LOD 2018 : The 4th Annual Conference on machine Learning, Optimization and Data science
Call For Papers
The 4th Annual Conference on machine Learning, Optimization and Data science (LOD) is a single-track machine learning, computational optimization, data science conference that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.
The International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier interdisciplinary conference in machine learning, computational optimization 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.
We invite submissions of papers, abstracts, posters and demos on all topics related to Machine learning, Optimization and Data Science including real-world applications for the Conference proceedings – Springer Lecture Notes in Computer Science.
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.
Topics of Interest
The last five-year period has seen a impressive revolution in the theory and application of machine learning 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.