Machine Learning - CfP: IJCLR Learning and reasoning
Description:
The journal track of the International Joint Conference on Learning and Reasoning (IJCLR), published as Special Issue on Learning and Reasoning in the Machine Learning journal (MLJ), has been accepting paper submissions on regular cut-off dates since February 2020.
Journal track papers are published online by the MLJ upon acceptance and authors of accepted papers are invited to present their work at IJCLR.
Submissions are solicited on all aspects of Learning and Reasoning and topics where machine learning is combined with machine reasoning or knowledge representation.
Authors are invited to submit novel, high-quality work that has neither appeared in, nor is under consideration for publication by other journals or conferences.
Topics of interest for the Journal Track include, but are not limited to:
- Theory & foundations of logical & relational learning.
- Learning in various logical representations and formalisms, such as logic programming & answer set programming, first-order & higher-order logic, description logic & ontologies.
- Statistical Relational AI, including structure/parameter learning for probabilistic logic languages, relational probabilistic graphical models, kernel-based methods, neural-symbolic learning.
- Systems and techniques that integrate neural, statistical & symbolic learning.
- Systems and techniques addressing aspects of integrating learning, reasoning & optimization.
- Knowledge representation and reasoning in deep neural networks.
- Symbolic knowledge extraction from neural and statistical learning models.
- Neural-symbolic cognitive models.
- Techniques that foster explainability & trustworthiness of AI models, including combinations of machine learning with constraints & satisfiability, explainable AI frameworks and reasoning about the behaviour of machine learning models.
- Inductive methods for program synthesis.
- Example-driven programming.
- Combining logic and functional program induction.
- Meta-interpretative learning & predicate invention.
- Scaling-up logical & relational learning: parallel & distributed learning techniques, online learning and learning structured representations from data streams.
- Human-Like Computing, including Cognitive and AI aspects of perception, action and learning
Editorial schedule:
See https://ijclr2023.di.uniba.it/~ijclr2023/dates/index.html (this opens in a new tab) for an overview of cut-off dates