kth.sePublications
Change search
Link to record
Permanent link

Direct link
Vasiloudis, Theodore
Publications (5 of 5) Show all publications
Vasiloudis, T., Cho, H. & Boström, H. (2019). Block-distributed Gradient Boosted Trees. In: SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval: . Paper presented at 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019; Museum of Science and Industry, Paris; France; 21 July 2019 through 25 July 2019 (pp. 1025-1028). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Block-distributed Gradient Boosted Trees
2019 (English)In: SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery (ACM), 2019, p. 1025-1028Conference paper, Published paper (Refereed)
Abstract [en]

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assume a row-distributed dataset, addressing scalability only with respect to the number of data points and not the number of features, and increasing communication cost for high-dimensional data. In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs. We achieve communication efficiency by making full use of the data sparsity and adapting the Quickscorer algorithm to the block-distributed setting. We evaluate our approach using datasets with millions of features, and demonstrate that we are able to achieve multiple orders of magnitude reduction in communication cost for sparse data, with no loss in accuracy, while providing a more scalable design. As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Gradient Boosted Trees, Distributed Systems, Communication Efficiency, Scalability
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-249994 (URN)10.1145/3331184.3331331 (DOI)000501488900130 ()2-s2.0-85073774997 (Scopus ID)978-1-4503-6172-9 (ISBN)
Conference
42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019; Museum of Science and Industry, Paris; France; 21 July 2019 through 25 July 2019
Funder
Swedish Foundation for Strategic Research , BD15-0006
Note

QC 20190426

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2022-12-12Bibliographically approved
Vasiloudis, T., Beligianni, F. & De Francisci Morales, G. (2017). BoostVHT: Boosting distributed streaming decision trees. In: International Conference on Information and Knowledge Management, Proceedings: . Paper presented at 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, 6 November 2017 through 10 November 2017 (pp. 899-908). Association for Computing Machinery
Open this publication in new window or tab >>BoostVHT: Boosting distributed streaming decision trees
2017 (English)In: International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery , 2017, p. 899-908Conference paper, Published paper (Refereed)
Abstract [en]

Online boosting improves the accuracy of classifiers for unbounded streams of data by chaining them into an ensemble. Due to its sequential nature, boosting has proven hard to parallelize, even more so in the online setting. This paper introduces BoostVHT, a technique to parallelize online boosting algorithms. Our proposal leverages a recently-developed model-parallel learning algorithm for streaming decision trees as a base learner. This design allows to neatly separate the model boosting from its training. As a result, BoostVHT provides a flexible learning framework which can employ any existing online boosting algorithm, while at the same time it can leverage the computing power of modern parallel and distributed cluster environments. We implement our technique on Apache SAMOA, an open-source platform for mining big data streams that can be run on several distributed execution engines, and demonstrate order of magnitude speedups compared to the state-of-the-art.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2017
Keywords
Boosting, Decision trees, Distributed systems, Online learning, Big data, Cluster computing, Clustering algorithms, Data mining, Distributed computer systems, Forestry, Knowledge management, Online systems, Trees (mathematics), Distributed clusters, Distributed streaming, Flexible Learning, Open source platforms, Parallel learning algorithms, Learning algorithms
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-227056 (URN)10.1145/3132847.3132974 (DOI)000440845300089 ()2-s2.0-85037345394 (Scopus ID)9781450349185 (ISBN)
Conference
26th ACM International Conference on Information and Knowledge Management, CIKM 2017, 6 November 2017 through 10 November 2017
Note

QC 20180503

Available from: 2018-05-03 Created: 2018-05-03 Last updated: 2022-06-26Bibliographically approved
Görnerup, O., Gillblad, D. & Vasiloudis, T. (2017). Domain-agnostic discovery of similarities and concepts at scale. Knowledge and Information Systems, 51(2), 531-560
Open this publication in new window or tab >>Domain-agnostic discovery of similarities and concepts at scale
2017 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 51, no 2, p. 531-560Article in journal (Refereed) Published
Abstract [en]

Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e., its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts—abstractions of objects—are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields and will be demonstrated here in three domains: computational linguistics, music, and molecular biology, where the numbers of objects and correlations range from small to very large.

Place, publisher, year, edition, pages
London: Springer, 2017
Keywords
Similarity discovery, Concept mining, Distributional semantics, Graph processing
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-249992 (URN)10.1007/s10115-016-0984-2 (DOI)000399408200007 ()2-s2.0-84984793995 (Scopus ID)
Projects
E2ECLOUDS
Funder
Swedish Foundation for Strategic Research , RIT10-0043
Note

QC 20190426

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2022-06-26Bibliographically approved
Vasiloudis, T., Vahabi, H., Kravitz, R. & Rashkov, V. (2017). Predicting Session Length in Media Streaming. In: SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval: . Paper presented at 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017; Tokyo, Shinjuku; Japan; 7 August 2017 through 11 August 2017 (pp. 977-980). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Predicting Session Length in Media Streaming
2017 (English)In: SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery (ACM), 2017, p. 977-980Conference paper, Published paper (Refereed)
Abstract [en]

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads.

In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017
Keywords
User Behavior, Survival Analysis, Dwell Time, Session Length
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-250037 (URN)10.1145/3077136.3080695 (DOI)000454711900127 ()2-s2.0-85029395373 (Scopus ID)978-1-4503-5022-8 (ISBN)
Conference
40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017; Tokyo, Shinjuku; Japan; 7 August 2017 through 11 August 2017
Funder
Swedish Foundation for Strategic Research , RIT10-0043
Note

QC 20190426

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2022-06-26Bibliographically approved
Görnerup, O., Gillblad, D. & Vasiloudis, T. (2015). Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery. In: : . Paper presented at 2015 IEEE International Conference on Data Mining (ICDM).
Open this publication in new window or tab >>Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Appropriately defining and then efficiently calculating similarities from large data sets are often essential in data mining, both for building tractable representations and for gaining understanding of data and generating processes. Here we rely on the premise that given a set of objects and their correlations, each object is characterized by its context, i.e. its correlations to the other objects, and that the similarity between two objects therefore can be expressed in terms of the similarity between their respective contexts. Resting on this principle, we propose a data-driven and highly scalable approach for discovering similarities from large data sets by representing objects and their relations as a correlation graph that is transformed to a similarity graph. Together these graphs can express rich structural properties among objects. Specifically, we show that concepts -- representations of abstract ideas and notions -- are constituted by groups of similar objects that can be identified by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of domains, and will here be demonstrated for three distinct types of objects: codons, artists and words, where the numbers of objects and correlations range from small to very large.

National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-250036 (URN)10.1109/ICDM.2015.85 (DOI)000380541000013 ()2-s2.0-84963516560 (Scopus ID)978-1-4673-9504-5 (ISBN)
Conference
2015 IEEE International Conference on Data Mining (ICDM)
Funder
Swedish Foundation for Strategic Research , RIT10-0043
Note

QC 20190426

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2022-06-26Bibliographically approved
Organisations

Search in DiVA

Show all publications