CAp is an interdisciplinary gathering of researchers at the intersection of machine learning, applied mathematics, and related areas.
The deadline for paper submission is April the 14, 2017 at 23:59, for the abstracts and April the 21, 2017 at 23:59 for the papers with final decisions made on May the 26, 2017.
Please use the
EasyChair website for all submissions.
We encourage the submission of recent papers accepted to high level conferences and journals in Machine Learning. Submitted papers can be either in English or in French and we encourage two types of submissions:
- Full research papers on the theme of machine learning theory and its applications should not exceed ten pages in CAp double-column format (including references and figures). Suitable LaTeX template for CAp is available here.
- Short papers can be up to four to six pages using the same format as Full papers. They present original ideas and provide an opportunity to describe significant work in progress.
Authors of accepted papers will be invited for oral presentation of their work and for a posters session. This session is an opportunity to have constructing and rigorous feedbacks, as well as to establish contacts with members of the french machine learning communinity. PhD Students are particularly welcome and encouraged to submit papers.
Contributions will be freely distributed on the conference website, subject to approval by the authors.
The conference and programm chairs of CAp 2017 invite those working in areas related to any aspect of machine learning to submit original papers for review.
Solicited topics include, but are not limited to:
- Learning theory, models and paradigms
- Active learning
- Online learning
- Multi-target, multi-task, multi-instance, multi-view and transfer learning
- Supervised, unsupervised and semi-supervised learning
- Reinforcement learning
- Relational learning
- Representation learning
- Symbolic learning
- Bandit algorithms
- Matrix and tensor factorization
- Grammar induction
- Kernel methods
- Bayesian methods
- Spectral methods
- Stochastic processes
- Ensemble learning and boosting
- Graphical models
- Gaussian process
- Neural networks and deep learning
- Learning theory
- Game theory
- Optimization et related problems
- Large-scale machine learning and optimization
- Optimization algorithms
- Distributed optimization
- Machine learning and structured data (spatio-temporal data, tree, graph)
- Classification with missing values
- Applications
- Social network analysis
- Temporal data analysis
- Bioinformatic
- Data mining
- Neuroscience
- Natural language processing
- Information retrieval
- Computer vision
