LOD 2019: The Fifth International Conference on Machine Learning, Optimization, and Data Science Certosa di Pontignano Siena, Italy, September 10-13, 2019 |
Conference website | https://lod2019.icas.xyz |
Submission link | https://easychair.org/conferences/?conf=lod2019 |
Poster | download |
Abstract registration deadline | May 31, 2019 |
Submission deadline | May 31, 2019 |
Submission Guidelines
When submitting a paper to LOD 2019, 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- long paper: original novel and unpublished work (max. 12 pages in Springer LNCS format);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.
List of Topics
- 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.
Invited Speakers
-
Michael Bronstein, Imperial College London, UK
Topics: Deep Learning on Graphs and Manifolds -
Marco Gori, University of Siena, Italy
Topics: Constraint-Based Approaches to Machine Learning -
Arthur Gretton, UCL, UK
Topics: Kernel Methods to Reveal Properties and Relations in Data -
Arthur Guez, Google DeepMind, London, UK
Topics: General Reinforcement Learning Algorithms -
Kaisa Miettinen, University of Jyväskylä, Finland
Topics: Multiobjective Optimization & Decision Analytics -
Jan Peters, Technische Universitaet Darmstadt
Max-Planck Institute for Intelligent Systems, Germany
Topics: Intelligent Autonomous Systems, Robotics & Machine Learning -
Mauricio Resende, Amazon, USA
Topics: Combinatorial Optimization & Heuristics -
Richard E. Turner, University of Cambridge, UK
Topics: Gaussian Processes & Computer Perception
Publication
The conference will consist of four days of conference sessions. We invite submissions of papers on all topics related to Machine learning, Optimization, Knowledge Discovery and Data Science including real-world applications for the Conference Proceedings by Springer – Lecture Notes in Computer Science (LNCS).
LOD uses the formula of 30 minutes presentations for fruitful exchanges between authors and participants.
Venue
The venue of LOD 2019 will be The Certosa di Pontignano — Siena.
The Certosa di Pontigniano
- address: Loc. Pontignano, 5 – 53019, Castelnuovo Berardenga (Siena) – Tuscany – Italy
- phone: +39-0577-1521104
- fax: +39-0577-1521098
- email: info@lacertosadipontignano.com
- web: http://www.lacertosadipontignano.com/
A few kilometres from Siena, on a hill dominating the town stands the ancient Certosa di Pontignano, a unique place where nature, history and hospitality blend together in memorable harmony. Built in the 1300, its medieval structure remains intact with additions of the following centuries. The Certosa is centred on its historic cloisters and gardens.
Contact
All questions about submissions should be emailed to lod@icas.xyz
Sponsors
Springer - Nature.
NeoData Group.