posted by system || 9764 views || tracked by 22 users: [display]

LOD 2019 : 5th International Conference on machine Learning, Optimization & Data science


When Sep 10, 2019 - Sep 13, 2019
Where Siena, Tuscany, Italy
Submission Deadline Apr 30, 2019

Call For Papers

The 5th International Conference on machine Learning, Optimization & Data science - LOD
An Interdisciplinary Conference: Deep Learning, Optimization and Big Data without Borders

Certosa di Pontignano (Siena) Tuscany, September 10-13, 2019

The International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier interdisciplinary conference in machine learning, computational optimization, knowledge discovery and data science. It provides an international forum for presentation of original multidisciplinary research results, as well as exchange and dissemination of innovative and practical development experiences.
LOD 2019 will be held in Certosa di Pontignano (Siena) – Tuscany, Italy, from September 10 to 13, 2019.

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.

Submission deadline: April 30
Any questions regarding the submission process can be sent to conference organizers:

* Michael Bronstein, Imperial College London, UK
Talk: "Deep learning on graphs and manifolds: going beyond Euclidean data"

* Marco Gori, University of Siena, Italy
Talk: ""
Topics: Constraint-Based Approaches to Machine Learning

* Arthur Gretton, UCL, UK
Talk: "A Kernel Critic for Generative Adversarial Networks"

* Arthur Guez Google DeepMind, London, UK
Topics: General Reinforcement Learning Algorithms

* Kaisa Miettinen, University of Jyväskylä, Finland
Talk: "Interactive Multiobjective Optimization in Decision Analytics with a Case Study"

* Jan Peters, Technische Universitaet Darmstadt
Talk: "Machine Learning of Robot Skills"

* Mauricio Resende, Amazon, USA
Talk: "Biased random-key genetic algorithms – Learning intelligent solutions from random building blocks"

* Richard E. Turner, University of Cambridge, UK
Talk: "Extending the frontiers of deep learning using probabilistic modelling"

LOD 2019 Best Paper Award
Springer sponsors the LOD 2019 Best Paper Award with a cash prize of EUR 1,000.
The Award will be conferred at the conference on the authors of the best paper award.

Topics of Interest
The last five-year period has seen an impressive revolution in the theory and application of machine learning, optimization and big data.

Topics of interest include, but are not limited to:
* Deep Learning
* Reinforcement Learning
* Deep NeuroEvolution
* Multi-Objective Optimization
* 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.

Call for Papers:
Submission deadline: April 30

LOD 2019 Big-Data Challenge
Our sponsor, Neodata Lab, will offer a prize of €2000 to the applicant who develops the most accurate algorithm to process an “approximate SQL-like query answering system” on a real dataset.

In order to participate to this contest (or if you have any inquire about the challenge) please send an email to the following address:

specifying your full name and affiliation. We will contact you with directions on how to download sample data.

See you in Siena!
LOD 2019 Organizing Committee.

Related Resources

ICDM 2023   International Conference on Data Mining
SI-MLT 2023   Special Issue on MACHINE LEARNING IN TOURISM - Int. J. of Machine Learning and Cybernetics (Springer)
Complex Networks 2023   12 th International Conference on Complex Networks and their Applications
BigData 2023 - 6th HealthCare Data 2023   IEEE BigData 2023 - 6th Special Session on HealthCare Data
TNNLS-GL 2023   IEEE Transactions on Neural Networks and Learning Systems Special Issue on Graph Learning
OMLMIA 2023   Optimization and Machine Learning in Medical Image Analysis
ICSC 2024   IEEE International Conference on Semantic Computing
VSI: AMLDFIS 2023   Special Issue of Information Fusion (Elsevier): New Trends of Adversarial Machine Learning for Data Fusion and Intelligent System
BigMM 2023   IEEE International Conference on Multimedia Big Data