algorithms. We propose heuristically-based ranking criteria to augment Moliere, and leverage this ranking to identify a new gene-treatment target for HIV-associated Neurodegenerative Disorders. We additionally focus on the latent features of graphs, and propose a new bipartite graph embedding technique. Using our graph embedding, we advance the state-of-the-art in hypergraph partitioning quality. Having newfound intuition of graph embeddings, we present Agatha, a deep-learning approach to hypothesis generation. This system learns a data-driven ranking criteria derived from the embeddings of our large proposed biomedical semantic graph. To produce human-readable results, we additionally propose CBAG, a technique for conditional biomedical abstract generation

Learning to compare nodes in branch and bound with graph neural networks

En informatique, la résolution de problèmes NP-difficiles en un temps raisonnable est d’une grande importance : optimisation de la chaîne d’approvisionnement, planification, routage, alignement de séquences biologiques multiples, inference dans les modèles graphiques pro- babilistes, et même certains problèmes de cryptographie sont tous des examples de la classe NP-complet. En pratique, nous modélisons beaucoup d’entre eux comme un problème d’op- timisation en nombre entier, que nous résolvons à l’aide de la méthodologie séparation et évaluation. Un algorithme de ce style divise un espace de recherche pour l’explorer récursi- vement (séparation), et obtient des bornes d’optimalité en résolvant des relaxations linéaires sur les sous-espaces (évaluation). Pour spécifier un algorithme, il faut définir plusieurs pa- ramètres, tel que la manière d’explorer les espaces de recherche, de diviser une recherche l’espace une fois exploré, ou de renforcer les relaxations linéaires. Ces politiques peuvent influencer considérablement la performance de résolution. Ce travail se concentre sur une nouvelle manière de dériver politique de recherche, c’est à dire le choix du prochain sous-espace à séparer étant donné une partition en cours, en nous servant de l’apprentissage automatique profond. Premièrement, nous collectons des données résumant, sur une collection de problèmes donnés, quels sous-espaces contiennent l’optimum et quels ne le contiennent pas. En représentant ces sous-espaces sous forme de graphes bipartis qui capturent leurs caractéristiques, nous entraînons un réseau de neurones graphiques à déterminer la probabilité qu’un sous-espace contienne la solution optimale par apprentissage supervisé. Le choix d’un tel modèle est particulièrement utile car il peut s’adapter à des problèmes de différente taille sans modifications. Nous montrons que notre approche bat celle de nos concurrents, consistant à des modèles d’apprentissage automatique plus simples entraînés à partir des statistiques du solveur, ainsi que la politique par défaut de SCIP, un solveur open-source compétitif, sur trois familles NP-dures: des problèmes de recherche de stables de taille maximum, de flots de réseau multicommodité à charge fixe, et de satisfiabilité maximum.In computer science, solving NP-hard problems in a reasonable time is of great importance, such as in supply chain optimization, scheduling, routing, multiple biological sequence align- ment, inference in probabilistic graphical models, and even some problems in cryptography. In practice, we model many of them as a mixed integer linear optimization problem, which we solve using the branch and bound framework. An algorithm of this style divides a search space to explore it recursively (branch) and obtains optimality bounds by solving linear relaxations in such sub-spaces (bound). To specify an algorithm, one must set several pa- rameters, such as how to explore search spaces, how to divide a search space once it has been explored, or how to tighten these linear relaxations. These policies can significantly influence resolution performance. This work focuses on a novel method for deriving a search policy, that is, a rule for select- ing the next sub-space to explore given a current partitioning, using deep machine learning. First, we collect data summarizing which subspaces contain the optimum, and which do not. By representing these sub-spaces as bipartite graphs encoding their characteristics, we train a graph neural network to determine the probability that a subspace contains the optimal so- lution by supervised learning. The choice of such design is particularly useful as the machine learning model can automatically adapt to problems of different sizes without modifications. We show that our approach beats the one of our competitors, consisting of simpler machine learning models trained from solver statistics, as well as the default policy of SCIP, a state- of-the-art open-source solver, on three NP-hard benchmarks: generalized independent set, fixed-charge multicommodity network flow, and maximum satisfiability problems

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

Elastic Dataflow Processing on the Cloud

Τα νεφη εχουν μετατραπει σε μια ελκυστικη πλατφορμα για την πολυπλοκη επεξεργασια δεδομενων μεγαλης κλιμακας, ειδικα εξαιτιας της εννοιας της ελαστικοτητας, η οποια και τα χαρακτηριζει: οι υπολογιστικοι ποροι μπορουν να εκμισθωθουν δυναμικα και να χρησιμοποιουνται για οσο χρονο ειναι απαραιτητο. Αυτο δινει την δυνατοτητα να δημιουργηθει μια εικονικη υποδομη η οποια μπορει να αλλαζει δυναμικα στο χρονο. Οι συγχρονες εφαρμογες απαιτουν την εκτελεση πολυπλοκων ερωτηματων σε Μεγαλα Δεδομενα για την εξορυξη γνωσης και την υποστηριξη επιχειρησιακων αποφασεων. Τα πολυπλοκα αυτα ερωτηματα, εκφραζονται σε γλωσσες υψηλου επιπεδου και τυπικα μεταφραζονται σε ροες επεξεργασιας δεδομενων, η απλα ροες δεδομενων. Ενα λογικο ερωτημα που τιθεται ειναι κατα ποσον η ελαστικοτητα επηρεαζει την εκτελεση των ροων δεδομενων και με πιο τροπο. Ειναι λογικο οτι η εκτελεση να ειναι πιθανον γρηγοροτερη αν χρησιμοποιηθουν περισ- σοτεροι υπολογιστικοι ποροι, αλλα το κοστος θα ειναι υψηλοτερο. Αυτο δημιουργει την εννοια της οικο-ελαστικοτητας, ενος επιπλεον τυπου ελαστικοτητας ο οποιος προερχεται απο την οικονο- μικη θεωρια, και συλλαμβανει τις εναλλακτικες μεταξυ του χρονου εκτελεσης και του χρηματικου κοστους οπως προκυπτει απο την χρηση των πορων. Στα πλαισια αυτης της διδακτορικης διατριβης, προσεγγιζουμε την ελαστικοτητα με ενα ενοποιημενο μοντελο που περιλαμβανει και τις δυο ειδων ελαστικοτητες που υπαρχουν στα υπολογιστικα νεφη. Αυτη η ενοποιημενη προσεγγιση της ελαστικοτητας ειναι πολυ σημαντικη στην σχεδιαση συστηματων που ρυθμιζονται αυτοματα (auto-tuned) σε περιβαλλοντα νεφους. Αρχικα δειχνουμε οτι η οικο-ελαστικοτητα υπαρχει σε αρκετους τυπους υπολογισμου που εμφανιζονται συχνα στην πραξη και οτι μπορει να βρεθει χρησιμοποιωντας εναν απλο, αλλα ταυτοχρονα αποδοτικο και ε- πεκτασιμο αλγοριθμο. Επειτα, παρουσιαζουμε δυο εφαρμογες που χρησιμοποιουν αλγοριθμους οι οποιοι χρησιμοποιουν το ενοποιημενο μοντελο ελαστικοτητας που προτεινουμε για να μπορουν να προσαρμοζουν δυναμικα το συστημα στα ερωτηματα της εισοδου: 1) την ελαστικη επεξεργασια αναλυτικων ερωτηματων τα οποια εχουν πλανα εκτελεσης με μορφη δεντρων με σκοπο την μεγι- στοποιηση του κερδους και 2) την αυτοματη διαχειριση χρησιμων ευρετηριων λαμβανοντας υποψη το χρηματικο κοστος των υπολογιστικων και των αποθηκευτικων πορων. Τελος, παρουσιαζουμε το EXAREME, ενα συστημα για την ελαστικη επεξεργασια μεγαλου ογκου δεδομενων στο νεφος το οποιο εχει χρησιμοποιηθει και επεκταθει σε αυτην την δουλεια. Το συστημα προσφερει δηλωτικες γλωσσες που βασιζονται στην SQL επεκταμενη με συναρτησεις οι οποιες μπορει να οριστουν απο χρηστες (User-Defined Functions, UDFs). Επιπλεον, το συντακτικο της γλωσσας εχει επεκταθει με στοιχεια παραλληλισμου. Το EXAREME εχει σχεδιαστει για να εκμεταλλευεται τις ελαστικοτη- τες που προσφερουν τα νεφη, δεσμευοντας και αποδεσμευοντας υπολογιστικους πορους δυναμικα με σκοπο την προσαρμογη στα ερωτηματα.Clouds have become an attractive platform for the large-scale processing of modern applications on Big Data, especially due to the concept of elasticity, which characterizes them: resources can be leased on demand and used for as much time as needed, offering the ability to create virtual infrastructures that change dynamically over time. Such applications often require processing of complex queries that are expressed in a high-level language and are typically transformed into data processing flows (dataflows). A logical question that arises is whether elasticity affects dataflow execution and in which way. It seems reasonable that the execution is faster when more resources are used, however the monetary cost is higher. This gives rise to the concept eco-elasticity, an additional kind of elasticity that comes from economics, and captures the trade-offs between the response time of the system and the amount of money we pay for it as influenced by the use of different amounts of resources. In this thesis, we approach the elasticity of clouds in a unified way that combines both the traditional notion and eco-elasticity. This unified elasticity concept is essential for the development of auto-tuned systems in cloud environments. First, we demonstrate that eco-elasticity exists in several common tasks that appear in practice and that can be discovered using a simple, yet highly scalable and efficient algorithm. Next, we present two cases of auto-tuned algorithms that use the unified model of elasticity in order to adapt to the query workload: 1) processing analytical queries in the form of tree execution plans in order to maximize profit and 2) automated index management taking into account compute and storage re- sources. Finally, we describe EXAREME, a system for elastic data processing on the cloud that has been used and extended in this work. The system offers declarative languages that are based on SQL with user-defined functions (UDFs) extended with parallelism primi- tives. EXAREME exploits both elasticities of clouds by dynamically allocating and deallocating compute resources in order to adapt to the query workload

Computational Labeling, Partitioning, and Balancing of Molecular Networks

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes

Network Analysis on Incomplete Structures.

Over the past decade, networks have become an increasingly popular abstraction for problems in the physical, life, social and information sciences. Network analysis can be used to extract insights into an underlying system from the structure of its network representation. One of the challenges of applying network analysis is the fact that networks do not always have an observed and complete structure. This dissertation focuses on the problem of imputation and/or inference in the presence of incomplete network structures. I propose four novel systems, each of which, contain a module that involves the inference or imputation of an incomplete network that is necessary to complete the end task. I first propose EdgeBoost, a meta-algorithm and framework that repeatedly applies a non-deterministic link predictor to improve the efficacy of community detection algorithms on networks with missing edges. On average EdgeBoost improves performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook. The second system, Butterworth, identifies a social network user's topic(s) of interests and automatically generates a set of social feed ``rankers'' that enable the user to see topic specific sub-feeds. Butterworth uses link prediction to infer the missing semantics between members of a user's social network in order to detect topical clusters embedded in the network structure. For automatically generated topic lists, Butterworth achieves an average top-10 precision of 78%, as compared to a time-ordered baseline of 45%. Next, I propose Dobby, a system for constructing a knowledge graph of user-defined keyword tags. Leveraging a sparse set of labeled edges, Dobby trains a supervised learning algorithm to infer the hypernym relationships between keyword tags. Dobby was evaluated by constructing a knowledge graph of LinkedIn's skills dataset, achieving an average precision of 85% on a set of human labeled hypernym edges between skills. Lastly, I propose Lobbyback, a system that automatically identifies clusters of documents that exhibit text reuse and generates ``prototypes'' that represent a canonical version of text shared between the documents. Lobbyback infers a network structure in a corpus of documents and uses community detection in order to extract the document clusters.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133443/1/mattburg_1.pd

Spatial Database Support for Virtual Engineering

The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex products when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery. This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects. After a short introductory Part I, we look in Part II at different spatial index structures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects. In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We introduce a very effective similarity model which is based on a new paradigm in similarity search, namely the use of vector set represented objects. In order to guarantee efficient query processing, suitable filters are introduced for accelerating similarity queries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets.Ein schneller und reibungsloser Entwicklungsprozess neuer Produkte ist ein wichtiger Faktor für den wirtschaftlichen Erfolg vieler Unternehmen insbesondere aus der Luft- und Raumfahrttechnik und der Automobilindustrie. Damit Ingenieure in immer kürzerer Zeit immer anspruchsvollere Produkte entwickeln können, werden effektive und effiziente Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten benötigt. Um den hohen Anforderungen eines produktiven Einsatzes zu genügen, müssen entsprechend spezialisierte Zugriffsmethoden in vollwertige Datenbanksysteme integriert werden, so dass zentrale Datenbankdienste wie Trans-aktionen, kontrollierte Nebenläufigkeit und Wiederanlauf sichergestellt sind. Ziel dieser Doktorarbeit ist es deshalb, effektive und effiziente Algorithmen für Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten zu ent-wickeln und diese in kommerzielle Objekt-Relationale Datenbanksysteme zu integrieren. Im ersten Teil der Arbeit werden verschiedene räumliche Indexstrukturen zur effizienten Bearbeitung von Kollisionsanfragen diskutiert und auf ihre Integrationsfähigkeit in Objekt-Relationale Datenbanksysteme hin untersucht. Daran an-knüpfend werden zwei generische Verfahren zur Beschleunigung von Kollisionsanfragen vorgestellt. Das erste Verfahren benutzt statistische Informationen räumlicher Indexstrukturen, um eine gegebene Anfrage zu beschleunigen. Das zweite Verfahren beruht auf einer kostenbasierten Zerlegung komplexer räumlicher Datenbank- Objekte. Diese beiden Verfahren ergänzen sich gegenseitig und können unabhängig voneinander oder zusammen eingesetzt werden. In einer ausführlichen experimentellen Evaluation wird gezeigt, dass die beiden vorgestellten Verfahren interaktive Kollisionsanfragen auf umfangreichen Datenmengen und komplexen Objekten ermöglichen. Im zweiten Teil der Arbeit werden verschiedene Ähnlichkeitsmodelle für räum-liche Objekte vorgestellt. Es wird experimentell aufgezeigt, dass datenpartitionierende Modelle effektiver sind als raumpartitionierende Verfahren. Weiterhin werden geeignete Filtertechniken zur Beschleunigung des Anfrageprozesses entwickelt und experimentell untersucht. Basierend auf Clustering und den entwickelten Ähnlichkeitsmodellen wird ein industrietauglicher Prototyp vorgestellt, der Benutzern hilft, durch große Datenmengen zu navigieren

Parallel and Flow-Based High Quality Hypergraph Partitioning

Balanced hypergraph partitioning is a classic NP-hard optimization problem that is a fundamental tool in such diverse disciplines as VLSI circuit design, route planning, sharding distributed databases, optimizing communication volume in parallel computing, and accelerating the simulation of quantum circuits. Given a hypergraph and an integer $k$, the task is to divide the vertices into $k$ disjoint blocks with bounded size, while minimizing an objective function on the hyperedges that span multiple blocks. In this dissertation we consider the most commonly used objective, the connectivity metric, where we aim to minimize the number of different blocks connected by each hyperedge. The most successful heuristic for balanced partitioning is the multilevel approach, which consists of three phases. In the coarsening phase, vertex clusters are contracted to obtain a sequence of structurally similar but successively smaller hypergraphs. Once sufficiently small, an initial partition is computed. Lastly, the contractions are successively undone in reverse order, and an iterative improvement algorithm is employed to refine the projected partition on each level. An important aspect in designing practical heuristics for optimization problems is the trade-off between solution quality and running time. The appropriate trade-off depends on the specific application, the size of the data sets, and the computational resources available to solve the problem. Existing algorithms are either slow, sequential and offer high solution quality, or are simple, fast, easy to parallelize, and offer low quality. While this trade-off cannot be avoided entirely, our goal is to close the gaps as much as possible. We achieve this by improving the state of the art in all non-trivial areas of the trade-off landscape with only a few techniques, but employed in two different ways. Furthermore, most research on parallelization has focused on distributed memory, which neglects the greater flexibility of shared-memory algorithms and the wide availability of commodity multi-core machines. In this thesis, we therefore design and revisit fundamental techniques for each phase of the multilevel approach, and develop highly efficient shared-memory parallel implementations thereof. We consider two iterative improvement algorithms, one based on the Fiduccia-Mattheyses (FM) heuristic, and one based on label propagation. For these, we propose a variety of techniques to improve the accuracy of gains when moving vertices in parallel, as well as low-level algorithmic improvements. For coarsening, we present a parallel variant of greedy agglomerative clustering with a novel method to resolve cluster join conflicts on-the-fly. Combined with a preprocessing phase for coarsening based on community detection, a portfolio of from-scratch partitioning algorithms, as well as recursive partitioning with work-stealing, we obtain our first parallel multilevel framework. It is the fastest partitioner known, and achieves medium-high quality, beating all parallel partitioners, and is close to the highest quality sequential partitioner. Our second contribution is a parallelization of an n-level approach, where only one vertex is contracted and uncontracted on each level. This extreme approach aims at high solution quality via very fine-grained, localized refinement, but seems inherently sequential. We devise an asynchronous n-level coarsening scheme based on a hierarchical decomposition of the contractions, as well as a batch-synchronous uncoarsening, and later fully asynchronous uncoarsening. In addition, we adapt our refinement algorithms, and also use the preprocessing and portfolio. This scheme is highly scalable, and achieves the same quality as the highest quality sequential partitioner (which is based on the same components), but is of course slower than our first framework due to fine-grained uncoarsening. The last ingredient for high quality is an iterative improvement algorithm based on maximum flows. In the sequential setting, we first improve an existing idea by solving incremental maximum flow problems, which leads to smaller cuts and is faster due to engineering efforts. Subsequently, we parallelize the maximum flow algorithm and schedule refinements in parallel. Beyond the strive for highest quality, we present a deterministically parallel partitioning framework. We develop deterministic versions of the preprocessing, coarsening, and label propagation refinement. Experimentally, we demonstrate that the penalties for determinism in terms of partition quality and running time are very small. All of our claims are validated through extensive experiments, comparing our algorithms with state-of-the-art solvers on large and diverse benchmark sets. To foster further research, we make our contributions available in our open-source framework Mt-KaHyPar. While it seems inevitable, that with ever increasing problem sizes, we must transition to distributed memory algorithms, the study of shared-memory techniques is not in vain. With the multilevel approach, even the inherently slow techniques have a role to play in fast systems, as they can be employed to boost quality on coarse levels at little expense. Similarly, techniques for shared-memory parallelism are important, both as soon as a coarse graph fits into memory, and as local building blocks in the distributed algorithm