Call for 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, 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).

Papers submission

All papers must be submitted using EasyChair

Submission deadline: June 4, 2019

Any questions regarding the submission process can be sent to conference organizers: lod@icas.xyz

Paper format

Please prepare your paper in English using the Springer – Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.

Types of Submissions

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Long Paper: original novel and unpublished work (max. 12 pages in Springer LNCS format);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 in Springer LNCS format);
Abstract for Oral Presentation Only (max 2 pages; 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.

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.

All the other papers (short papers, abstracts of the oral presentations, abstracts for poster presentations) will be published on the LOD 2019 web site.

NEW IN 2017-2018: We can now include ORCID for authors of the proceedings. See this announcement https://goo.gl/nAX4y8 and the guidelines https://goo.gl/oiMNAm (editor guidelines, section 4.7) for more information. For more information see: https://goo.gl/hbsa4D.

Past proceedings:

LOD 2018 Proceedings: Springer LNCS, volume 11331, 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers.
MOD 2017 Proceedings: Springer LNCS, volume 10710: Machine Learning, Optimization & Big Data Second International Workshop, MOD 2017, Volterra, Italy, September 14-17, 2017, Revised Selected Papers; editors: Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton.
MOD 2016 Proceedings, Springer LNCS, volume 10122: Machine Learning, Optimization & Big Data Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers; editors: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G.;
MOD 2015 Proceedings, Springer, LNCS volume 9432: Machine Learning, Optimization & Big Data First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015.

Topics

Active Learning
Analogical learning methods
Applications
Approximate Inference
Audio and Speech Processing
Auditory Perception and Modelling
Automated knowledge acquisition
Bandit Algorithms
Bayesian Non-parametrics
Bayesian Theory
Belief Propagation
Bioinformatics
Biologically inspired machine learning algorithms
Brain Imaging
Brain-computer Interfaces and Neural Prostheses
Case-based methods
Causality
Classification, regression, recognition, and prediction
Clustering
Cognitive Science
Collaborative Filtering and Recommender Systems
Component Analysis (ICA, PCA, CCA, FLDA)
Compressed Sensing and Sparse Reconstruction
Computational Neural Models
Computer Vision
Connectionist networks
Control Theory
Data mining
Deep Learning
Density Estimation
Design and diagnosis
Ensemble Methods and Boosting
Evolution-based machine learning methods
Exact Inference
Explanation-based learning
Feature Learning
Frequentist Statistics
Game playing
Game Theory and Computational Economics
Gaussian Processes
Graph Based Learning
Graphical Models
Hardware for Machine Learning
Image Segmentation
Inductive logic programming
Industrial, financial, and scientific applications of all kinds
Information Retrieval
Information Theory
Kernel Methods
Knowledge Representation and Acquisition
Language (Cognitive Science)
Large Margin Methods
Large Scale Learning and Big Data
Learning decision and regression trees and rules
Learning from instruction;
Learning in integrated architectures
Learning Theory
MCMC
Matrix Factorization
Missing Data
Model Selection and Structure Learning
Motor Control
Multi-Agent Systems
Multi-strategy learning
Multi-task and Transfer Learning
Music Modelling and Analysis
Natural Language Processing
Natural Scene Statistics
Neural Coding
Neural Networks
Neuroscience
Nonlinear Dimension Reduction and Manifold Learning
Object Recognition
Online Learning
Other Supervised Learning Methods
Other Unsupervised Learning Methods
Privacy, Anonymity, and Security
Probabilistic Models and Methods
Probabilistic networks and other statistical models
Problem solving and planning
Quantitative Finance and Econometrics
Ranking and Preference Learning
Reasoning and inference
Regression
Reinforcement Learning Algorithms
Reinforcement Learning (Cognitive/Neuroscience)
Relational Models
Representation Learning
Robotics
Robotics and control
Scientific discovery
Semi-Supervised Learning
Signal Processing
Similarity and Distance Learning
Social Networks
Sparse Coding
Sparsity and Feature Selection
Spectral Methods
Speech and Signal Processing
Speech Recognition
Statistical Learning Theory
Stochastic Methods
Structured Prediction
Supervised Learning
Support Vector Machines
Systems Biology
Text Mining
Theoretical Neuroscience
Time Series Analysis
Topic Models
Unsupervised learning methods
Variational Inference
Video, Motion and Tracking
Vision and speech perception
Visual Features
Visual Perception
Visualization of patterns in data
Web Applications, Web mining and Internet Data

Biological inspired Optimization
Combinatorial optimization
Convex Optimization
Derivative-based Optimization
Derivative-free Optimization
Deterministic Global Optimization
Discrete-Continuous Nonlinear Optimization
Evolutionary Optimization
Geometric Programming
Global Optimization
Integer Programming
Financial Optimization
Large Scale Optimization
Local versus Global Optimization
Metaheuristics and Benchmarking
Mixed-integer Nonlinear Optimization
Multiobjective Optimization
Nonlinear Programming
Nonlinear Optimization
NP Complete Problems
Optimal Control
Other Optimization Methods
Polynomial Optimization
Quantum Optimization
Randomized Optimization
Routing
Scheduling
Smoothed Analysis
Stochastic Optimization

Advanced database and Web Applications
Algorithms and Programming Techniques for Big Data Processing
Algorithms and Systems for Big Data Search
Anomaly and APT Detection in Very Large Scale Systems
Anomaly Detection in Very Large Scale Systems
Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
Big Data Analytics and Metrics
Big Data Analytics in Government, Public Sector and Society in General
Big Data Analytics in Small Business Enterprises
Big Data Applications
Big Data Architectures
Big Data in Business Performance Management
Big Data Models and Algorithms
Big Data as a Service
Big Data Open Platforms
Big Data in Mobile and Pervasive Computing
Big Data Foundations
Big Data Industry Standards
Big Data Infrastructure
Big Data Management
Big Data Persistence and Preservation
Big Data Quality and Provenance Control
Big Data in Enterprise Management Models and Practices
Big Data in Government Management Models and Practices
Big Data in Smart Planet Solutions
Big Data for Enterprise Transformation
Big Data Protection, Integrity and Privacy
Big Data Encryption
Big Data Search and Mining
Big Data Security & Privacy
Big Data for Enterprise, Government and Society
Big Data Economics
Big Data for Business Model Innovation
Big Data for Vertical Industries (including Government, Healthcare, and Environment)
Big Data Search Architectures, Scalability and Efficiency
Big Data Toolkits
Cloud Computing Techniques for Big Data
Cloud/Grid/Stream Computing for Big Data
Cloud/Grid/Stream Data Mining- Big Velocity Data
Collaborative Threat Detection using Big Data Analytics
Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
Computational Modeling and Data Integration
Crowdsourcing
Data Acquisition, Integration, Cleaning, and Best Practices
Data and Information Quality for Big Data
Database Management Challenges: Architecture, Storage, User Interfaces
Data Management for Mobile and Pervasive Computing
Data Management in the Social Web
Data Preservation
Data Protection, Integrity and Privacy Standards and Policies
Data Provenance
Distributed, and Peer-to-peer Search
Energy-efficient Computing for Big Data
Experiences with Big Data Project Deployments
Foundational Models for Big Data
HCI Challenges for Big Data Security & Privacy
Heterogeneous and Multi-structured Data Integration
High Performance Cryptography
High Performance/Parallel Computing Platforms for Big Data
Interfaces to Database Systems and Analytics Software Systems Information Integration
Intrusion Detection for Gigabit Networks
Large-scale Recommendation Systems and Social Media Systems
Large-scale Social Media and Recommendation Systems
Link and Graph Mining
Machine learning based on Big Data
Management Issues of Social Network Big Data
Mobility and Big Data
Models and Languages for Big Data Protection
Multimedia and Multi-structured Data- Big Variety Data
New Computational Models for Big Data
New Data Standards
New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
Novel Data Model and Databases for Emerging Hardware
Novel Theoretical Models for Big Data
Privacy Preserving Big Data Analytics
Privacy Threats of Big Data
Programming Models & Environments for Cluster, Cloud, & Grid Computing to Support Big Data
Real-life Case Studies of Value Creation through Big Data Analytics
Representation Formats for Multimedia Big Data
Scientific Applications of Big Data
Security Applications of Big Data
Semantic-based Data Mining and Data Pre-processing
SME-centric Big Data Analytics
Sociological Aspects of Big Data Privacy
Social Web Search and Mining
Software Systems to Support Big Data Computing
Software Techniques and Architectures in Cloud/Grid/Stream Computing
Spatiotemporal and Stream Data Management
Threat Detection using Big Data Analytics
Visualization Analytics for Big Data
Visualizing Large Scale Security Data
Web Search Distributed and Peer-to-peer Search