Since 2015, the LOD Conference brings academics, researchers and industrial researchers together in a unique multidisciplinary community to discuss the state of the art and the latest advances in the integration of machine learning, optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. Since 2017, LOD adopted the Asilomar AI Principles.
The 7th International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier multidisciplinary conference in machine learning, computational optimization, knowledge discovery and data science. It provides an international forum for the presentation of original multidisciplinary research results, as well as exchange and dissemination of innovative and practical development experiences.
LOD 2021 will be held in the Wordsworth Hotel & SPA in Grasmere, Lake District, England on October 4 – 8, 2021. 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 Post-Proceedings by Springer Nature – Lecture Notes in Computer Science (LNCS).
All papers must be submitted using EasyChair.
Paper Submission deadline: Tuesday June 15, 2021 (AoE)
Any questions regarding the submission process can be sent to conference organizers: lod@icas.cc.
Please prepare your paper in English using the Springer Nature – 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 2021, authors are required to select one of the following four types of papers:
- Long Paper: original novel and unpublished work (max. 15 pages, including References, in Springer LNCS format);
- Short Paper: an extended abstract of novel work (max. 5 pages, including References, in Springer LNCS format);
- 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.
Each paper submitted will be rigorously evaluated. The evaluation will ensure the high interest and expertise of reviewers. Following the tradition of LOD, we expect high-quality papers in terms of their scientific contribution, rigor, correctness, novelty, clarity, 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.
All accepted long papers will be published in a volume of the series on Lecture Notes in Computer Science (LNCS) from Springer – Nature after the conference (Conference Post-Proceedings). 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 2021 web site.
We can now include ORCID for authors of the proceedings. See this announcement and the guidelines (editor guidelines, section 4.7) for more information. For more information see this link.
Past proceedings:
- LOD 2020 Proceedings: Springer LNCS, volumes 12565 and 12566, 6th International conference on machine Learning, Optimization & big Data – LOD, July 19-23, 2020, Revised Selected Papers.
- LOD 2019 Proceedings: Springer LNCS, volume 11943, 5th International conference on machine Learning, Optimization & big Data – LOD, September 10-13, 2019, Revised Selected Papers.
- LOD 2018 Proceedings: Springer LNCS, volume 11331, 4th International Conference on machine Learning, Optimization & big Data, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers.
- MOD 2017 Proceedings: Springer LNCS, volume 10710: 3rd International Conference on Machine Learning, Optimization & Big Data, MOD 2017, Volterra, Italy, September 14-17, 2017, Revised Selected Papers.
- MOD 2016 Proceedings, Springer LNCS, volume 10122: 2nd International Conference on Machine Learning, Optimization & Big Data, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers.
- MOD 2015 Proceedings, Springer, LNCS volume 9432: 1st International Conference on Machine Learning, Optimization & Big Data, 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