LOD 2018: 4th International Conference on Machine Learning, Optimization and Data Science SIAF Learning Village Volterra, Pisa - Tuscany, Italy, September 13-16, 2018 |
Conference website | https://lod2018.icas.xyz |
Submission link | https://easychair.org/conferences/?conf=lod2018 |
Abstract registration deadline | May 31, 2018 |
Submission deadline | May 31, 2018 |
Submission Guidelines
LOD 2018 Call for Papers
All papers must be original and not simultaneously submitted to another journal or conference. When submitting a paper to LOD 2018, authors are required to select one of the following four types of papers:
- long paper: original novel and unpublished work (max. 12 pages in Springer LNCS format);
- short paper: an extended abstract of novel work (max. 4 pages);
- work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference;
- abstract for poster presentation only (max 2 pages; any format). The poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide, respectively 46.8 x 33.1 inch). For research work which is relevant and which may solicit fruitful discussion at the conference.
Following the tradition of LOD, we expect high-quality papers in terms of their scientific contribution, rigor, correctness, quality of presentation and reproducibility of experiments.
Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact.
https://lod2018.icas.xyz/call-for-papers/
Call for Special Sessions
Special session proposals are invited to the fourth International Conference on Machine learning, Optimization and Data Science (LOD) to be held in Volterra (Pisa) Tuscany, Italy on September 13-16, 2018.
A special session proposal should include the title, aim and scope of the proposed session, list of potential contributors, and the names, e-mail addresses, affiliations and short bios of the organizers.
Special session proposals will be evaluated based on the timeliness of the topic, its uniqueness, and qualifications of the proposers. The proposers are expected to have a PhD degree and have a good publication track record in the proposed area. A tentative accept/reject decision on the proposal will be sent to the proposers within a few weeks after its receipt by the Special Sessions Chair. Accepted special sessions will be listed on the website. However, it is likely that an accepted proposal will be combined with similar proposals to avoid multiple special sessions covering a similar topic. A final decision will be made two weeks after the special session proposal deadline (February 28, 2018).
Submissions of papers to special sessions should be done through the paper submission website of LOD 2018 where authors can choose a special session title as the main topic of their paper from a list of regular session topics and special session titles. All papers submitted to special sessions will be subject to the same peer-review review procedure as the regular papers. Special sessions having fewer than accepted papers will be cancelled and the accepted papers will be moved to regular sessions.
https://lod2018.icas.xyz/call-for-special-sessions-tutorials/
List of Topics
The last five-year period has seen a impressive revolution in the theory and application of machine learning, optimization and big data. Topics of interest include, but are not limited to:
- Foundations, algorithms, models and theory of data science, including big data mining.
- Machine learning and statistical methods for big data.
- Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks.
- Unsupervised, semi-supervised, and supervised Learning.
- Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning.
- Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states.
- Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization.
- Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
- Big Data mining systems and platforms, and their efficiency, scalability, security and privacy.
- Computational optimization. Optimization for representation learning. Optimization under Uncertainty
- Optimization algorithms for Real World Applications. Optimization for Big Data. Optimization and Machine Learning.
- Implementation issues, parallelization, software platforms, hardware
- Big Data mining for modeling, visualization, personalization, and recommendation.
- Big Data mining for cyber-physical systems and complex, time-evolving networks.
- Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains.
We particularly encourage submissions in emerging topics of high importance such as data quality, advanced deep learning, time-evolving networks, large multi-objective optimization, quantum discrete optimization, learning representations, big data mining and analytics, cyber-physical systems, heterogeneous data integration and mining, autonomous decision and adaptive control.
Committees
Program Committee
Organizing committee
General Chairs
- Renato Umeton, MIT, USA
- Vincenzo Sciacca, IBM, Italy
Program Chairs
- Panos Pardalos, University of Florida, USA
- Giovanni Giuffrida, University of Catania, Italy & Neodata Group
Special Sessions Chair
- Aris Anagnostopoulos, Università di Roma “La Sapienza”, Italy
Tutorial/Special Sessions Chair
- Giuseppe Narzisi, New York University Tandon School of Engineering, USA, and New York Genome Center, USA
Publicity Chair
- Stefano Mauceri, NCRA, University College Dublin, Ireland
Industrial Session Chairs
- Ilaria Bordino, UniCredit R&D, Italy
- Marco Firrincieli, UniCredit R&D, Italy
- Fabio Fumarola, UniCredit R&D, Italy
- Francesco Gullo, UniCredit R&D, Italy
Organizing Committee
- Alberto Castellini, University of Verona, Italy
- Piero Conca, CNR, Italy
- Jole Costanza, Italian Institute of Technology, Milano, Italy
- Giuditta Franco, University of Verona, Italy
- Giorgio Jansen, University of Catania, Italy
- Giuseppe Narzisi, New York University Tandon School of Engineering, USA, and New York Genome Center, USA
- Andrea Patanè, University of Oxford, UK
- Andrea Santoro, Queen Mary University London, UK
Keynote Speakers
◦ Jörg Bornschein
DeepMind, London, UK
◦ Peter Flach
University of Bristol, UK - Editor-in-Chief of the Machine Learning Journal
◦ George Karypis
University of Minnesota, USA
◦ Martin Ravetti
Universidade Federal de Minas Gerais, Brasil
◦ Andrey Raygorodsky
Moscow Institute of Physics and Technology, Russia
◦ Stephen Smale - Fields Medal
University of California Berkeley, USA
More Speakers TBA!
Publication
LOD 2018 Proceedings
All accepted long papers will be published in a volume of the series on Lecture Notes in Computer Science (LNCS) from Springer after the conference. Instructions for preparing and submitting the final versions (camera-ready papers) of all accepted papers will be available later on.
Venue
The LOD 2018 conference will be held in Volterra (Pisa) – Tuscany at the Learning Village SIAF.
SP del monte Volterrano (località “Il Cipresso”) – 56048 Volterra (Pisa) – Tuscany – Italy
p: +39-0588-81266
f: +39-0588-86414
w: http://www.siafvolterra.it/wp/en/
Contact
All questions about submissions should be emailed to lod@icas.xyz