CAp 2017 - 19th annual meeting of the francophone Machine Learning community

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, with final decisions made on May the 26, 2017. Please use the EasyChair website for all submissions.
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 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