Morteza Haghir Chehreghani Abstract We study Minimax distance measures for K-nearest neighbor search and classiﬁcation. Recently, the use of this distance measure is shown to improve the K-nearest neighbor classiﬁcation results. We consider the computational aspects of this problem and propose

Morteza Haghir Chehreghani has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

However, existing approaches for modeling transitivity suf-fer from at least one of the following problems: i) they produce graphs from a specific class like bipartite graphs, ii) they do not give an analytical Morteza Haghir Chehreghani (academic supervisor) morteza.chehreghani@chalmers.se; Sadegh Rahrovani, (industrial supervisor) sadegh.rahrovani@volvocars.com; Martin Magnusson (Group manager at VolvoCars), martin.m.magnusson@volvocars.com (*) The project is taken and will be conducted by students during spring 2021 2020-07-28 We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different Morteza Haghir Chehreghani 1 · Mostafa Haghir Chehreghani 2 Received: 11 November 2019 / Revised: 11 May 2020 / Accepted: 6 July 2020 / Published online: 16 August 2020 Morteza Haghir Chehreghani morteza.chehreghani@inf.ethz.ch Mario Frank mfrank@berkeley.edu Andreas P. Streich andreas.streich@alumni.ethz.ch Department of Computer Science, ETH Zurich, Switzerland Buhmann Chehreghani Frank Streich sender S problem generator PG receiver R ˝^ Mostafa Haghir Chehreghani, Morteza Haghir Chehreghani, Caro Lucas, and Masoud Rahgozar Abstract—Frequent tree patterns have many practical appli-cations in different domains such as XML mining, web usage analysis, etc.

arXiv preprint arXiv:1905.05095, 2019. If you have any questions please contact academic supervisor Morteza Haghir Chehreghani at morteza.chehreghani@chalmers.se and Morteza Haghir Chehreghani (Chalmers). Maria Svedlund (Volvo Cars) maria.svedlund@volvocars.com. For more general questions you could contact hiring Application deadline: 25 October, 2018. For questions, please contact: Morteza Haghir Chehreghani, e-mail: morteza.chehreghani@chalmers.se Visa mindre [11] Morteza Haghir Chehreghani, Mostafa H. Chehreghani, “Modeling Transitivity in Complex Networks”, Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2016.

[12] Mostafa H. Chehreghani, Morteza Haghir Chehreghani , “Transactional Tree Mining” , European Conference on Machine Learning and Principles and Practice of Knowledge Discovery ( ECML / PKDD ) , (1) 182 Morteza Haghir Chehreghani Associate professor, Data Science and AI division, Department of Computer Science and Engineering.

Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited

We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets; Mostafa Haghir Chehreghani and Morteza Haghir Chehreghani. Efficient Morteza Haghir Chehreghani, Alberto Giovanni Busetto and Joachim M. Buhmann. 30.

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Vol. 109 (11), p. 2063-2097 ‪Chalmers University of Technology‬ - ‪Cited by 575‬ - ‪Artificial Intelligence‬ - ‪Machine Learning‬ - ‪Data Science‬ Morteza Haghir Chehreghani bor i en bostadsrätt på Doktor Hjorts gata 1 D lgh 1303 i postorten Göteborg i Göteborgs kommun. Området där han bor tillhör Göteborgs Annedals församling. På adressen finns en person folkbokförd, Morteza Haghir Chehreghani (39 år). Read more about Frank-Wolfe Optimization for Dominant Set Clustering (ZOOM: https://chalmers.zoom.us/j/62497622669); Active Learning for Artificial Neural Networks Morteza Haghir Chehreghani. Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) Read more about Project with industry: Discovering novel chemical reactions through applying machine learning on knowledge graphs (*) poster Poster session as author at New Frontiers in Model Order Selection, together with: Alexandre Lacoste, Nicolas Baskiotis, Stefan Kremer, Aurélie Boisbunon, Yuri Grinberg, Amir-massoud Farahmand, Marina Sapir, Mohammad Ghavamzadeh, Yevgeny Seldin, 4355 views Morteza Haghir Chehreghani is this you? claim profile ∙ 0 followers Chalmers University of Technology Research Scientist at Xerox Research Center Europe ( NAVER LABS Europe), PhD Student at ETH Zurich from 2008-2013.

NAVER LABS Europe. (formerly known as Xerox Research Centre Europe - XRCE) morteza.chehreghani@naverlabs.com.
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papers with code. 18. papers. 2. results.

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12/21/2018 ∙ by Morteza Haghir Chehreghani, et al. ∙ 0 ∙ share read it Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities

results. Research Areas. Autonomous Learning representations from dendrograms Downloaded from: https://research.chalmers.se, 2021-04-01 14:52 UTC Citation for the original published paper (version of record): Morteza Haghir Chehreghani, Faculty of Computer Engineering, Web Intelligence Laboratory, Department of Computer Engineering, Sharif University of Technology, Azadi Filter by Year. OR AND NOT 1. 2007 12/21/2018 ∙ by Morteza Haghir Chehreghani, et al. ∙ 0 ∙ share read it Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities Alberto Giovanni Busetto 1;2, Morteza Haghir Chehreghani , Joachim M. Buhmann 1 Department of Computer Science, ETH Zurich, Zurich, Switzerland 2 Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland ABSTRACT Models can be seen as mathematical tools aimed at pre-diction. The fundamental modeling question is: which Niklas Akerblom˚ 1;3, Yuxin Chen2 and Morteza Haghir Chehreghani3 1Volvo Car Corporation 2The University of Chicago 3Chalmers University of Technology niklas.akerblom@chalmers.se, chenyuxin@uchicago.edu, morteza.chehreghani@chalmers.se Abstract Energy-efﬁcient navigation constitutes an impor-tant challenge in electric vehicles, due to their lim- Morteza Haghir Chehreghani (academic supervisor) morteza.chehreghani@chalmers.se; Sadegh Rahrovani, (industrial supervisor) sadegh.rahrovani@volvocars.com; Martin Magnusson (Group manager at VolvoCars), martin.m.magnusson@volvocars.com (*) The project is taken and will be conducted by students during spring 2021 CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider sorting data in noisy conditions.

Morteza Haghir Chehreghani Author page based on publicly available paper data. 1. papers with code. 18. papers. 2. results. Research Areas. Autonomous

1. papers with code. 18. papers. 2. results.

∙ 0 ∙ share read it Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities Filter by Year. OR AND NOT 1. 2007 An Online Learning Framework for Energy-Efﬁcient Navigation of Electric Vehicles Niklas Akerblom˚ 1;3, Yuxin Chen2 and Morteza Haghir Chehreghani3 1Volvo Car Corporation 2The University of Chicago 3Chalmers University of Technology niklas.akerblom@chalmers.se, chenyuxin@uchicago.edu, morteza.chehreghani@chalmers.se CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. An important source of high clustering coefficient in real-world net-works is transitivity. However, existing approaches for modeling transitivity suf-fer from at least one of the following problems: i) they produce graphs from a specific class like bipartite graphs, ii) they do not give an analytical Morteza Haghir Chehreghani (academic supervisor) morteza.chehreghani@chalmers.se; Sadegh Rahrovani, (industrial supervisor) sadegh.rahrovani@volvocars.com; Martin Magnusson (Group manager at VolvoCars), martin.m.magnusson@volvocars.com (*) The project is taken and will be conducted by students during spring 2021 2020-07-28 We propose unsupervised representation learning and feature extraction from dendrograms.